Year: 2018

#RPA in Healthcare: The Path Ahead for Health IT Leaders By Sreejith Madhavan


Historically, healthcare industry has shown a reluctance to invest in technologies that did not come under the purview of diagnostics and treatment, or demanded by insurance payors (such as electronic claims submission). Anything that required cognitive (human) intervention or intuition was kept aside from the technological takeover. The unprecedented growth of life expectancy, the discovery of new drugs and treatments, and the ability of modern medicine to combat chronic ailments and epidemics have spurred the need for technological inclusion in multiple areas of healthcare.

As patients become more digital savvy, caregivers are increasingly implementing technology solutions that enable both parties to perform several activities online such as accessing personal medical information to online scheduling of appointments. Today, healthcare industry is looking at those technologies or combinations of technologies that can optimize their front, middle and back-office operations so that care givers get adequate time to spend on priority tasks.

Robotic Process Automation (RPA) is one of the key technologies that has gone mainstream in many industries including healthcare. Why health IT leaders should continue to turn their pivot towards RPA? We’re exploring the reasons through this post.

RPA in Healthcare: Common Applications and Benefits

Robotic Process Automation or RPA automates processes that are repetitive and transactional, primarily by imitating human behavior for rule-based tasks.  RPA enables caregivers to focus on high-value activities by enhancing overall administration of healthcare processesIt executes routine tasks at a fraction of time than that’s taken by a human, eliminating the risk of human errors. The scope of RPA in the administrative and clinical functions of healthcare is very vast. 

Technologies such as cloud computing and data virtualization have enabled scalable deployment of RPA software across various units and geographic locations of a healthcare organization. So far, healthcare administrators have leveraged RPA in several areas of their back, middle and front-office operations; few of which are mentioned in the table below:  

Healthcare
Areas of RPA implementation
Benefits to healthcare providers
Back Office

  • Human resource management
  • Finance and supply chain management
  • Streamline onboarding process to improve efficiency
  • Clinicians can impart care without interruption caused by administrative functions
  • Human resource management
  • Ensure new clinical staff gains access to systems and facilities from day 1
Middle Office

  • Revenue cycle management
  • Claim submission and reconciliation
  • Patient scheduling
  • Accelerate revenue cycle by automating coverage eligibility verification process, claims posting, and claim resubmission
  • Insurance data management
Front Office

(relatively untapped by RPA)

  • Care delivery setting
  • Health data utilization and report generation
  • Integration of disparate care management systems to assimilate date efficiently
  • Ensure clinicians spend more time for patient care by minimizing their administrative work
  • Enhance case management


Most of the present day healthcare organizations are using RPA for automating rules-driven and repetitive back office work. The potential RPA can offer healthcare in unison with advanced technologies such as machine learning (ML) and artificial intelligence (AI) is tremendous. It’s no surprise if we consider Robotic Process Automation a stepping stone to integrating these sophisticated cognitive technologies into healthcare.

What needs to be automated in healthcare?

Here’re a few potential use cases: 

1 Connecting and automating disparate health monitoring devices: The case of neonatal ICU:

A 2017 Business Insider post talks about the need to automate oxygen supply to patients hospitalized with pulmonary hypertension. Currently, the system only alerts the staff (nurse) through a monitor beep when the blood oxygen level of the patient drops and the staff has to attend the case. If the nurse is attending other patients and misses out the alert, the chance for a mishap is more. The article from Thomas Hooven, a Neonatologist in the U.S. suggests how automation of oxygen inflow at the moment of crisis could save patients with chronic pulmonary hypertension.

2 Compliance monitoring and analysis:

Imagine a hospital that processes thousands of claims daily and attends the need of a large number of insurance beneficiaries. RPA can be used to gather and consolidate data from multiple disparate sources or systems that improves the efficiency of regulatory, non-financial, and risk reporting. Automation of compliance monitoring analytics eliminates time-consuming activities involved in the collection, compilation, cleansing and summarization of large amounts of information. Security of medical data and records is a major concern for any healthcare organization. Robotic Process Automation helps protect patient privacy and achieve compliance with HIPAA and other mandatory health regulations by generating custom reports and detailed audit logs.

3 IoT analytics to empower process automation

The goal of any IoT deployment should not be limited to collecting data from multiple sources (devices). It must ensure that the data is actionable in real-time, to support relevant processes. Process automation is recognized as the common endeavor to improve operational efficiency by lowering costs, increasing profits and improving customer satisfaction. Integrating IoT into process automation could deliver greater value across product lines. For instance, consider the claims settlement process in healthcare that is deeply influenced by the data being collected from several devices. During the claims settlement process, if the system could take into account the details of the data aggregated by IoT devices such as lowering a premium based on usage behavior, or a difference in user-provided information, that could lead to process optimization and faster decision-making. IoT analytics in healthcare can avoid the cost of admissions by automating prescriptions, reduce medical error in treatment and improve quality of patient services.

Leveraging RPA with exponential technologies

RPA is just one of the growing technologies that can empower healthcare organizations. Once RPA is integrated successfully into their core business strategies, hospitals should consider incorporating the advanced spectrum of cognitive technologies such as AI and machine learning. Unlike RPA, artificial intelligence has the ability to identify patterns in data. Similarly, machine learning adds more meaning and power to process automation by enabling healthcare organizations to identify payment variance and remediate complex payment methodologies.

The future healthcare environment could look very different from what we see today. Technologies like Robotic Process Automation will have a greater say on employee productivity. Automating routine tasks such as collecting blood samples could help the job of a nurse, reduce task time and eliminate manual errors, while improving the patient experience. As organizations progress from depending on manual tasks to applying RPA and cognitive computing, the workforce also shifts from being “doers” to “reviewers.” Health IT leaders and providers, hence should focus on developing proactive, winning strategies to attain long-term financial sustainability and improved patient experience.

Author
Sreejith Madhavan

Sreejith Madhavan is the Chief Operating Officer of Zerone Consulting Pvt. Ltd., a custom software development company with an exceptional track record of successfully completing over 500 challenging projects for 140 plus satisfied customers globally. Sreejith’s experience includes a demonstrated history of working in the outsourcing/offshoring industry, managing and mentoring multiple teams in the web and mobile development arena

#mHealth: A New Growth Engine in MedTech Industry by Ananya Bhandari

Source: Grand View Research

Mobile health highlights the risks and opportunities of pharmaceutical and Medtech industries. Surge in number of purely digital players transformed the mHealth app market





According to the estimates of Grand View Research, the global mHealth market size was valued at USD 4.75 billion in 2014 and is expected to witness substantial gains throughout the forecast period. Improvement in 3G & 4G networks and favorable government initiatives in healthcare IT owing to increase in demand for such services. According to WHO Global Observatory for eHealth, about 58% of the health authorities around the globe is involved in the development and adoption of mHealth in health sector. High penetration of smartphones coupled with technological advancement in smartphone applications is also anticipated to further boost up the demand for mHealth services.

Furthermore, the growing adoption of mobile applications paved the way for driving up the demand for various mHealth apps that can be used for fitness tracking, diagnostic & monitoring, consultation, medical information & education services, chronic care management, and ageing solutions. Growing demand for mHealth technology to provide remote patient monitoring services and to run surveillance programs in developing countries, which is further anticipated to propel the market growth. However, lack of reimbursement policies, poor network coverage especially in emerging economies, and data security issues can hamper the growth of mHealth market.

U.S. mHealth Market Revenue by Services, 2014 – 2025 (USD Million)
Source: Grand View Research

mHealth: Market Segmentation

Based on the services, the market is segmented as monitoring services, diagnosis services, healthcare system strengthening, and other services. Monitoring services accounted for major share of over 65% in 2016 pertaining to the factors such as increasing ageing population and rising incidence of chronic diseases such as obesity & diabetes, congestive heart failure, cancer. mHealth helps in monitoring of various health parameters such as cholesterol levels, blood pressure, heart rate, and nutrient intake.

There is continuous advancements in diagnostic device technology that integrates digital technology in medical devices. It enables patients to send clinical data to the healthcare providers through their smartphones. Healthcare system strengthening services provide healthcare surveillance and administration, emergency response and support to healthcare providers. Other services consist of prevention and wellness of patients through mHealth applications. It focused on elderly care, drug abuse prevention, child and women care, smoking de-addiction and healthy living.

Mobile operators held the largest market share of about 49% in 2016. MNO’s provide 3G and 4G broadband network coverage. mHealth allows healthcare professionals to handle appointments and to monitor remote patients. Device vendors are involved in integrating digital technology in medical devices that allows transmission of clinical data such as blood pressure, heart rate, blood-glucose levels and others. Leading vendors such as Biotrick, Medtronic, ResMed and Philips Respironics are successfully using this technology.

Some of the key factors attributing to the market growth include rising number of health and wellness apps with growing adoption of smartphones by population. Asia pacific region is expected to be the fastest growing segment with a CAGR of 27.2% over the forecast period. 

Source: Grand View Research


The growth in the region is driven by the factors such increased healthcare awareness, improving network infrastructure, rising rural population and government initiatives towards digitalization in countries like India and China.


In addition, improving internet connectivity, reduction in costs and increasing use of mHealth for various surveillance and awareness programs for rural areas drive the growth of mHealth in the region. Growing number of surveillance programs for AIDS & other infectious disease and rising incidence of chronic diseases in Latin America and Middle East Asia drives the demand for mHealth applications.

Author
Ananya Bhandari

Ananya is a MBA in Marketing with a professional experience of nearly three years in healthcare domain. She is a Research Analyst, medical devices and healthcare IT, at Grand View Research. Ananya has been working with various Fortune 500 companies in medical devices and healthcare IT domain. She is well versed with healthcare market trends and specializes in strategy building and competition insights. She has successfully driven a team and delivered over 100 market research, due diligence, and consulting assignments for various industry participants and management consultants.

What does it take to build real-world #AI enabled healthcare solution? By Vijayananda J, @vijayanandaj


Development of new technologies has undoubtedly enabled several breakthroughs in the healthcare industry. To put it simply, it has revolutionised the growth of healthcare from nascent patient-care to accomplishing treatment of life-threatening diseases. High-performance computing and the availability of digital data have extended these remarkable outcomes explaining why AI-based healthcare solutions are at top of the funding lists and are continuously gaining traction.


The progress in research due to AI is facilitating Machine Learning (ML) techniques with potential to reduce costs, improve results, and increase patient satisfaction. However, applying ML techniques to create AI models is only a small portion in the overall realization of a clinical solution. Let me illustrate this with an example. Consider two cases – identifying a person from your personal photos vs. identifying a lesion in MR images of the brain. Both of these can be implemented using standard AI techniques but there would be a considerable variance in how quickly they could be deployed in use by the end-user.

So, what does it take to build a real-world AI-enabled clinical solution? First, let me draw attention to the principle challenges that exist today – the acquisition and aggregation of data. In the world of AI, it is often believed that one who has access to data is the king. But, in my view, one who has access to curated, annotated and consent managed data is the emperor of the world!

Unlike the large collections of public datasets that are available for creating general-purpose applications such as ImageNet, CIFAR etc., there is a significant shortage of high-quality medical datasets of specific clinical conditions. A vast majority of medical data is unstructured and dispersed across various systems, which makes data acquisition, extremely complex. Furthermore, region specific data residency and privacy laws also limit the sharing of patient data.

Once the problem of data acquisition is resolved, we are then confronted with the issues of quality and diversity. For example, to develop a lung-abnormality detection application, thousands of CXR images for each of the abnormalities – both in isolation and combination are required. Searching the hospital image archive for specific abnormalities in CXR images is highly error prone due to amount of manual effort required. In addition, for many clinical problems, a single data point is inadequate and needs more elements from the longitudinal record of the patient such as imaging priors, radiology reports, genomic data, lab reports etc.

Before the model creation, the data scientist has to spend a considerable amount of time and effort on the pre-processing steps. For example, to test the condition of the cells in Pathology, pre-processing steps like detection and segmentation of nuclei and glands are required, which themselves demand a highly accurate AI model. Although Deep Learning (DL) models are good at automatically extracting the features, a number of traditional features (like pathomics in pathology) improve the overall accuracy of a classifier. The challenging part is of course to find out which features are relevant and influential on the final prediction model.

Here comes the final and most appealing part of AI – the model development. It is only through close consultation with clinical experts that a data scientist can understand the problem statement for building the AI model. The more adept the data scientist is at choosing the analytical method, the higher will be accuracy of the model. Additionally, choosing a ML vs. DL technique holds significant importance in healthcare since the solution needs to justify the end result.

At Philips, we think of AI-enabled solutions as an extension of human capabilities, rather than a replacement. Any AI-enabled solution needs to demonstrate that it can augment the healthcare provider’s expertise in a meaningful and accurate manner. As the model is trained on a limited dataset, it is critical to be aware of its limitations when tested on datasets from diverse regions.


Despite the progress on the interoperability front, there are very few common representations of clinical pathways in healthcare. An AI model can’t be implemented in a hospital using the same parameters that were used to design it in the lab. Thus, it’s imperative to integrate it seamlessly with the existing clinical workflow of hospitals.

To ease the deployment of AI-enabled solutions in healthcare while keeping the aspects of hosting, performance and security in mind, we decided to build the HealthSuite Insights in Philips based on inputs from the data science and clinical communities.

Here are a few recommendations from us, which can simplify the overall process of creating AI-enabled clinical solutions. Firstly, churn out a cross-functional team of data scientists, clinicians, product managers, application specialists, software engineers, user-experience experts, DevOps and Q-and-R engineers. Then, study the clinical problem statement and role out the limitations of the existing solution. Further, establish clarity on the intended use of the proposed solution and clinical workflow integration. Institute a comprehensive data management strategy including acquisition and curation and acquire data from geographically distributed institutions.

Spend quality time in real-time testing to determine the overall accuracy of the model and focus on matters of privacy including consent management, security and regulatory. To eliminate issues pertaining to scalability, adopt an ML platform that can accelerate the development and deployment phases. Therefore, a comprehensive validation strategy that considers the intended use of the solution, interpretation variabilities, data and workflow diversity can help us confront the challenges and unlock the immense potential of AI in healthcare.

AI is an emerging technology. The healthcare institutions which will invest in open and interoperable standards for data management and clinical workflows today, will pioneer the implementation of AI-enabled solutions tomorrow. This will not only lead to better outcomes for patients but also significantly simplify the overall healthcare system.

In conclusion, I would like to quote an ancient Indian Sanskrit verse that keeps me personally motivated as we undertake this revolutionary journey: 

The article was first published on the Author’s LinkedIn pulse blog, its republished here with the Author’s permission. 
Author
Vijayananda J

Chief Architect and Fellow, at Philips Healthcare. Lead Architect and responsible for driving the strategy, architecture and technology roadmap for the Big Data, Analytics and Machine Learning platform within Philips HealthTech

NITI Aayog’s National Health Stack – a Healthy Stack?! by Divya Raj @divyaraj1


Extraordinary problems need extraordinary solutions. And creating a country level IT infrastructure addressing challenges in India’s Healthcare management for its 1.3 billion population definitely falls very well into that category. 


NITI Aayog’s “National Health Stack – Strategy and Approach” document published in July ’18 is a good starting point in the direction of digitizing India’s healthcare management for meeting the challenge of healthcare of India’s masses. It’s a clear reflection of the realization that India’s Healthcare needs a digital infrastructure. The National Health Stack (NHS) is outlined as a “visionary digital framework” with four key components — electronic health registries of health service providers and beneficiaries, a coverage and claims platform, a federated personal health records framework and a national health analytics platform. 



However at the same time there are some gaps and untouched aspects which must be taken care sooner than later to ensure initiatives across the nation start on robust and comprehensive foundations. Ironically, while the document as well clearly recognizes that Ayushman Bharat has a 2 pronged strategy — setting up of 1.5 lakh Wellness Centers in Primary Healthcare and increasing the financial protection for secondary and tertiary care – the Wellness Centers are not at all touched upon in the proposed Digital framework. There can be no two thoughts about the high criticality of the Primary Healthcare system in India’s healthcare. NHS implementations designed with primary focus on insurance claims and coverage will be a lopsided strategy for the scale of efforts involved. In fact, extending this further beyond the Wellness Centers, the NHS must plan to give adequate provisioning for Anganwadi and other grassroots level Health-workers who are working most closely with the masses and form the lowest layer of healthcare services hierarchy which is extremely critical for preventive and primary healthcare. Any digitization initiative leaving these grassroots workers out of purview would be stunted and ineffective. Options must also be explored to address all existing gaps in this is extremely critical layer. 


Coming to the technical aspects of the NHS stack it is important to understand that in our highly democratic and federal setup it may be justified for NITI Aayog to restrict their guidelines only till technical stack level. However leaving the next line of details totally to various public and private stakeholders will likely lead to anarchic and incompatible solution outcomes across the country. It is imperative that NITI Ayog comes up with next level of guidelines and pushes the states and all stakeholders to align to those guidelines. Without going into the modalities of the way it will be done, the rest of this article will focus on some of the key design considerations which must be included by various implementers for ensuring there is basic hygiene and consistency in this National registry of this scale. 


Envisioning National Health Electronic Registry as a national one, as “a single source of truth for and manage master health data of the nation” sounds very ambitious. Rather than letting this happen at the “democratic” pace, this needs to be executed with greater authority, careful planning and a best-in-breed technology platform. At the same time we must also look at the returns on investment for this grand registry or repository – do we really see a significant proportion of patients moving across states for health treatments? 


Instead of trying to build a mammoth data repository a more practical and effective approach may be to maintain the repositories at state-level for now, provisioning the central registry to have only meta-data for querying and pulling information from the state health repositories. 


For unique identification of patients across various systems and networks a standard and uniform mechanism will have to be ensured while giving due regard to all the various Government approved Identity mechanisms and not just Aadhar. This is to ensure that the treatment are not delayed or denied for lack of Aadhar or any other identity mechanism. This needs to be balanced carefully with the need to provide robust mechanism for avoiding any Data duplication or Data redundancy. 


Another extremely important aspect to be looked into is data privacy and data security. Vision of having a centralized registry of health data for 130 billion people entails a huge challenge in terms of ensuring the data is secure, only authorised and appropriate data is accessible to stakeholders and the data cannot be misused by technical or non-technical individuals, agencies, organizations or negative forces. This requires very strong and explicit guidelines to be provided to all the implementers at different levels because any gaps and nuisances with respect to data security and data privacy can have cascading effect and has tremendous detrimental potentials on this mega initiative. 


The envisioned health registry will be the central registry for all Health establishments, professionals, patients, health workers, medical personnel and other stakeholders. And it will be closely integrated with the Health data repository which should have all the data or meta-data for all patients, their visits to all different health-establishments, diagnosis, scans, test-reports and treatments. Considering the scales, volumes and complexities it is obvious that a digital platform connecting all these cannot afford to be based on any manual data updates without any data-duplication. All the different applications will have to be integrated in a seamless manner and in real-time basis using open APIs. Hence all participating applications need to be mandated to expose APIs in a standard way. API formats and protocols need to be laid out clearly rather than leaving it to participating organizations and stakeholders. 


NITI Aayog deserves a pat on the back for envisioning the National Health Stack which will push the digitization initiative in India’s Healthcare in a big way, paving the way for numerous healthcare benefits to the masses including the financial protection and also other benefits including policy making, governance, research and so on. In doing this NITI Aayog have set the bar high for themselves. However it will be extremely important to translate this framework into large scale adoption and follow it up with detailed IT architecture guidelines for National or State Health IT Platforms, or possibly even the solution architecture itself, incorporating the inputs highlighted here among all other considerations. They must also apply the crucial lessons learnt from the India stack adoption. Only then we can be assured that this Digitization initiative goes beyond a cliche and fetches results in the range of expectations!  

The article was first published on the Author’s LinkedIn pulse blog, its been republished here with the author’s permission. 
Author
Team HCITExperts

The author, Mr. Divya Raj, is Head of Programs at E-Health Research Center (EHRC) of International Institute of Information Technology – Bangalore where he is also an adjunct faculty. EHRC has mental health, malnutrition and disabilities as the strong focus areas. Mr. Divya Raj comes with around 2 decades of IT industry experience with a specialization in Artificial Intelligence and Enterprise Integration technologies and is passionate about IT based public and social initiatives.

#Blockchain in Healthcare: Will it or won’t it survive? By Tirupathi Karthik, @TirupathiKarthi


What is Blockchain

Blockchain offers a permanent record of online transactions. Transactions are deemed as a “Block” and a ledger binds them in a “chain” thus earning its moniker “Blockchain”. Each transaction is validated and stored by a network participant based on rules but sans a governing central authority. Information can neither be modified nor copied or deleted.

Every transaction has a time and date stamp, offering a trusted transaction history and allowing verification of such records. Since the information is encrypted, the only way to access the blockchain is with a passcode. This shared ledger system makes Blockchain rather secure. Given this, Blockchain is gaining new use cases for applications that require trusted and immutable data.


Blockchain in healthcare

The disruptions wrought by blockchain technology in the fin-tech industry are all over the news – Healthcare is not immune to this disruption. Healthcare Rallies for Blockchain, a study from IBM, found that 16% of surveyed healthcare executives had solid plans to implement a commercial Blockchain solution this year, while 56% expected to by 2020. (1)

It is projected that 55 % of healthcare applications will have adopted Blockchain for commercial deployment by 2025. (2)


The known use cases of Blockchain in Healthcare

Presently, healthcare transactions are slow, cumbersome and expensive. As with any new technology in the hype cycle, Blockchain also generates a lot of excitement but has few real commercial applications. There are even fewer start-ups with a proven business model. This is both an opportunity and a threat. The threat comes from lack of traction which might eventually lead to Blockchain being ignored by the entire Healthcare industry. However the opportunity to make our own future is exciting. It is really up to our imagination to conjure up innovative solutions. Few key areas of interest seen thus far include

1.     Health Data Exchange and Interoperability

With transfer of data through API’s – Blockchain achieves standardization of data format, which is used to transmit data, irrespective of capabilities of EHRs, to communicate different HL7 versions.

Blockchain provides a foundation for secure, permissioned framework for data exchange thus allowing data to be freed up for enhancing efficiency in care coordination.

2.     Data Security and backups

In 2017, over 50,000 patient records were compromised through a series of breaches resulting in multi-million dollar fines for providers. However with Blockchain, malicious parties wanting to gain access would need to simultaneously breach every participant in the network, not just one. (3)

3.     Billing Management

An estimated 5–10% of healthcare claims are fraudulent as a result of either excessive billing or billing for non-performed services. (4)

Blockchain could reduce this level of fraud and automated billing would reduce admin costs by eliminating the need for intermediaries, ultimately making the process more efficient.

4. Pharmaceuticals and Drug Tracking

Using this technology supply chain management can track drug sourcing to reduce the impact of counterfeit drug on Patient’s health. 

Challenges to Healthcare Blockchain Adoption

Fig: Healthcare executives on barriers to healthcare adoption of Blockchain worldwide in 2016 (5)

Immature technology, insufficient skills and regulatory constraints were cited as a Top-3 barriers to adopting Blockchain technology in healthcare. Some others include:

1.     Existing systems and cultural shift

Presently patient EMR data is already being managed in large legacy systems by Health systems globally. In the absence of an adverse event, Blockchain based solutions don’t have compelling business case for a rip-and-replace strategy. So it will have to evolve over a period of time and large-scale Blockchain based EMR replacement projects are unlikely to be awarded any time soon.

2.     Healthcare stakeholder network is distributed so it’s hard to implement

Insurance payers and healthcare physician providers are all not consistent in terms of how different entities handle records. In the absence of single payer, who could drive data standards, it would be extraordinarily difficult to pull different stakeholders together to adopt Blockchain as a technology. 

3.     Many players aren’t willing to share

For example insurance payers and hospitals actively try to not share data. It is a competitive advantage for hospitals to keep cost data to themselves. If they are forced to share with insurance companies, they might get lowered payouts for patients. It is difficult to share data in an environment in which these entities are for-profit.

Intrinsically Payers and Providers have conflicting priorities. Both try to maximise their returns and hence collaboration that involves data sharing, especially on costs, is not in their interests. Consequently interoperability becomes a casualty.

Can the Dotcom boom serve as a guide to the future of Blockchain?


Blockchain technology may not be the panacea for healthcare industry challenges, but it does provide efficiency in the overall health ecosystem by dis-intermediating some high cost transactions. By now, most healthcare organizations around the world have recognized that Blockchain has the potential to reduce the cost, time and risks associated with the delivery of healthcare services. According to an analysis done by BIS Research, the global Blockchain (Healthcare) market is estimated reach to USD 5.61 billion by 2025. (6)

However as history has shown, not all such exciting technology solutions survive the real-life business. In the late 90s B2B exchanges were supposed to provide similar value by bringing all stakeholders on to a common platform. It did lead to a dotcom boom for a short while but the euphoria died a natural death when there were no takers for such a vision in the real business world. Today some of the big names in B2B tech industry such as CommerceOne, FreeMarkest, Covisint, Ariba are but a pale shadow of themselves or have been completely wiped out by forces of acquisitions. There is a lesson here for us. All solutions that can technically solve a problem may not be practically monetisable!

As Captains of the Healthcare IT industry we need to make Blockchain count for the industry and ensure its commercial success. Let’s keep the solutions small and affordable but with a laser like focus on generating lasting benefits for the providers. Once that is done, scale will come as also the eventual benefits that will allow us to monetise our investments.

Source

1.https://www.forbes.com/sites/bernardmarr/2017/11/29/this-is-why-blockchains-will-transform-healthcare/#4aa71a101ebe

2.https://www.statista.com/statistics/759208/healthcare-blockchain-adoption-rate-in-health-apps-worldwide/

3.http://health.oliverwyman.com/drive-innovation/2018/04/is_blockchain_thean.html

4. https://hackernoon.com/how-blockchain-is-set-to-disrupt-the-healthcare-industry-in-2018-5d4fda455911

5.https://www.statista.com/statistics/759312/barriers-to-healthcare-adoption-of-blockchain-worldwide/

6. http://makingthehealthcaresystemwork.com/2018/07/06/beyond-the-buzz-real-opportunities-for-blockchain-in-health-care/

The article was first published on Mr. Tirupathi Karthik’s LinkedIn pulse Blog, the article is republished here with the Author’s permission. 

Author
Tirupathi Karthik

A leader in the Healthcare IT space, Tirupathi Karthik has extensive business leadership experience across Asia, the Middle East and USA, particularly in the enterprise software space. He is a passionate advocate for the innovative use of technology that turns IT investments into competitive differentiators for their stakeholders rather than using IT as a pure cost containment initiative.

In various hospital implementations, he has been championing the use of Mobility as a pervasive information delivery channel. His vision led to the use of themFirst approach with the infusion of HTML5 and Apple’s mobility products across the Napier platform. Napier’s leadership in the global marketplace continues to gather momentum on the back of one of the most modern implementations of such a technology stack.

As an Eldercare thought leader, he has been driving productivity agendas for aged care models globally and seen to the expansion of Napier’s product vision to include elderly care services delivery. Applying technology-enabled solutions for senior care providers offering nursing home, home care and activity-centre services, Napier today enables productivity and improved quality of care.

Containing Health Care Cost, What is our role as a Physician? by Dr. Chandrika Kambam @Ckambam



Indian health care is at an inflection point. Today governments’ spending on healthcare needs is one of the lowest amongst the Developing countries [1]. India spends about 5% of the total expenditure on Health which is around 1.7% of the GDP. Public healthcare growth has slowed down over years. In 1998 about 43% of population was served by Public Hospitals and today only 30% use the Public health care system. [2] That means almost 70% of the health care needs are serviced by Private players, trust hospitals and non-profit institutions. This has led to the rapid growth of Private players who are growing at the rate of CAGR 16.5% year on year [3]. The costs of procedures or hospitalization has increased anywhere from 83% to 263% in 10 yrs. i.e. 2004 to 2014. There is also a wide variation of the cost for the same procedure in different hospitals [4]. It is also noted that 86% of rural Indian patients and 82% of urban Indian patients do not have access to any form of employer-provided or state-funded insurance.


Government of India is cognizant of this gap and is taking a 360-approach to help people of India get affordable, accessible, quality healthcare. They have capped prices for certain lifesaving drugs, stents and implants. They have created a common entrance examination throughout India. The Medical council of India is being replaced by National Medical Commission which has more representation across different states. Ayushman Bharath is world’s biggest and ambitious project to cover 10 lakh family appropriately 50 crore people based on socio economic status defined by the Socio- Economic caste census 2010. 

Some of the states are also proactively implementing systems to monitor delivery of the healthcare services through State medical establishment acts.
Being an integral part of the healthcare delivery system, we are not only responsible for treating patients but also understand our role and responsibility in the way care is delivered. We are the primary drivers, who can steer the system in the most cost-effective way, with good clinical outcomes or remain oblivious of costs! In order to help the patient and the hospital, it is important we understand what goes in to the revenue and costs of running a hospital and how each factor plays a role in escalating and deescalating the costs. In a study done by IMS (Intercontinental marketing company- Parent IQVIA) institute on avoidable costs in healthcare[5] they attributed avoidable costs into six major buckets: They are:

– Medication Non compliance
– Non/Delayed adherence to Evidence based medicine
– Antibiotic misuse
– Medication errors
– Suboptimal use of generics
– Mismanaged polypharmacy in elderly

If the above mentioned are the six major causes in the delivery of care, the following are the major factors in inappropriate utilization of services i.e. inappropriate admissions, overuse of outpatient services, misuse and abuse of prescriptions and unindicated ER visits. 

Medication non adherence:
Medication non adherence alone contributes to $68billion to $148billion dollars in costs. Patients usually are non-adherent to prescriptions due to costs, lack of information on the long term effects of noncompliance, cultural beliefs, side effects and lack of social support. It is noted that only 75% of patient fill their prescription when written first time. And 32% -40% do not fill up their prescriptions on subsequent follow up. Government initiatives in capping the prices and fixing the selling price do help in improving compliance. But as Doctors we can play our role by educating patients, prescribing low cost, quality product so that we do not burden our patients.  

Nonadherence or delayed adherence to Evidence Based Medicine protocols:
Avoidable costs due to delayed or non-adherence to evidence based medicine costs anywhere from $19 billion to $64billion. Not able to timely diagnose, start treatment and lack of follow up are the major contributing factions. Guideline adherence is seen only on 61.9% in Diabetes and 20% in Hepatitis C patients. The importance of keeping ourselves updated with recent changes in the standards and protocol and use them appropriately in order to avoid such wastage cannot be stressed enough. Educating patients on long term complications and help patients understand that prevention always costs less than the actual treatment, goes a long way. 


Antibiotic misuse:
Antibiotic misuse, the cost opportunity for the antibiotic misuse ranges from $27 billion to $42 billion. Prescriptions for viral infections and usage of broad spectrum antibiotics tops the list of Antibiotic misuse. The common reasons are pressure from patients, defensive medicine. Being more responsible, while prescribing antibiotics, understanding the communities’ microbial nature and their sensitivity pattern helps to decide on the antibiotic needs. 

Similarly medication errors, suboptimal use of generics and mismanaged polypharmacy in elderly also contribute to approximately $50billion in costs. 

Apart from patient and clinical factors, administrative factors adds on to $126 to $315 billion in cost for delivering health care[6]. The cost are majorly coming from ineffective claims process, staff turnover, ineffective IT systems and paper prescriptions.

There are tools available to calculate the healthcare wasteful spending in USA[7]. These tools assess spending at the micro level, helps to develop specific targets and to assess the results of specific Interventions.

Another trend that is catching up is on payments based on value of care given rather than quantity. Value based payment models are slowly, but surely catching up across many developed countries and in India it is in its nascent stage enforced by few Insurance companies.

While we are grappling with inadequate funding, inefficient systems, lack of standardization, there is whole new wave that is going to make its presence felt sooner than later which is on “Information technology” in health care. There is already quite a bit of information technology solutions used in public sectors such as national health portal, online registration system, Central drug standard control organization so on and so forth. In private sectors the use of technology is far advanced in the form of electronic medical records, apps, call center, point of care devices, internet of things etc… The growth of this sector in health care will continue to see upswing as they try to help us find out solutions for each of the problem case in Health care. 

The hospitals of the future will move from hospitals to home, utilize mobile technologies to stay connected with patients, care pathways to help standardize delivery of the care[8]. The hospital beds probably will get restricted to use for post-operative care, intensive care and such other high end work. Public insurance will gradually increase the spectrum of population they cover[9]  and public private partnership has to happen in order to deliver care for such huge population base.  Becoming cost effective is the need of the hour.

[10] Rising income level, ageing population, growing health awareness and changing attitude towards preventive healthcare is expected to boost healthcare services demand in future, but in a different areas, than what it is today. We need to understand these trends and prepare ourselves better so that we are not caught unaware.

The article was first published in American college of Physician-India chapter in their 3rd annual conference, Lucknow and has been republished here with the author’s permission.

References: 

1.  World health organization and world health statistics 2017
2.  National sample survey office(NSSO)
3.  Frost and Sullivan LSI financial services, Deloitte
4.  BMJ Open. 2013; 3(6): e002844.Published online 2013 Jun 11. doi:  10.1136/bmjopen-2013-002844 PMCID: PMC3686227 PMID: 23794591 Costs of surgical procedures in Indian hospitals Susmita Chatterjee and Ramanan Laxminaraya
5.  IMS Institute for healthcare informative: Avoidable costs in Healthcare.
6.  Analysis by PricewaterhouseCoopers’ Health Research Institute: the price of excess.
7.  American health policy institute: using data driven disruption to reduce wasteful spending in health care.
8.  NAT health PWC funding Indian healthcare, catalyzing the next wave of growth.
9.  An HFMA value Project report: Strategies for restructuring costs structure.
10.  India brand equity foundation


Author
 Dr. Chandrika Kambam

PRESENT DESIGNATION: VICE PRESIDENT – CLINICAL SERVICES

PRESENT AFFILIATIONS: COLUMBIA ASIA HOSPITALS- INDIA

MAJOR ACHIEVEMENTS: ESTABLISHING CLINICAL GOVERNANCE AND QUALITY MONITORING SYSTEM FOR COLUMBIA ASIA GROUP. MANAGING DOCTOR RECRUITMENT AND DOCTOR COST. STARTED HOSPITALIST PROGRAM. WORKED WITH THE TEAM ON VARIOUS DISEASE SPECIFIC PROGRAMS E.G. COLUMBIA DIABETES CARE. INVOLVED IN CONTINUOUS REVIEW AND UPGRADE ON INFORMATION TECHNOLOGY FOR THE GROUP. ACTIVELY INVOLVED IN OPENING NEW HOSPITALS.

PURSUED ACADEMIC QUALIFICATIONS, BY COMPLETING MBA, ABMQ, PGDMLE .

I HOLD VOLUNTEER POSITIONS IN KRUPANIDHI GROUP OF INSTITUTES AS MEMBER OF BOARD OF STUDIES.

I WORK WITH NASSCOM ON CENTRE OF EXCELLENCE FOR IOT IN HEALTH CARE SPACE.

I am founder and chairman for Yeshshomaheswari trust through which I help economically weaker section for education and health care needs.

Free time, I enjoy gardening and I work out to keep fit. I write blogs on topics close to my heart. drchandrikakambam.com

Software Product For Hospital Industry by Girish Koppar @KopparGirish


Before we talk about software product for hospital industry lets understand how the Hospitals are broadly classified

– Based on the legal entity ( Private , Trust or Corporate)
– Based on specialty ( Super specialty, Multi-specialty, Single specialty)
– Based on bed strength ( Larger hospitals and Nursing Homes)


Hospital Industry is unique as compared to BFSI and FMCG industry as there is minimal or almost no standardization in the Processes/Operations between hospitals of similar nature, for example “Admission, Discharge and Billing Processes may vary from hospital to hospital. One more major difference is about the employment of Doctor’s. In some hospitals Doctors are Consultants and in some hospitals they are employees or on the payroll of that hospital. 

Due to above factors it’s very difficult to build and implement a global product for hospital industry. Although many companies have attempted to build a global product for the hospital industry they have not been very successful. 

The software product developed by the vendors may be technically sound for the hospital industry. However, most of the vendors face major implementation challenges as they are not aware of the practical scenarios in different hospitals since the nature of the hospitals and processes in every hospitals vary as mentioned above. 

Hence the customization percentage is very high and the stability of the product becomes an issue. As the degree of customization various from hospital to hospital, the customized product becomes local to that hospital and it becomes difficult for the vendor to maintain and give support to a particular hospital. The other major challenge faced by the vendors is to implement a software product/solution in a brown field project (running hospital), where the processes are set, hence there is a resistance to change by the users to implement a new software. In case of a green field (new hospital) it is not very difficult to implement a software solution as there are no preset processes.  

Now let’s see the major software applications used in hospital industry.
–   HMS (Hospital Management System).
–   RIS (Radiology Information System) and 
–   PACS (Picture Archival and Communication Systems).
–   DMS (Document Management System).
–   Mobility Apps.

HMS is the core application or like the ERP used in hospital industry. It mainly contains modules like Admission, Discharge, Transfers, Billing, In Patients, etc. Since it has the mentioned modules like Admission and Billing, it’s difficult to develop a global HMS application as the variation in processes across hospitals. Other modules like Finance, Inventory, PACS are standard in nature and may or may not be a part of HMS. These modules can be separately developed and seamlessly integrated with the HMS application. Most of the hospitals have adopted the practice of having HMS with only the core Billing and Admission modules and build & integrate other modules around HMS.

Mobile apps & BI tools have helped Vendors to build standard applications wherein they have to fetch the data from HMS and other modules and display it in the app. Unlike HMS application which is dependent on the processes of that particular hospital, mobile apps & BI tools are not process dependent and just fetch data from HMS and other modules to be displayed to the top management for analysis of Business process and making key decisions.

Lot of vendors are now focusing on capturing clinical data and converting the same into EMR/EHR. Although there are various solutions available for capturing clinical data adoption of such software is still an issue. Since most of the hospitals have started capturing clinical data, the next logical step is to use the data to develop applications that can assist doctors in their diagnosis and treatment. CDSS (Clinical Decision Support System) and Artificial Intelligence will be the focus of the vendors which will bring a revolution in the Healthcare ecosystem. These applications will be widely used by Doctors not only for preventive health to diagnose and treat their patients, but also will be used to predict the health of a patient depending on the amount of data that has been captured.


The article was first published in the CIO Insider Magazine, here. The article has been republished here with the authors’ permission.
Author
Girish Koppar

Experience of managing IT for Lilavati Hospital and Research Centre for over a decade, and an overall experience of 25 years

Committee Member of HIMSS (Healthcare Information and Management Systems Society) Asia Pacific Chapter and International Member of CHIME (College of Health Information Management Executives).

Secretary & Principal founder of Hospital Information Technology Association
(http://www.hospitaltech.in/) connecting IT personnel across various hospitals pan India. HIT Association is a non-profit association registered under the Bombay Port Trust Act which aims to “Provide Transformational and Visionary Leadership for successful adoption of Digital Technologies in Hospitals.

Board Member and Co-Founder of Medical and Health Information Management Association (MaHIMA) http://www.mahima.org.in/MaHIMA is accredited by Maharashtra Medical Council (MMC) for conducting Continues Medical Education.

On the advisory board of the following companies as a Healthcare Subject Matter Expert (Honorary)
https://www.lemarksolutions.com/mentors
http://findyourfit.in/?page_id=1235

Electronic Health Record System from the Perspective of Data Privacy by Dr. SB Bhattacharyya @sbbhattacharyya

Electronic health record systems handle health-related ultra-sensitive data of a person throughout his life, along with all personal information that accurately identifies him. This makes it imperative to protect the data from cyber-threats and consequent untold damages. This article discusses the various issues involved and the different mitigation methods.

During the course of any clinical encounter a person discloses ultra-sensitive health related information to his provider to enable the latter to address his health-related problems better, faster, and hopefully, cheaper. Information that he would otherwise rather keep well under wraps. Ethics demands all providers treat all information that their patients disclose to them with the greatest of care and keep them secreted away from everyone, even the spouse, unless explicitly released from this obligation by the patient. The confidentiality of the private information needs to be maintained at the highest possible levels of security by medical professionals at all times—unless there are extenuating circumstances to disclose them, like for the public good, compliance to the law, etc.


When the information is recorded electronically, the onus of maintaining the secrecy continues to wrest on the provider and he needs to ensure that it is indeed maintained at all times, else he would be liable for all consequences thereof. The fear of compromises due to lack of adequate control of the cybersecurity from threats has made the public to naturally be very wary of having their information maintained there. The digital health industry is aware of all this and already have in place a number of appropriate processes and enabling tools that are able to effectively address them to robustly. The following sections discusses some of the commonly-used ones in brief and simply.

EHR

An Electronic Health Record (EHR) is a life-long record of all the different health-related encounters that a particular person has throughout life. All of these encounter documents need to be lined up and merged together into a single continuous document to help provide that person’s journey through life with respect to health. This life-long record contains every single health-related detail of a person, many of which are sensitive enough to merit special considerations be given to the data privacy and confidentiality issues so that the person whose data is being handled and his provider are able to feel reasonably confident about permitting their location in an electronic format in the cyberspace.

Privacy is the claim of individuals, groups or institutions to determine for themselves when, how and to what extent any information about them is communicated to others. It also refers to the ability of individuals to manage the collection, retention and distribution of private information and has been variously defined as the control of access to private information while avoiding certain kinds of embarrassment and ensuring what all can be shared, or not, with others. In short, privacy is ensuring that others do not get to know all that one does not wish to tell.

Confidentiality

Confidentiality is the protection of personal information and entails keeping certain information strictly limited to a selected few and usually is a set of rules or promise that ensures it.
Confidentiality in healthcare requires healthcare providers to keep a person’s personal health information private unless consent to release the information has been provided by the patient.
Patients routinely share personal information with health care providers. If the confidentiality of this information were not protected, trust in the physician-patient relationship would consequently be diminished. Persons would then be less likely to share sensitive information, which could negatively impact their care.
Creating a trusting environment by respecting a person’s privacy encourages the patient to seek care and to be as honest as possible during the course of a health care visit. It may also increase the person’s willingness to seek care. For conditions that might be stigmatising, such as reproductive, sexual, public health, and psychiatric health concerns, confidentiality assures that private information will not be disclosed to anyone including partners, family, friends, employers or any other third party without their explicit consent.

Due to ethical and legal reasons, breaching confidentiality is justified, but only in certain special circumstances.
1. Concern for the safety, both of self and of other specific persons: access to medical information and records by third parties is legally restricted. Yet, at the same time, clinicians have a duty to protect identifiable individuals from any serious, credible threat of harm if they have information that could prevent it. The determining factor is whether there is good reason to believe specific individuals (or groups) are placed in serious danger depending on the medical information at hand.
2. Legal requirements to report certain conditions or circumstances: applicable laws usually require the reporting of certain communicable/ infectious diseases to the public health authorities. In these cases, the duty to protect public health outweighs the duty to maintain a patient’s confidence. From a legal perspective, the state has an interest in protecting public health that outweighs individual liberties in certain cases.
3. Ethical considerations make it indefensible not to use information that may save the life and limb of another, where the data of one may help not only alleviate the pain and suffering of another but perhaps even save the life that would otherwise be lost. For example, if a person has a life threatening condition and information about someone else also having suffered a similar condition who was successfully treated of the condition exists, then it would be morally indefensible not to use that knowledge and save a life.

Security

In a healthcare context, security is the method and technique to protect privacy and is a defence mechanism from any type of attack. Studies have showed that the slow adoption of EHR is mostly due to privacy concerns. People need to be in control of the collection, dissemination, and storage of their health information. If they feel out of control, their feeling of vulnerability and general mistrust of healthcare information systems and the information that they have disclosed with the expectation of it being held in trust increases manifold. Digital health systems are used in medical applications for delivery, efficiency and effectiveness of healthcare and the users have the right to know about the various security measures that are in place in order to feel secure about their privacy.

Functional Challenges

The various functional challenges to the successful establishment and use of an EHR are as follows.

Centralised availability

There is a need to ensure that all records of a person are available at a central place so that they may be accessed and processed together in real-time.

Privacy issues

There is a need to ensure that private things are indeed kept private.

Confidentiality issues

There is a need to ensure that confidentiality of information is maintained as well as the information is available to those who need it for safety, legal or ethical reasons.

Security issues

There is a need to ensure that both of the above are successfully addressed in a meaningful and demonstrable manner to the satisfaction of care receivers (persons and patients) and their care providers (medical professionals).

Technological Solutions

The various functional challenges detailed above are addressed in this section.


Cloud-based solutions

The ‘Cloud’ is actually a group of interconnected computer servers that is accessible through the Internet by a broad group of authorised users across enterprises, geographical locations and operating platforms.
A person visits a number of healthcare professionals to receive services over his lifetime. These services could be for routine attention like immunisation, health check-up, etc., or special like a doctor visit for consultation due to illness or a facility visit for undergoing procedures like surgery or emergency due to some accident – minor or major.
Each of these healthcare encounters leads to the creation of a record. Creating one single life-long record from all of these individual encounter-based records requires all of the latter to be serially collated from the very first to the very latest and then processed together. Consequently, the availability of all the records at a central place is crucial.
Either using a Cloud-based solution or storing a copy of each and every encounter in the Cloud makes this very practical.

Cyberspace, security, and threats

Cyberspace is a notional space created by networking various digital devices including computers. Basically, it is the electronic ecosystem where not all of rules of the natural laws of physics and chemistry apply. The ‘Cloud’ essentially exists in the cyberspace.
Cyber security refers to the techniques of protecting computers, networks, programs and data from unauthorised access or attacks that are essentially malicious.
Cyberthreat is the possibility of malicious attempts to damage or disrupt a computer network or system.

Achieving EHR security

There are a number of methods by which adequate levels of security can be achieved in any EHR system that would be sufficient to allay the various security-related concerns of the stakeholders.

Technical Solutions – using various security techniques as follows:

Encryption

• Authentication
• Role based access control (RBAC)

Human Solution

Privacy Awareness
• Privacy Education
Information system designer and developers need to ensure that privacy requirements are included in the design and development phases itself. This is an extremely issue that all EHR vendors must pay particular attention to and failure to do so would in all likelihood result in serious legal consequences, which would mean one definite thing—business failure.
Security anywhere is as weak as it is at its weakest point. This unfortunately happens to be the users themselves. Using passwords that can easily be guessed, making them available from where even a toddler can access, sharing them with all and sundry, etc. are all extremely dangerous practises that many users, unknowingly and knowingly continue to indulge in for a variety of reasons, mostly due to matters of convenience.


Encryption

Encryption is the process of using an algorithm to transform readable data into an unreadable encoded one in order to make it incomprehensible to unauthorised users. The encoded data can only be decrypted to make it readable with a security ‘key’. This end-to-end data protection process, which falls under the science of cryptography, is essential for to ensure a trusted delivery of sensitive information, including those over such open networks like the Internet.
Advanced Encryption Standard (AES) is an example of symmetric-key encryption process that uses a 128-bit, a 192-bit or a 256-bit key is considered pretty reliable as breaking them is virtually impossible at the currently available computing power. The Pretty Good Privacy (PGP) is an example of asymmetric-key encryption and is a public-key encryption process that uses private and public keys in tandem.

Authentication and authorisation

Authentication is the process of determining whether someone or something is, in fact, who or what it is declared to be. This is accomplished by identifying an individual through the person’s unique user identifier and a password (or passphrase, biometric, OTP challenge, etc.). It is distinct from authorisation, which is the process of giving individuals access to system objects based on their identity. Once a person has been authenticated, he is permitted access to the system based on his access rights. This is authorisation. Both are accomplished through the log-in functionality.

Role-based access control

Aka RBAC, this is a process by which system access to users is granted based on the roles they are authorised to perform. By tagging the roles to access, a user is permitted, or not, to execute a certain set of functions based on the roles they perform. This provides the flexibility to deny any unauthorised user, including those unknown, who are trying to gain access with malicious intent, from carrying out task or tasks that they are not permitted to.

Consent management

Any person whose data is being managed using a system needs to provide as explicit a consent as is practical to permit anyone who uses the system to access the data, or not. Taking such a consent in as transparent a manner as possible provides the necessary legal protection to all those who use the system and access the data contained therein while ensuring that the person who has provided the consent has done so with sufficient clarity as to what all he has consented to and not.

Audit trail

The genesis of audit trail belongs to the world of accountancy and is basically a system that traces the detailed transactions relating to any item in a record. In the context of EHR, it is a tracing record of detailed transactions of all activities performed on it. Such a record is able to keep track of everything that has occurred with respect to the EHR and is able to provide details of all activities, thereby making it easier to detect most, if not all, malicious activities. Any compromise to the data integrity or the performance of any nefarious activity can not only be traced but the culprits identified so that necessary action can be undertaken, often in real-time.
Through the use of audit trail in digital health documenting systems, any person or entity, including a court of law, can be provided with sufficient information with a better-than-acceptable levels of confidence that the health records maintained in the electronic format is safe and secure.

Data integrity

Data integrity is a fundamental component of information security and generally refers to the accuracy and consistency of data stored anywhere, whether in a database or data warehouse or data mart or something else. For data to be complete, all of its characteristics including business rules, relations, dates, definitions and lineage need to be correct. Data integrity is maintained through the ongoing use of error checking and validation routines, like ensuring that numeric columns/cells do not accept alphabetic data.
As a process, it verifies that the data has remained unaltered in transit from creation to reception. As a state or condition, it is a measure of the validity and fidelity of a data object. Database security professionals employ any number of practices to assure data integrity, including data encryption that locks data by cipher, data backup that stores a copy of data in an alternate location, having in place appropriate access controls, including assignment of read-write privileges, input validation, to prevent incorrect data entry, and data validation, in order to certify uncorrupted transmission.
This ensures that the data, as intended to be captured, is not only captured in that state but also stored, retrieved, or exchanged, is exactly the same from the time of entry forever.

Hashing

Hashing is the transformation of a string of characters into a fixed-length value or key that represents the original string and is used in many encryption algorithms apart from its use in indexing data in databases to make data location and retrieval quick.
This technique makes it possible to generate and store a hash key of a particular record and subsequently to re-generate the hash key of the same record and check the re-generated key with the original key. A match means that the original record is being preserved. Else it points to compromise of the record’s integrity. This is a red flag indicating breach of security that may have privacy and confidentiality issues.

Safeguards:

Physical

These are safeguards put in place to ensure that all computer hardware including servers, networking equipment including routers, continuity of power supply and temperature maintenance are in a safe place free from any physical harm due to elements of nature, acts by animals or breaking and entering by humans. Various guidelines are drawn up and rigorously followed to ensure that all threats are adequately dealt and mitigated.

Administrative Safeguards

These are basically a set of standard operating procedures related to how security is to be handled, the rules that govern the personnel who deal with or handle sensitive data, how risks are to be managed, methodology for oversight, etc.

Blockchain technology

As of 2018, this is ‘the new kid on the block’ as far as cyber-security is concerned and appears to be on the up and up on the hype cycle of the type popularised by Gartner.
A blockchain is a continuously growing list of records that are linked and secured using cryptography containing a cryptographic hash of the previous block, a timestamp and transaction data. By design, it is inherently resistant to modification of the data. They are secure by design and exemplify a distributed computing system with high fault tolerance. This makes these types of database potentially suitable for the recording of events, medical records, and other records management activities, such as identity management, transaction processing, documenting provenance, food traceability, voting, etc.
Sadly, what prevents it from being the answer to all EHR-related problems is its inherent latency in data retrieval. While this is not a serious enough issue in the non-critical care settings like outpatients or routine inpatients where the patient is well-settled, it is definitely a problem that cannot be mitigated using high-end technological solutions in critical care settings including accidents and emergencies.

Privacy awareness & education

Painful as it is, there is no recourse other than to admit that awareness about privacy and rights related to confidentiality is practically non-existent amongst the publicat-large. Too often a person will not think twice before sharing their intimate details on the social network, but mention of someone entering information into a health information system makes that very person extremely concerned that assumes the hue of outright paranoia. Such a situation is, sadly, all too common for one’s comfort.
This results in the requirement of appropriate raising of awareness and educating the stakeholders using simple and easy-to-follow techniques so that their concerns are adequately allayed and their knowledge regarding the various related do’s and don’ts are suitably augmented. Public messages in the media, private emails and messages, availability of capacity building videos and other educational documents, appropriate postings in the various discussion forum, etc. are some of the various methods that can be adopted in this regard.

Concluding Observations

As is evident from the various functionalities, techniques and tools mentioned and discussed above, robust safeguards are well-known and extensively used by the Information Technology industry to ensure that the privacy and confidentiality of any data can be securely handled with reasonable care.

By using them in EHR systems the data they contain can be well-protected in a reasonably secure manner. Stakeholders can consequently rest easy, confident in the knowledge that the sensitive health-related data contained in EHRs are sufficiently safe in the cyberspace.


The article was first published here, it has been republished on the HCITExperts Blog with the authors’ permission. 

Author
Dr. SB Bhattacharyya

SB Bhattacharyya is a practising family physician and health informatics professional with more than 29 years of experience as a general practitioner and business solution architect for digital applications in healthcare, pharmaceutical and medical devices domains. He is currently Member, National EHR Standardisation Committee, MoH&FW, Government of India; Member, Healthcare Informatics Sectional Committee, MHD 17, Bureau of Indian Standards; Member, IMA Standing Committee for Information Technology, IMA Headquarters; and Head – Health Informatics, TCS.

A Data Scientist’s Experience in Decoding Chest Imaging by Vidya MS


The Chest Imaging Update 2018 held by the Narayana Health group, brought together over 150 radiologists, pulmonologists and doctors gathered to update and improve their knowledge in the reporting of Chest Imaging, both X-ray and CT. As a data scientist with keen interest in medical imaging, my aim was to get an inside look into the daily practice of medical professionals in detection and diagnosis of pulmonary diseases.


The conference opened with Dr. Vimal Raj, Conference chair and an accomplished Cardiothoracic Radiologist, stating that thoracic imaging is not reported well enough and there is a lot more value that a radiologist could add in the diagnosis to enable better treatment options for the patient. The flow of the subsequent sessions was extremely well constructed reflecting the everyday reporting workflow in the chest imaging space – starting from assessment of the humble chest X-ray to the CT followed by diagnosis and treatment plans. The second day was mainly focused on hands on sessions.
The first session of the day was conducted by Dr. Kishore Kumar from NH and Dr. Aparna Irodi from CMC Vellore on what never to miss on a chest X-ray and assessment of neonatal chest-X-rays. Dr. Kishore covered the important parts to access in a chest X-ray before reporting it as a normal one. One of the challenges that he brought out was that these areas are often easily missed and there is large amount of misdiagnosis made while assessing a chest X-ray which could led to significant impact later on for the patient. One case was presented where an early stage opacity, which could have been easily caught, progressed into a cancerous stage. There are a multitude of reasons for misdiagnosis, including, superimposition of thoracic structures, similarity in radiographic appearances of some chest diseases, and subtlety of some chest pathologies rendering them indistinguishable. Each chest X-ray takes a trained radiologist several minutes to review and given the ubiquitous nature of the X-ray in the imaging world, this often leads to significant increase in the workload. Another reason could be the variance of reporting across radiologists due to inconsistent terminologies.
The sessions then shifted towards the more complex CT imaging, where the physics and protocols behind the CT imaging were first introduced, followed by multiple sessions on reporting of lung cancer on a CT and standard definitions of chest patterns on CXR and CT to ensure better reporting.
Since the conference was focused on updating radiologists and how to better report their studies, the important question on why accurate reporting is needed was excellently handled by Dr. Murali Mohan. He walked through a comprehensive series of statistics conducted by various institutions, to explain why the importance of reporting accurately.
One of the highlights and most interesting sessions for the day for me, was the ‘Multi-Disciplinary Team’ panel featuring, Dr. Vimal Raj, Dr. Murali, Dr. Aparna, Dr. Rajani Bhat, Dr. Ranganatha R. They wonderfully presented how an MDT team conclude in diagnosis and determining a course of action when things are unclear. This was extremely insightful for an outsider like myself to truly understand the complications, uncertainties and how the various disciplines come together to define the best course of action for the patient.
Considering all the complications mentioned above, the variations in manifestations of the same medical condition across demographics, overlap of multiple patterns and mimic cases, many of these cases require significant expertise in the diagnosis of a thoracic scan. There is also a high variance in reporting across radiologists and the opinions are sometimes subjective especially in chest X-rays. Many of these uncertainties sometimes require a group of expertise to reach a consensus. In addition to these clinical challenges, developing an AI system to aid the reporting process requires large amounts of consistently reported data to truly learn the patterns on an image. Many of these systems also do not have access to much of the other patient information that may be pivotal in assessment. The difficulty in obtaining such large high-quality data makes it more complex to build these systems.
AI is still not there to diagnose the myriad of pathologies on a thoracic scan, primarily the chest X-ray though there have been many solutions claiming to detect a limited set of abnormalities. That being said, with the immense improvement in technology in the recent years, AI can certainly aid a radiologist in diagnosis and subsequently the treatment. The focus for AI models should be around aiding radiologists in repetitive tasks, ensuring that highly skilled expertise focuses on more complex and abnormal cases, detecting and highlighting unusual patterns in the image (especially those than can be easily missed), and also in providing clinical insights to a radiologist in the detection of abnormal regions.

Finally, I would like to thank the organizers of the Chest Imaging Update 2018. Overall, it was a wonderful experience for me to be in the midst of a large group of experts from various disciplines, interact with some of them to understand the key problem areas. 

The article was first published on the Author’s LinkedIn pulse Blog, its been re-published here with the Author’s permission. All ideas presented are author’s personal views

Author
Vidya S.M

Data Scientist at Philips. Data Scientist with a demonstrated history of working in the hospital & health care industry. Skilled in AI, image processing, & algorithms. Strong engineering professional with a Master’s degree focused in Machine Learning & AI from National University of Singapore.

PregBuddy’s year with Google Launchpad by Sivareena S. L. @SarikaSivareena



We’re all aware about the Google Launchpad accelerator which selects pre-series A startups across the globe every year to assist them scale their business. Along with this, Google Launchpad has few more offerings where they have extremely well structured programs for various stages of startups. Pregbuddy has benefited from couple of these programs as we grew our product.



We started building PregBuddy from the end of 2016. In the first half of 2017, we were heads-down focused on co-creating our product with our early set of users. PregBuddy is India’s first peer-to-peer reassurance platform for expecting mothers. Along with personalised information, we connect them based on their trimester, location, vernacular language and medical condition. Our early set of users loved PregBuddy, we were growing organically via word-of-mouth and via discovery on Google Play Store.

A year ago, in July 2017, we got into Google Launchpad Build – a 2-day super-packed program for early stage startups that connects mentors to validate their product and growth strategies. This was one of the key events in our journey of building PregBuddy.
Let me share with you how it helped us:
  1. Early Product Validation: The fact that Google selected a handful of startups across India was a great early product validation that we are building something good, and meaningful for our users.
  2. Right Mentor-Startup Matching: Google Launchpad has a huge network of mentors from within Google and industry experts in the fields of technology, product, marketing, sales and design. The Launchpad team matches each startup with the right mentors based on the challenges we’re currently facing or based on our product roadmap.
  3. Expert Insights: Launchpad mentors not only shared their perspectives on our product, design, and growth strategies, but also shared their insights on how we can structure our business model, focus our efforts to scale faster and what may not work based on their learnings. This was very crucial at that stage of our bootstrapping journey, as it helped us save both time and money and not repeat mistakes which others did.
  4. Media Coverage: Google Launchpad Build literally helped us launch PregBuddy in media with their wide media coverage. We got covered both online (YourStory, Inc42, and more) and offline in print media (Deccan Herald, Business Standard, and more). It helped us reach a wide variety of audience – users from across India, talent who wanted to work with us, and get attention from potential partners and investors from the startup ecosystem.
  5. Inbound Partnerships: Apart for the fact that people got to know about PregBuddy, and we got some users, Google Launchpad helped us get inbound leads from hospitals and brands, who wanted to work with us.
  6. Continued Support: Being a part of the Google Launchpad alumni network, the Launchpad team provides continued support with the right introductions with mentors, businesses and technology support whenever we needed.
  7. Recognition & Growth: The fact that you graduate from a program like Google Launchpad really opens up doors. While working closely with our inbound partners, we were able to identify much deeper challenges which we are now solving in the healthcare space.Since 2017 PregBuddy has grown from being just a small project to a company, thanks to the support of Google Launchpad. We’re humbled to be awarded by actor Akshay Kumar and Govt of India, and onboard renowned angels from Google, Uber, Times Group, YuMe, and Indian Angel Network.
Earlier this year, we were fortunate again to be a part of the 1st cohort of Google Launchpad Solve for India program, where Google selected 10 startups who are focused on solving local challenges and building experience for the next billion users coming online from India. It was at the perfect time in our journey, where our first hires got a chance to interact and learn hands-on from the Google and industry experts from this 5-day structured Launchpad program. A really great exposure for your core employees.

I’m sure you’re wondering how you can be a part of Google Launchpad, well here is your chance. Google Launchpad has recently announced an accelerator program focusing on the startups Solving for India using AI/ML technologies. If you are one of them, do apply here. So, what are you waiting for? Get on the ride, and take your startup to the next stage with Google Launchpad!

Pregbuddy made it to the Yourstory’s 2018 Tech 30 Startups
Author
Sivareena S.L

Co-Founder PregBuddy (Google Launchpad)

Almighty Data or Hype? By INDERJITH DAVALUR @INDERDAVALUR

DIGITAL TRANSFORMATION AND THE PLACE FOR DATA


Mea Culpa, I am one of those who is guilty of getting on and staying on the Big Data wagon for the wrong reasons. “Data is the new oil” is an oft-repeated phrase. I am about to commit a “virtual” suicide by proclaiming that it is not so. Data has its place and it is not at the top of the digital food chain. I feel that we have crowned the half-naked prince, Emperor in haste.

For the sake of clarity, when I say data, I will be referring to digital data throughout this piece. Data is a by-product of any activity. Therefore, creating data is as natural as breathing. So we have data. A lot of data. So what? Accumulating data, structuring it, storing it, analyzing it are a natural progression from that point onwards. How and what we do with the data is more important. Software. 

The magic that is software, to me, is more transfixing. Consider the prospect of a language written in a semantic that is alien to our natural human language. A cryptic command, logic, condition, trigger – anything at all – that is magically read, understood and acted upon by silicon. Hardware that contains baked-in code that can parse and carry out complex instructions at blazing speeds. Pieces of such chips soldered on a board and communicating through ‘roadways’ of circuits laid out on a board. The miracle of hardware coupled with the magic that is software is what gets my adrenalin pumping. How can such a marvel not be exciting?

Even the awesomeness of hardware pales in comparison to software. Hardware is more or less static. It is confined to physical and functional dimensions. Software, however, is supreme. It can use the same hardware (with some limitations of course) and carry out simple tasks, entertain with games, or perform wildly complex calculations at very very high rates of speed, accurately all the time. And it can do this million million times with alacrity. This is just the beginning of what software can do. But wait, there’s more!

Consider intelligence in software. It suddenly becomes a living, breathing, dynamic being. Almost. Software can learn and teach itself. Crunching data and spitting out patterns and actionable analysis suddenly becomes mundane, banal almost pedestrian. No. I am not against data or big data. By itself, big data is just that. A monstrosity. Sometimes, big data actually gets in the way. Misleads us in making decisions quickly. Software breathes life into data. 

Take any software language or tool. Examine it. Study its flow, the eloquence, the nuance and its brilliance. Brevity in software coding is revered by programming perfectionists. There is elegance in a well-written piece of code that executes beautifully, perfectly, every time. Anyone that can find literary melody in Shakespeare or Milton can certainly begin to enjoy the harmony in a beautifully crafted software application code. So, my appeal goes out to all those who are worshipping big data to take a moment to reflect upon the joy that software brings to our daily lives. After all, the future is software!

Author
Inder Davalur

Inderjith Davalur is a healthcare technology specialist, speaker, writer and utopian dreamer.
Inder works with hospitals committed to transforming the healthcare paradigm with the aid of new innovative technologies. His primary area of interest lies in using data analytics and technologies such as Deep Learning to shift the current physician-driven healthcare model to a patient-driven market dynamic.
Inder focuses on the manifold ways in which data crunching and machine learning can lead to better diagnoses that can not only be made at the time of illness, but predicted way before any symptoms surface. The path ahead in the sector, he believes, lies in the deployment of evolving technologies that immensely influence both diagnostic and therapeutic aspects of healthcare, delivering real patient-driven, data-enabled, informed healthcare.
Inder currently works as the Group CIO at KIMS Hospitals Private Limited, Hyderabad and has previously assumed leadership roles at leading hospitals and companies, in India and the United States of America.

Simplifying Health Economics by Dr. Karan Sharma

After hearing about India’s New Health Insurance Program, I thought it is good idea to share about Health Economics, so here I am


Health economics is a branch of economics concerned with issues related to efficiency, effectiveness, value and behavior in the production and consumption of health and healthcare. 


Alan William Plumbing Diagram about Health Economics
I am using Alan Williams “Plumbing Diagram” to comprehensively understand Healthcare Economics. He has divided scope of healthcare economics into eight distinct topics (explained in the documents) which are:
·        What is health and what is its value?
·        What influences health? (other than healthcare)
·        The demand for healthcare
·        The supply of healthcare
·        Micro-economic evaluation at treatment level
·        Market equilibrium
·        Evaluation at whole system level
·        Planning, budgeting and monitoring mechanisms.
There are interlinkages between each topic, which make it possible to see Health Economics as an integrated whole – more than an Ad-hoc assemblage of topics. According to understanding – The first five boxes
(A) Health and its values,
(B) Influencers to health,
(C) Demand for healthcare,
(D) Supply of healthcare and
(E) Market equilibrium factors are the analytical “Engine” of health economics.

The remaining three (F) Microeconomic evaluations, (G) Planning, budgeting and monitoring and (H) Evaluation of system are main area of Applied Economics. 

Let us understand each topic and its relationships:
CORE ENGINE
A.    Health 
Health can be defined as physical, mental, and social wellbeing, and as a resource for living a full life. It refers not only to the absence of disease, but the ability to recover and bounce back from illness and other problems.
Health generally evaluated through its value and perceived attributes, which are like:
1.     Productivity of individual healthy days
2.     Value of life
3.     Expenses caused by diseases and etc.
Health can be treated both as consumption and an investment good, Consumption: health makes people feel better, Investment: it increases the number of healthy days to work and to earn income.
Health does have characteristics that more conventional goods have; it can be manufactured; it is wanted and people are willing to pay for improvements in it; and it is scarce relative to people’s wants for it. It is less tangible than most other goods, cannot be traded and cannot be passed from one person to another, although obviously some diseases can.
B.     Influencers
According to WHO, many factors combine together to affect the health of individuals and communities. The few factors which affect health include:
1.     Income and social status – higher income and social status are linked to better health. The greater the gap between the richest and poorest people, the greater the differences in health.
2.     Education – low education levels are linked with poor health, more stress and lower self-confidence.
3.     Physical environment – safe water and clean air, healthy workplaces, safe houses, communities and roads all contribute to good health. Employment and working conditions – people in employment are healthier, particularly those who have more control over their working conditions
4.   Social support networks – greater support from families, friends and communities is linked to better health. Culture – customs and traditions, and the beliefs of the family and community all affect health.
5.     Genetics – inheritance plays a part in determining lifespan, healthiness and the likelihood of developing certain illnesses. Personal behavior and coping skills – balanced eating, keeping active, smoking, drinking, and how we deal with life’s stresses and challenges all affect health.
6.     Health services – access and use of services that prevent and treat disease influences health
7.     Gender – men and women suffer from different types of diseases at different ages.
There are evidences available of other examples which has been documented which are like: Transport, Food and Agriculture, Housing, Waste, Energy, Industry, Urbanization, Water, Radiation, Nutrition etc.
C.     Demand
Health demand is to achieve larger stock of Health Capital (healthy days). It is not passively purchased from market; it is produce in combining time with purchased medical inputs. Both value of Health and its influencers affect the demand. 
The demand for health is unlike most other goods because individuals allocate resources in order to both consume and produce health. There are four roles of person in health economics:
1.    Contributors
2.    Citizens
3.    Provider
4.    Consumers
 In the context of ordinary goods and services, economics distinguishes between a want, which is the desire to consume something, and effective demand, which is a want backed up by the willingness and ability to pay for it. It is effective demand that is the determinant of resource allocation in a market, rather than wants. But in the context of health care, the issue is more complicated than this, because many people believe that what matters in health care is neither wants nor demands, but needs. Health economists generally interpret a health care need as the capacity to benefit from it, thereby relating needs for health care to a need for health improvements. 
Not all wants are needs and vice versa. For example, a person may want nutrition supplements, even though these will not produce any health improvements for them; or they may not want a visit to the dentist even if it would improve their oral health.
Healthcare has its peculiarity that may mean, it is not considered as any good or service where demand can be analyzed, however that the usual assumptions about the resource allocation effects of markets do not hold meaning for healthcare. Moreover, it may well be that people wish resource allocation to be based on the demand for health or the need for health care, neither of which can be provided in a conventional market. 
D.    Supply
Supply is to achieve and fulfill the demand of health. The supply side of the market is analyzed in economics in two separate but related ways. One is related to the Resource input and Goods output model, looking at how resource use, costs and outputs are related to each other within a system.
Important influencing factors to supply are as follows:
1.     Cost of production of service
2.     Alternatives of services
3.     Substitutes of inputs
4.     Remuneration and incentives
5.     Medical equipment and pharmaceutical markets
Other way in which supply is analyzed is Market structure – how many firms are there supplying to a market and how do they behave with respect to setting prices and output and making profits. These generally managed through market equilibrium
E.     Market equilibrium 
State where economic forces like demand and supply balanced. For healthcare many believes, it is imperfectly competitive market (Nash Equilibrium) where there is strategic interdependence between two firms. The Nash equilibrium occurs when both firms are producing the outputs which maximize their own profit given the output of the other firm. The other side believes it is competitive market. Market equilibrium factors are as follows:
1.     Money (payer), investment etc.
2.     Price mechanism
3.     Time price factors
4.     Waiting list
APPLIED ECONOMICS
F.      Micro-economics evaluation
In simple words it is decision making related to allocation of resources. Major goal of microeconomics is to analyze the market mechanisms that establish relative prices among goods and services and allocate limited resources among alternative uses. It also analyzes market failure, where markets fail to produce efficient results. Few topics which would play important role in micro economics evaluation are:
1.     Cost effectiveness and cost benefit analysis of alternative treatment
2.     Cost utility analysis
3.     Opportunity costing
4.     Allocation based on phases of disease (Detection, diagnosing, treatment and after care)
5.     Market structure
Healthcare market typically which are analyzed are:
1.     Healthcare financing market
2.     Physician and Nurse services market
3.     Institutional service market
4.     Input factors market
5.     Professional education market
G.    Planning, Budgeting and Monitoring
Optimizing the system through effective instruments and tools, few are as follow:
1.     Budgeting
2.     Manpower allocation
3.     Regulation and norms
4.     Incentives structure
H.    Evaluation of system
It is to bring efficiency and equity to the system to bear on (E) Market equilibrium and (F) Micro economic factors through inter regional comparison, international comparison and benchmarking.
Efficiency – the allocation of scarce resources that maximizes the achievement of aims by Knapp.
Equity is always an important criterion for allocation of resources. However, it is observable that people attach more importance to equity in health and health care than they do to many other goods and services. It is important to distinguish equity from equality. Equity means fairness; in the health care context this means a fair distribution of health and health care between people and fairness in the burden of financing health care. Equality means an equal distribution, but it may not always be fair to be equal. 
Health economics has number of methodological limitations but it can offer us useful concepts and principles which help us think more clearly about the implications of resource decisions. An understanding of some basic economic principles is essential for all practitioners not only to understand the useful concepts the discipline can offer but to appreciate its limitations and shortcomings.
Wish to hear more from my connections on this…

The article was first published on Dr. Karan Sharma’s LinkedIn pulse page here, its been re-published here with the Author’s permission. 

Author
Karan Sharma

Healthcare Strategy and Customer Experience Manager, Technology Enthusiast, Innovator and Healthcare Business Leader.

Highly experienced and focused senior Executive with strong background in Healthcare strategies and business problem solving. Have managed multiple projects in different disciplines and geographies with strong track record of building great teams with exceptional results. Provide and Execute vision, strategies or idea.

He is a clinician and healthcare management professional, worked in India, Middle East and Maldives.

Some perceived shortfalls in the proposed Indian National Health Stack by Dr. Pramod Jacob

There is ongoing work in India for a Nationwide Information Technology platform, that will support and facilitate the deployment of the Ayushman Bharat program, which is called the “National Health Stack”, the objective of which is to help achieve Continuum of Care across Primary, Secondary and Tertiary care for each of its citizens and facilitate payment for the care.

A draft of the National Health Stack (NHS) strategy and approach was put out in July 2018 for feedback and comments till July 31, following which no final draft has been published in the public domain. Hence the shortfalls brought out in this write up are based on the July 2018 draft and so these are perceived shortfalls, because the final version may have addressed these concerns. If so, request that the final document be published in the public domain. http://niti.gov.in/writereaddata/files/document_publication/NHS-Strategy-and-Approach-Document-for-consultation.pdf  


There is  recognition for the need of holistic longitudinal individual electronic health records for citizens, rather than just collated population-based data, for which one of the key components in the NHS Stack is going to be the Federated Personal Health Record. But is this requirement of an individual’s record to ensure continuity of care or mainly to avoid fraud and bring greater trust into the claim handling process? If it is for the stated objective of fulfilling the National Health Policy 2017 that states 

“The attainment of the highest possible level of health and wellbeing for all at all ages, through a preventive and promotive health care orientation in all developmental policies, and universal access to good quality health care services without anyone having to face financial hardship as a consequence… “

Then, in this write up I focus on two issues of immediate concern.

1.  No requirement explicitly stated for compliance to Healthcare Information Technology (HIT)/EHR standards as recommended by MOHFW and published on December 2016 

2. Different applications being developed at various levels of care, both in the public and private healthcare domain, which are not proven to “talk” to each other i.e. exchange healthcare data (interoperability)

Going into greater details about each of these issues:

1. No requirement explicitly stated for compliance to Healthcare Information Technology (HIT)/EHR standards as recommended by MOHFW and published in December 2016, except for patient/beneficiary identification. 
https://www.nhp.gov.in/categories-for-adoption-of-standards_mtl

It is understandable that when the program starts – the focus is going to be on assembling the registries of beneficiaries, providers, empanelled hospitals etc and the claims or payment for healthcare services rendered by providers. For validating the claims there is going to be proof of services rendered to be provided by filling forms and uploading supporting documents, such as test results, into the claims component of the stack by the hospitals/providers. However, instead of just having a checklist format of proof of service, if the data input is coded compliant to recommended standards (such as SNOMED CT for Diagnosis or LOINC for lab results) instead of just free text or proprietary codes –– then the healthcare data being collated is of much more immense value for clinical study and analytics. More importantly, this would bring about the perception that the information being asked for and checked on, has value in providing in-sights to providing better healthcare, instead of being perceived as an overseeing billing validation into the services provided by the clinicians, and so will facilitate onboarding clinicians to digitization.  

For continuity of care and to facilitate quality clinical care, the assumption that having an open API based paradigm for fetching the records of a citizen from across different points of care, without the need for being standard compliant, maybe misplaced. Ok, so touch points will bring across data from corresponding associated fields when different healthcare systems exchange data, for example diagnosis from the exporting system into the importing system. However here lies the problem if not standard compliant, when attempting to consolidate the diagnosis section of a patient in a repository or into a consolidated longitudinal record: – say a patient has Pulmonary Tuberculosis and over time, goes to 3 different doctors in a few years. It is very possible that the first doctor may record the diagnosis as “Pulmonary Tuberculosis”, the next doctor may have logged in this diagnosis as “Tuberculosis of the Lung” and yet a third doctor may have put in the diagnosis as “Pulmonary TB”. So, when the data is being collated – the computer will not understand that all these three different terminologies represent the same concept and site of the disease, and may record them as separate problems. However, if the diagnosis was standard compliant and coded with the recommended SNOMED CT code (Concept ID 154283005), then the compilation and consolidation of this individual’s diagnosis list will be correct, since this standard code consolidates all three terminologies as the same disease and site. Similarly, lab tests results may have various terminologies, for example Fasting Blood Sugar aka Fasting Blood Glucose aka FBS, but if the recommended LOINC code (1558-6) is tagged, then during consolidation of a patient’s test results, the correct interpretation that these are results of the same test will occur and so will be trended accurately. This will come into play even at the claims phase. Healthcare is knowledge intensive, with whole lot of concepts, terminologies, semantics and nuances involved, which needs a framework of standards to convey the correct meaning and interpretation, when exchanging information between different HIT systems.

Another trend is that in those states that already have such universal health coverage programs deployed, there is a tendency to come up with proprietary codes for procedures in each of these different schemes, to suit the billing/claims end users. For example, Andhra Pradesh’s NTRVS program has got procedure codes like S5 for orthopaedics procedures, drilling down to S5.1 for fracture correction in orthopaedics procedures, further drilling down to S5.1.4 for reduction of compound fracture and external fixation. The same procedures have a different proprietary coding system in the program run by Tamil Nadu. So, what happens when you try to compare outcomes from the same procedures between these two states?  If the recommended SNOMED coding system for procedures was applied in both the states – then carrying out such comparative studies become much more feasible and meaningful. Instead of reinventing the wheel with proprietary or local codes, if the recommended international standards that have been developed over the years by domain experts are put into place, then not only can we carry out such studies between our states but also between India and other countries, leading to adoption of the most efficient, cost effective, least invasive interventions with best outcomes. 

It is of utmost importance that these recommended standards, including clinical standards, be introduced at the foundational phase of the framework for the National Health Stack. With about 20% more effort upfront, it is possible to plug in the look up databases for these standards into their respective fields- such as Diagnosis, Labs, Procedures, Medications etc. Even better, that these standards be deployed and utilized (where relevant) even for claims (as explained above), while place holders be put into place for those  standards (mainly clinical) that may come into play only when the Federated PHR phase is activated. Importantly, to enable exchange of data between HIT systems, it is highly advisable to be compliant to HIT messaging standards such as HL7/FHIR. That will be the only way that the National Health Stack will have the robustness and flexibility to handle billing, claims and clinical healthcare functionalities optimally. If this is not done at the foundational phase and if the NHS framework is mainly set up for billing and claims, this will straitjacket the framework to effectively introduce these standards later and lead to fitting a square peg into a round hole situation. Also, an even bigger problem that proprietary codes could lead to, is if down the line wisdom prevails, and a decision is made to mandate recommended HIT standards, then the big headache issue of retrospective mapping of these proprietary codes to standard codes comes up for existing patients with past visits/admissions. It should not be billing and claims requirements that be the primary driving force for the National Health Stack, but ideally should be patient care and provider requirements in conjunction with billing/claims requirements that should be the driving force. 

 2. Different applications being developed at various levels of care, both in the public and private healthcare domain, which are not proven to “talk” to each other i.e. exchange healthcare data (interoperability)
   
The NHS document states that the National Health Stack a. Is designed to bring a holistic view across multiple health verticals and enable rapid creation of diverse solutions in health b. To enable patients to effectively become a Healthcare Information Exchange (HIE) of one: as meaningful data accumulates in a patient controlled repository, a complete picture of the patient emerges, resulting in improved quality of care across a range of providers.

For the above stated objectives to be attained, it requires at least these two conditions to be fulfilled: –

a. The diverse HIT systems that are involved in healthcare of the beneficiaries should ” talk to each other ” with ability to exchange data appropriately and without loss of meaning and interpretation in the exchange i.e. Interoperability. That is how accurate meaningful data of a patient should be accumulated.  Considering that 70% of healthcare in India is provided by the private sector, this accumulation of a patient’s data will require visits/admissions to private hospitals to be brought in. For this, there is the most important requirement and need to publish the open APIs specifically being used in the NHS, so that these private healthcare organizations’ systems can integrate and exchange healthcare data with the NHS. 

For example, if I am an authorized doctor for a patient – what is the API to be used to fetch this patient’s healthcare longitudinal record  from the National Health Stack?  Again, if the recommended standard like HL7’s FHIR (which is API based) was adhered to for data exchange, it would have made this deployment, hooking up and integration with NHS much easier and effectively feasible.

b. For the data to be meaningful, classified and categorised correctly with terms implying the same concept put into the same category and not into different ones, need the variations in terminology (especially clinical terminologies) to map back to the correct concept as that intended by the provider – which requires the recommended HIT standards to be mandated. Only then can the healthcare data be meaningfully analysed, trends and patterns including outcomes be detected (by deploying statistical methodologies including machine learning and AI) and standard protocols with best outcomes for the various respective Indian ethnicities be formulated, thus achieving the stated goals and objectives of the NHS

If the National Health Stack does provide the latest and greatest in this  platform- with the recommended standards, then with our large numbers, English speaking brilliant human resources, internationally renowned prowess in Information Technology and Healthcare ; this assimilation  of a treasure trove of Healthcare Information, along with the  well-known Indian ingenuity, presents a huge opportunity for the country to leap frog healthcare to the next level and bring about betterment for humanity. 

Author

Dr Pramod D. Jacob (MBBS, MS- Medical Informatics)

After completing his medical degree from CMC Vellore and doing his Master of Science in Medical Informatics from Oregon Health Sciences University (OHSU) in the US, Dr Pramod worked in the EMR division of Epic Systems, USA and was the Clinical Systems Project Manager in Multnomah
County, Portland, Oregon. He went to do Healthcare IT consultancy work for states and counties in the US and India.

At present he is a Director and Chief Medical Officer of dWise Healthcare IT solutions. He was also a consultant for WHO India in the IDSP project and for PHFI for a Non Communicable Diseases Decision Support Application.

Universal Healthcare: How do we get there? by Ritesh Dogra @ritesh_medium

There is undoubtedly a clear argument for Universal healthcare. The question still looming large is “How do we get there”


Angus Deaton, a well renowned economist, explains that while there is a correlation between higher income and better life expectancy, this is not the only factor. There are means to ensure great health at less cost and equally spending large sum with no purpose, America being one case in point. While earlier any spending on healthcare was dubbed as social overhead, it is no longer so – there is enough evidence to prove that spending on healthcare speeds growth of the nation.

Today, the National Health Protection Scheme (NHPS) has been credited as the world’s largest health insurance plan. The plan aims to provide a health insurance cover of up to Rs 5 Lakh annually to 10 crore families, which would in turn cover 40 percent of country’s population. RSBY, the earlier predecessor of Ayushman Bharat was able to reach 3.6 Crore families over a 10-year timeframe against a targeted coverage of 6 Cr families, let’s say 60% success rate in 10 years. Undoubtedly, the scheme is very well intentioned and fundamentally ambitious which is the need of the hour. The scheme, however, currently seems to address only one of the three pillars – Affordability for healthcare services; two other pillars access and quality remain unanswered!

Do we have the infrastructure access? 


India has around 1.6 million hospital beds and around 55,000 hospitals (excluding community health centres and primary health centres). The infrastructure is woefully inadequate to cater to the healthcare needs of the country. In addition, there is a large variation across states. While states like Karnataka and Tamil Nadu have ~1000 people served by one government hospital, states like Bihar and Assam have more than 5000 people being served by a government hospital. Given this, how do we deliver care to the population remains a question. The gaps are even more pronounced across Tier-1/2/3/4 towns. However, the opportunity also presents solutions;

The government needs to smartly build capacity as utilization increases and also increase capacity utilization of existing Primary Health Centres (PHCs) and Community Health Centres (CHCs). However, there is a lot of ground to be covered; the current efforts are still geared towards building a registry of hospitals in Rohini (Registry of Hospitals in Network of Insurance) which finally claims to have ~33,000 unique hospitals.

Good primary care is an essential precondition for a healthy nation. And rightly so, Ayushman Bharat also proposes setting up of 1.5 lakh health and wellness centres across the country. These centres would provide comprehensive healthcare, maternal and child care, disease screening, free drugs and diagnostics to the poor. A meticulous implementation and robust healthcare delivery in these centres could reduce the need for secondary and tertiary care. Addressing problems associated with supply logistics and spurious medication is another challenge. There could be an opportunity to tie up with players involved in last mile logistics to tackle some of these challenges.

Finally, a large question that looms over is the participation from private sector. Can the government assure enough incentives to the private sector which already faces problems of receivable and collection from other government insurance schemes? Given that government hospitals have 0.5 beds per 1000 people, non-participation or even limited participation from private sector could adversely impact implementation.

Do we have skilled personnel? 

Our country has around 1 million doctors. While states like Karnataka and Tamil Nadu have 1.5 doctors per 1000 population, states like Bihar and Assam have less than 0.5 doctors per 1000 population. Apart from Physicians, contractual staff accounts for more than half of skilled workforce in the country.

Manpower optimization practices; creation of skilled manpower including nurses, technicians and other support staff through short term training courses could increase resource efficiency for doctors. Healthcare Sector Skill Council (HSSC) had already taken this initiative. However, it requires participation from some private players to jointly build the ecosystem. Certain practices such as midwifery which have been quite successful as isolated examples, need planning and mass implementation.


There are also sporadic examples and learnings from other countries. For instance, Costa Rica established integrated primary healthcare teams each looking after 5000 people. The team included paramedics to visit patients, an executive who maintains records, a nurse, pharmacist and finally a doctor. Ethiopia has a concept of health extension workers who are rural high school graduates undergoing one-year training before they are sent back to their native areas. These health extension workers have played a key role in reducing the child and maternal mortality by 32% and 38% respectively. In a review of studies conducted across some countries in Africa, it was found that clinical officers with three years of training performed Caesarean Sections as safely as doctors. In Thailand, there are incentives in place for doctors who work in rural areas. Inculcating some of these best practices should bring in much more efficiencies in the current system.

Do we care about quality? 

In India, the average length of doctor consultation is little more than 2 minutes and features a single question – “What’s wrong with you?”. Not surprisingly, research done by World Bank has shown that only 30% of the consultations have resulted in correct diagnosis. Citing another example, in India, around half a million children die of diarrhoeal diseases every year. In this context, a research done by the World bank around Diarrhoea in Delhi showed that only 25% of the providers ask parents whether there was blood or mucous in the child’s stool, which is the definitive symptom of the disease. Some of these are fundamental corrections needed in the healthcare quality today.

We have seldom talked about quality standards in existing public or private hospitals. A glance in the corridor of some of the best public hospitals across the country could send shivers down the spine. Is quality the least concern? While we have quality standards drafted by bodies such as NABH (National Accreditation Board for


Hospitals), compliance is altogether a different subject. In addition, less than one percent of hospitals have NABH accreditation.

Sometime back, Ministry of Health and Family Welfare launched an initiative Mera Aspataal (My Hospital) an app-based platform to enable patients share real time feedback on hospitals. The app has seen a meagre 5000 downloads and numerous complaints of inability to share feedback or non-actioned feedback. In addition, the website has numerous challenges right from accepting a mobile number for registration.

A large-scale quality and patient experience audit followed by implementation of drastic interventions is required to drive overall quality. There must be a commitment to deliver quality healthcare and not just on paper. Quality needs to be defined on multiple parameters and incentives need to be created around these quality standards. India would need standardized survey instrument and data collection methodologies to measure patients’ perspectives of hospital care. Hospitals providing quality as reflected in standardized patient scores need to be both recognized and incentivized appropriately. Practices such as HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) in the United States need to be studied and some best practises need to be suitably adapted to the Indian Context.

Is there a need to educate the consumer?

In order to drive healthcare consumption and changes in health seeking behaviour of the population, there is a need to educate the consumer. More importantly, the government needs to take a lead in facilitating patient education around insurance. This would also include educating them on seeking healthcare from the right set of institutions. The move would be much easier than educating informal physicians on right diagnosis and treatment. The government should take the lead in facilitating public health; focusing on awareness and education. Pulse polio campaign which witnessed a resounding success in India, needs to be created for Non-Communicable diseases in the country.

Increased penetration of both feature phones and smart phones could be another opportunity. In Kenya, for example, M-Tiba is a dedicated health account on cell phone that allows anyone to send, save and spend funds for medical treatment. In addition, it uses internationally recognized ‘safe care’ standards to monitor quality of care at approved facilities.

The way forward

The concept of Universal healthcare is not something new and has been embraced by quite a few countries across the globe while being a work in progress for others. In addition, it has helped them achieve desired results. Look at Rwanda, a small African country as an example, its GDP per person is only $750 but its healthcare scheme covers 90% of the population and infant mortality has halved in a decade.

The fulcrum of change is Niti Aayog and almost everyone in healthcare industry is keen to associate themselves with the program execution along with Niti Aayog; right from medical device and pharma firms, health tech platforms and consulting firms,


however what the program needs is a clear thinking and internally designed implementation roadmap.

Ayushman Bharat, undoubtedly, could be a game changer in the Indian context if planned meticulously and implemented well. Amitabh Kant, Niti Aayog CEO, expects around 50% of the families to receive coverage in the first year. As per him. “the challenge is not resources for the scheme, but challenge is its implementation”. The goal of Universal Healthcare is certainly achievable and affordable by the government; it needs a thinking on how to optimally use scarce resources!

The healthcare SIG  is planning a panel discussion and networking event at Equinox on this theme. Please reach ritesh_dogra2009@pgp.isb.edu if you wish to collaborate for the same.

References

1. On Death and Money – History, Facts and Explanations – Angus Deaton

2. Census of India – Annual Health Survey Bulletins

3. Government of India Ministry of Finance – Ayushman Bharat for a New India -2022

4. Medium Healthcare Consulting Analytics

This article has been written by Ritesh Dogra, alumnus from PGP Co ’09, Moderator of the Alumni Healthcare Special Interest Group(SIG) & Managing Partner, Medium Healthcare Consulting. The article was first published here, and has been re-published on the blog with the author’s permission. The images in the article body have been sourced from the original article.

Healthcare Conference
Register for the 6th Annual Conference by Medium Consulting, Sep 28th 2018,  at Hyderabad: 
http://www.amchamindia.com/healthcareconference2018/
Author
Ritesh Dogra

Ritesh has been a member of the Founding Team at Medium Healthcare Consulting. He has led a number of engagements in areas as diverse as market expansion strategy for a Fortune 500 medical equipment manufacturer to planning and commissioning of novel healthcare concepts to performance transformation of a leading hospital chains in South and East India. He has received numerous accolades from clients for his rare insights and extraordinary commitment.

Timeline: The History of the EMR/EHR by David Rice @bigdatadavid13


Much of the conversation around healthcare technology is centered on where new developments are taking us. But as the age old adage goes, you can’t know where you’re going until you know where you’ve been.
And when it comes to health IT, few innovations have been more significant or played more of a central role in innovation, than the digitization of medical information in the form of the electronic medical record (EMR) and later, the electronic health record (EHR). To better illustrate the history behind this technology, we’ve created the following timeline to provide you with some context behind the evolution of this technology.


The article was first published here. The article has been republished here with the authors’ permission. 
Author
David Rice

David Rice is the editor of USF Health Online. He covers a wide range of health IT related topics for the University of South Florida’s online informatics and healthcare analytics programs.

A PhD Researcher’s QnA on #BigDataAnalytics (BDA) with a Healthcare CIO by Inder Davalur, @INDERDAVALUR & Nishita Mehta


Q1. Nishita Mehta: What is data’s role in healthcare & how do you see it influencing future health sector growth in India?

A. Inder Davalur: 

Big Data Analytics (BDA) will have a huge role in healthcare. Healthcare has been a latecomer to using IT as a tool but the future looks good. AI and its children – ML, IoT, and M2M are excellent candidates for advancing technology in healthcare. There is a real potential for technology to advance what I have termed “Connected Continuum of Care” in one of my blogs. This means that with wearables and other Internet of Healthcare Things (IoHT), creating a biome where the patient and doctor/hospital are always connected would become a reality. Always-on Internet is the future and extending that to healthcare is a natural progression. With the price of Internet in India being one of the lowest in the world, we will be in an excellent position to incorporate technology in advancing healthcare delivery.   


Q2. Nishita Mehta: What are the unique challenges of working with clinical data? 

A. Inder Davalur: 

Doctors. Well, the challenge lies with the fact that most clinical data is unstructured. Doctors and hospitals are notorious for NOT wanting to follow standards when it comes to coding diagnoses and treatment. Adoption of DSM, Snomed, ICD codes is very spotty. Physicians complain about the inconvenience in the classifications and prefer to use free text in writing their diagnoses and treatment protocols. This creates a credibility gap in how clinical data can be meaningfully classified and analyzed for any useful prediction or AI driven protocols. EMR applications in India struggle with the similar challenges with physicians disagreeing on a set of standards in capturing and documenting clinical data. 

Q3. Nishita Mehta: Healthcare seems to be moving from the use of structured data to unstructured data. What is the difference between them when it comes to clinical utility & improving patient outcomes?

A. Inder Davalur:  

Healthcare has always suffered from a lack of structured data. Unstructured data creates several challenges in a software trying to classify the same diagnosis written with slight variations. The same fate awaits treatment plans. If medical coding (DSM, ICD etc.) is followed, it will enable any analytics software to make sense of the data and provide useful insights. With AI, structured data is still king. Predictability of an outcome for a set of patient symptoms, medications, prior history, genetic propensity, lifestyle habits would have a high accuracy 

Q4. Nishita Mehta:What do you think does a hospital need to implement Big Data solutions, i.e. Big Data Analytics Capabilities in terms of infrastructure and personnel requirement?

A. Inder Davalur:  

One of the major challenges a CIO or an IT head faces in a hospital is the lack of budget allocation for anything beyond the basic networking, computing and storage needs. Hospitals do not see the value in the data they currently possess most likely because they are more empirically driven rather than evidence driven. What this means is that hospitals and by nature the doctors who hold a sway over management decisions are more comfortable with their own decades of experience over some hotshot CIO trying to promote the idea of data mining and predictive value of patient outcomes based on past data. There is also a severe shortage of technology-rich personnel in hospitals due to the dull routine of maintenance of existing hardware and software rather than experimenting with new technology. The pay structure for IT personnel in hospitals is also woefully poor in comparison to the technology companies. All these factors combine to deter anyone who is driven to create in hospitals a digital core

Q5. Nishita Mehta: While Big Data can generate a plethora of interesting patterns or hypotheses, there is still a need of experts to analyze the results to confirm whether they make sense or merit further inquiry. Would you like to comment on this?

A. Inder Davalur:  

Absolutely. Right now, there is a paucity of people with skills to interpret and recommend action plans once an organization implements any sort of analytics software. Unlike other verticals, healthcare is lagging far behind in its focus on data interpretation and application in its business model. It might be a whole decade before hospitals wake up to the reality of meaningful interpretation of data and building an action plan around it


Q6. Nishita Mehta: What are the major drivers of Big Data Analytics in healthcare in India?

A. Inder Davalur:  

Have not seen much evidence of it. Perhaps some hospitals have ventured into some basic AI driven applications in specific areas such as pharmacy sales or patient wait times. Other than that, BDA is yet to catch up.

Q7. Nishita Mehta: What are the key benefits Indian hospitals will draw from implementation of Big Data Analytics? 

A. Inder Davalur:  

First and foremost, hospitals will get to see for themselves how poorly structured their data is. BDA for Indian hospitals can cover a better management of the following: 
  1. Sponsored
  2. Accounts Receivables
  3. Professional Fees
  4. Disposables and Consumables
  5. Pharmacy – Generic vs brand name consumption
  6. Targeted marketing
  7. Continuum of Care post-discharge
  8. Predictability of illness propensity from regular Health Check ups
  9. Results-oriented tasking for better output from employees

Besides these areas, hospitals can contribute tremendously to public health issues by sharing anonymized patient data with the State Health Department which can then study outbreaks and lifestyle disease patters in the general public. 

Q8. Nishita Mehta: How does Big Data Analytics help better decision-making & building disease understanding?

A. Inder Davalur:  

One of the most ignored areas is a deeper dive into results from investigations. Empirically speaking, the values considered “normal range” are never questioned. If a better study is conducted, what is normal for one cohort may not be so for another cohort. As an academic exercise, I had a simple deep analysis done to study the correlation between borderline values of lipid profile and any other element from a blood test. The result was a high (>70%) correlation between borderline lipid profile values and an elevated monocytes count. It turned out that among those who fell in this group, nearly 78% of them were later admitted for some coronary complication. The medical reason is that the monocyte levels are elevated when there is presence of a heart disease. Every one of these patient was merely getting a Health Check. Imagine if hospitals did such studies on a multitude of investigations routinely conducted for patients and conducted regular follow ups as a preventive measure

Q9. Nishita Mehta: One of the biggest concerns in healthcare is the rising costs. What potential solutions does Big Data offer for this problem in Indian context?

A. Inder Davalur:  

India’s population is now facing more mortalities from lifestyle diseases – Non Communicable Diseases (NCD) as opposed to communicable diseases. There is a great potential to flip the business model of the healthcare industry to go from disease management to health management. I have written blogs on this topic. The premise is very simple. Make it more profitable for hospitals to keep the public healthy than to treat them. If the payment structure is modified to increase the prices for health checkups and promoting healthy prophylactic therapy methods as opposed to coronary by-pass surgeries, it could completely change the paradigm. These prices can be graded based on age. All old age related treatments can receive higher prices; while treatments like a heart surgery for a 40-year old can be less. At the same time, therapeutic treatments for younger population geared for promoting good health can receive higher prices. A larger healthy population means a larger market for the hospitals. This ensures that the hospitals have a higher incentive to make the healthy population larger

Q10. Nishita Mehta: What would you highlight as being the major challenges today in developing & actually implementing Big Data Analytics capabilities to truly extract meaningful insights?

A. Inder Davalur:  

An urgent awareness creation among promoters and owners of hospitals of the benefits of investing in the technical hardware and personnel resources to build and maintain a BDA infrastructure. Without that awareness, IT costs are always seen as a sunken wasteful expenditure as opposed to an investment. There is nothing else lacking in this respect.

Q11. Nishita Mehta: Do most doctors now have a checklist for what they should be doing with patients with certain conditions? How does Big Data solution change what they are doing currently?

A. Inder Davalur:  

Hard to predict. Most clinical pathways and treatment protocols are traditionally empirically driven. It is hard to imagine a medical community to take notice of what BDA might reveal and radically change their protocols. That said, things have changed – take robotic surgery – and there is hope and a high degree of probability that medicine might be “data-powered” (my phrase over the more commonly used data-driven) offering the physician to choose to use such data-powered results wherever she finds it viable or desirables

Q12. Nishita Mehta: How do hospitals need to adapt to embrace the full potential of data-driven innovation?

A. Inder Davalur:  

Promoters and owners having a greater understanding of the power of data

Q13. Nishita Mehta: How important do you think Big Data Management & Analytics is right now to enhance healthcare in India?

A. Inder Davalur:  

Tremendously. With the technical resources at its disposal, India would be imprudent not to take full advantage of the benefits of BDA. Population health data is one of the most ignored among developing nations. India would do extremely well to develop and use BDA for advancing population health

Q14. Nishita Mehta: What do you see as the main emerging opportunities for hospitals from greater adoption of Big Data Analytics?

A. Inder Davalur:  

Connected Continuum of Care (a phrase I first used in a blog) is a concept of keeping the patient engaged post treatment and post discharge through the use of wearables and IoHTs (Internet of Healthcare Things). This ensures that hospitals are not merely agents in episodic encounters and instead become agents of well-being. BDA will help provide the big picture in the overall health and well-being of the population it serves

Q15. Nishita Mehta: What are some of the biggest challenges facing the healthcare industry in terms of its ability to use Big Data to improve healthcare outcomes?

A. Inder Davalur:  

A better understanding and incentive to invest in the infrastructure is all it takes. Once that happens, India is best equipped to leverage from its large technology-aware population. At the hospital level, BDA could help establish a new approach to purely outcomes-driven pricing structure and treatment protocols that would be data-powered. 

Q16. Nishita Mehta: Would you like to share additional insights on the topic, which I might have missed?

A. Inder Davalur:  

Public-Private-Partnerships with educational institutions and hospitals would also be beneficial. There is going to be a severe shortage of technical resources who are trained in AI and BDA by 2020. If the government partnered with colleges to promote courses and training in AI and BDA India could be the largest supplier of technical talent to the world. If hospitals also partnered with the government to share health data, the state of overall population health will rise and costs will come down.

The article was first published on Mr. Inder Davalur’s LinkedIn Pulse page. The blog was Mr. Inder’s answers to Ms. Nishita Mehta’s Survey published on the HCITExpert Blog earlier, here. I would like to thank both the Author’s for sharing their insights via the HCITExperts Blog. 
Team @HCITExperts [Updated: 03 rd Sep 2018]
Authors
Nishita Mehta

Ph.D. Scholar at SYMBIOSIS INTERNATIONAL UNIVERSITY

Inder Davalur

Inderjith Davalur is a healthcare technology specialist, speaker, writer and utopian dreamer.
Inder works with hospitals committed to transforming the healthcare paradigm with the aid of new innovative technologies. His primary area of interest lies in using data analytics and technologies such as Deep Learning to shift the current physician-driven healthcare model to a patient-driven market dynamic.
Inder focuses on the manifold ways in which data crunching and machine learning can lead to better diagnoses that can not only be made at the time of illness, but predicted way before any symptoms surface. The path ahead in the sector, he believes, lies in the deployment of evolving technologies that immensely influence both diagnostic and therapeutic aspects of healthcare, delivering real patient-driven, data-enabled, informed healthcare.
Inder currently works as the Group CIO at KIMS Hospitals Private Limited, Hyderabad and has previously assumed leadership roles at leading hospitals and companies, in India and the United States of America.

Glossary of terms and acronyms for #Blockchain and #Cryptocurrency


Blockchain: The foundational technology behind the blockchain and cryptocurrency sector. It is a virtual, immutable (unchangeable), distributed store of data stored on servers around the world. This is a new way of distributing both trust and data. It is an alternative to traditional systems where a central organization holds all the data.

Think of it as a chain of blocks of data, verified by consensus by any computer that chooses to participate. Each block of data containing anything from who has sent cryptocurrency to others to who owns what plot of land in a land registry.
Blockchain is a distributed ledger.
Block: A package of data containing multiple transactions over a given period of time.
Chain: The cryptographic link that keeps blocks together using a ‘hash’ function.
Distributed ledger: This is an analogy often made about blockchains. Instead of a centralized bank ledger, blockchains offer the promise of distributing balances throughout a network of computer servers.
You aren’t going to a single bank to store where you send your value — instead, you are going to a decentralized network of peers.
Distributed ledgers aren’t a new concept: the island of Yap used individual tables as early as 500 AD. They yelled at one another whenever they made a new transaction. Blockchains and cryptocurrencies offer the global, virtual network equivalent of that system.
Cryptocurrency: A token or currency built on top of blockchain technology. This token helps capture and distribute value from users of the blockchain. You can think of Bitcoin as the first application and cryptocurrency stemming from the blockchain. Cryptocurrencies are a subset of what are known as cryptoassets. 
Tokens: The means of exchange to give value to a transaction; typically a native cryptocurrency. Some non-currency blockchain architectures can be tokenless.
Cryptocurrency Exchange: Cryptocurrency exchanges are websites or services that let you exchange digital cryptoassets and cryptocurrencies between one another or exchange fiat currencies such as the US dollar for cryptoassets. Two of the most prominent examples of these exchanges are Coinbase and Binance.
Public/Private Keys: Keys are your way to access crypto balances and to send and receive value or data in cryptocurrency. Your public key is like your email address. It’s what allows other people to send you funds. You can share your public key with the general public.
Services like Etherscan can scan account balances and transactions associated with a public key.
Private keys are the password to your email account. Anybody who holds the private key to a wallet can access and control it and spend any tokens within it. It’s a unique string of data that represents proof of identification within the blockchain, including the right to access and own that participant’s wallet within a cryptocurrency. It must be kept secret: it is effectively a personal password
A unique string of data that identifies a participant within the blockchain. It can be shared publicly.
If you lose your private key or forget it, you’ll lose control of all the crypto assets tied to that private key/public key combination.
Cryptocurrency Wallets: Cryptocurrency wallets are ways of storing your private and public keys to your cryptoassets. A wallet is a safe you can access to then get your keys.
Wallets allow for easier access and backups if you don’t remember your private key with techniques such as the mnemonic seed phrase, a series of 25 random words you have to input to get access to your private key.
There are software wallets and hardware wallets: software wallets store your keys online, while hardware wallets use a physical device such as the Trezor to protect your private key.
MultiSig: MultiSig is a permissions system for crypto wallets. The majority of cryptocurrency wallets are single-signature. This means you only need one person’s private key to control the balance within it.
MultiSig means you need more than one private key to spend funds. This allows you to set up an M-of-M scheme. As an example, you might need 5 out of 9 signers to approve of a transaction for it to go through. This is useful for corporate wallets, where many owners and employees have to approve before a transaction is sent.
Platforms like BitGo and Xapo provide MultiSig wallets for their users.
Proof-of-work: A system where blocks of transaction data on the blockchain are mined and validated by specialized computers who earn a reward for solving specific math equations. Repeatedly running a hash function, the mechanism by which data miners win the right to add blocks to a bitcoin-style blockchain.
Mining: A practice in proof-of-work systems where computers are dedicated to solving math problems in order to claim the right to mine a block of data and to get an amount of cryptocurrency.
How it works in some more detail: the cryptographic mining piece involves solving cryptographic puzzles. A computer needs to find a nonce to combine with unverified transactions to output a verified string.
Data Mining:  The process of solving cryptographic problems using computer hardware to add newly hashed blocks to a public blockchain such as bitcoin. In fulfilling this function, successful data miners keep the blockchain actively recording transactions and, as an incentive, are awarded newly minted bitcoins for their trouble.
Mining poolA mining pool aggregates computing resources dedicated to mining cryptocurrencies and allocates any of the mined blocks proportionally. In practice, mining cryptocurrencies has some randomness to it, so mining pools serve an essential purpose in keeping volatility down for individual miners.
Proof-of-stake: Proof-of-stake pushes people who own a selection of a blockchain’s tokens to make decisions on validating the chain. In practice, it’s a much less energy-intensive practice than mining.
Resource to read: Proof-of-stake – Wikipedia
Node: Any computing server around the world can run as a cryptocurrency node, which can store a copy of the blockchain and serve to verify transactions.
Hash The result of applying an algorithmic function to data in order to convert them into a random string of numbers and letters. This acts as a digital fingerprint of that data, allowing it to be locked in place within the blockchain.
Hash Rate: A measure of the computing power dedicated to any blockchain by the miners validating transactions and blocks. The higher the hash rate, the more active the chain is and the more appealing it is to miners. It then becomes harder to attack the chain, and infiltrate it with false transactions (known as a 51% attack).  
Decentralization: A measure of how much authority is held by a central holder. You can argue that blockchains are naturally more decentralized than other methods of distributing data because there is (at least in public chains) no gatekeeper on who can join: as long as you have the computing power, you can participate in the blockchain.
Instead of all of your data residing in one central provider (ex: Equifax), it now sits and is processed and verified by a global network of computers.
Decentralization is an ideal of the blockchain community. However, it has not been perfectly achieved.
For example, the mining pools that mine most of Bitcoin are mostly based in China: a consortium of these mining pools might decide to do what is called a 51% attack. They would use their assembled computing power to change the rules of the blockchain and facilitate conditions such as “double spend”: the ability to infinitely spend the same block of cryptocurrencies, essentially creating wealth out of nothing. The control of mining resources is very centralized.
Public vs. Private Chains: There are blockchains open entirely to the public (anybody can participate) such as Bitcoin and Ethereum. There are also private blockchains that have gatekeepers who determine who can join.
Private blockchainA closely controlled network operated by consortia in which the data is confidential and is accessed only by trusted members. Private blockchains do not require a token.
Interoperability: Blockchains and cryptocurrencies are often isolated with one another, and need to be exchanged in order to be used.  
Blockchains like Aion are looking to solve the interoperability piece by making different blockchains and cryptocurrencies interoperable, or compatible with one another: imagine, for example, a world where you can trade Bitcoin and Ethereum seamlessly (without exchanges) and use them interchangeably.
Atomic Swaps: Atomic swaps involve cryptocurrencies that are tradeable with one another without needing an exchange in the middle. Typically, they have to follow the same encryption standard and have a payment channel protocol such as Lightning Network. With what’s called a hash-time locked smart contract, two individuals can trustlessly trade cryptocurrency pairs with one another: solving the interoperability piece.  
Hash functions/tables: A more technical and precise description of the underlying technical foundation of how data is shared and stored on a blockchain. Hash tables are a mainstay of computer science.
Bitcoin: Bitcoin was created by Satoshi Nakamoto in 2008 as the first application of the blockchain and as the first cryptocurrency. It is still the dominant cryptocurrency now.
Fork: There are soft forks, where a cryptocurrency maintains its value and its rules are simply rolled forward and changed in a reversible manner, usually with the assent of the majority of the community.
Hard forks are when a blockchain fails to reach consensus and has to do a hard reset and splits off into two chains. One chain adopts one set of rules and another continues the original set of rules. This is non-reversible. A hard fork is how Bitcoin and Bitcoin Cash split.
SegWit: Segmented Witness (or SegWit) is a soft fork that happened with the Bitcoin blockchain. It solved congestion on the network by increasing the blockchain’s block size limit and splitting blocks of data in two. It separated out the unlocking signature with the scripts that send and receive data with the transactional data.
This allows the network to process more transactions per second. Users don’t have to wait as long for bitcoin transactions.
Resource to read: Segregated Witness, Part 1  
Lightning Network: Lightning Network is an off-chain solution that can settle transactions without having to use the underlying blockchain. It opens up bidirectional payment channels between different individuals, allowing Bitcoin to process many more transactions per second.
Payment channels have pre-deposited amounts of crypto placed into them. They allow individuals with channels open between them to transact seamlessly without using the blockchain. Once you get a final balance, it is validated into the blockchain.
This allows for many more payments to be done per second. It also means there is some centralization between large payers.
Resource to read: Lightning Network
Schnorr: A large Bitcoin update brewing as of the time of drafting for this article, Schnorr proposes to give users a new way to generate the private and public keys critical to cryptocurrencies. It replaces the Elliptic Curve technique currently used to generate keys with the Schnorr technique.
This update increases both privacy and security by grouping together MultiSig and regular transactions in the same category, allowing the blockchain process to more transactions and hiding whether or not a transaction is MultiSig or not.
Ethereum: The blockchain behind the second largest cryptocurrency. Ethereum differentiates itself from Bitcoin by allowing programmers to build on top of the blockchain with a Turing-complete programming language. This allows programmers to build distributed applications.
While Bitcoin can be seen as one application (transfer of value) on the distributed web just like email, Ethereum is a network that allows for many different applications to come to the fore.  
The cryptocurrency associated with the Ethereum blockchain is known as Ether.
Decentralized Apps (DApps): A decentralized application is a specific type of app that serves a specific purpose within a blockchain network. It must be open-source, autonomous, and it must make changes to the underlying software via consensus from its users. It must store all its data on a public blockchain, which is auditable by the public, and it must generate tokens and be accessible via those same tokens.
DApps seem like regular web applications. Client-side, the same mechanism is in play, but server-side (or the back-end), data and control are distributed among a network of P2P (peer-to-peer) nodes and smart contracts rather than a centralized set of servers and server code.
Resource to read: DApps – Flipside Crypto
Smart Contracts:  Smart contracts refer to code that is placed on a blockchain and is then executed on it. The code can be audited by the public. Smart contracts are often regarded as a compliment or a replacement to traditional legal contracts.
A smart contract might algorithmically implement escrow payments without having the need to create a binding legal contract to hold parties accountable.
However, the term is often seen as overly broad as it can mean any block of code placed on the blockchain.
Gas: Gas is used as a transactional cost in the Ethereum blockchain. When you use Ether to access distributed applications, you have to spend a portion of gas associated with it. Gas is correlated with how much computational work your request takes. This ensures that transactional costs are rightly set for the amount of work the system needs to do.
Gas is a way to ensure that nobody tries to attack the Ethereum network by filling it with invalid requests.
Solidity: Solidity is currently the most popular programming language to write smart contracts on the Ethereum blockchain, based around EMCAScript (the basis of JavaScript).
DAO: DAOs or decentralized autonomous organizations are a collective grouping in which smart contracts make choices. The entire organization is run on the blockchain. Shareholders buy tokens that give them the right to vote on future decisions.
Resource to read: DAO – Flipside Crypto
Casper:  Casper is an implementation of the Ethereum blockchain that promises to process more transactions per second. Ethereum used to be able to process 20 transactions a second. Bitcoin could only process 4. Visa and Mastercard can process about 20000 transactions a second. Casper is an in-between step for the Ethereum blockchain to change over from proof-of-work to proof-of-stake. It implements sharding (dividing the main Ethereum chain into smaller subcomponent chains) to provide parallel processing and increased throughput.
ERC20: A set of standards based on the Ethereum blockchain. ERC20 allows anybody to create a token built on top of Ethereum’s blockchain. It is the basis of the initial coin offering craze and the advent of new “altcoins”.
Altcoins: Altcoins are tokens, cryptocurrencies and cryptoassets outside of Bitcoin and Ethereum. Coinmarketcap gives you a good view of how many there are!
Stablecoins: Stablecoins are cryptoassets pegged to a certain value or asset — for example, you have stablecoins that trade 1:1 with the US dollar. These are collateralized or not with other cryptoassets.
Initial Coin Offering: Another way to originate tokens for a blockchain. An ICO involves a marketing process, private sale, then a public sale of a newly-listed token, which then aims to be listed on as many cryptocurrency exchanges as possible. Note that there is no standard way of conducting initial coin offerings.
Hyperledger An umbrella project set up by the Linux Foundation comprising various tools and systems for building open-source blockchains.
Oracle: A bridge from a blockchain to an external data source that allows a smart contract to complete its business by referencing timely real-world information. An oracle might allow a smart contract to access consumer energy usage, live train timetables, election results, and so on.
Peer-to-peer (P2P)The direct sharing of data between nodes on a network, as opposed to via a central server. 
Permissioned ledgerA large, distributed network using a native token, with access restricted to those with specific roles.
Proof of stake The mechanism by which participants earn the right to add new blocks and so earn new tokens, based on how much of that currency they already hold.
Public blockchainA large distributed network using a native token (such as bitcoin), open to everyone to participate and maintain.

References: 

[1]: A definitive glossary of blockchain and cryptocurrency terms: 
https://thenextweb.com/contributors/2018/08/21/a-definitive-glossary-of-blockchain-and-cryptocurrency-terms/

[2]: Flipside Crypto’s guide to blockchain and cryptocurrencies:  https://flipsidecrypto.com/wealthadvisor-ebook/
Team @HCITExperts [Updated: 1st Sep 2018]
Author
Team HCITExperts

Your partner in Digital Health Transformation using innovative and insightful ideas

How Mobile Will Transform Primary Healthcare Access in India by Prasad Kompalli, @pkompalli ( mfine @mfinecare )


A few days ago, we came across a very interesting albeit a rare case where a mother wanted to consult a paediatrician. Under a few minutes, she was able to have an online consultation with one of the top paediatricians in Bangalore, who immediately prescribed the required treatment for her child as the  symptoms were severe. At this point the patient informed the doctor that she was on a moving train and travelling towards Bangalore but needed the assistance urgently and was glad to have spoken to him. The doctor meanwhile was totally taken aback. Quickly recovering, he felt a deep appreciation for technology and its ability to empower people and help them access essential services at the hour of need.

More than 330 million smartphone users and rapidly falling bandwidth prices are redefining how essential services are delivered in India. There would be 900 million smartphone subscribers by 2023 and smartphone traffic is expected to grow 11 times to 14 EB in another five years*. India also has one of the lowest Internet data rates in the world, as low as 10 INR (approx 6 U.S.cents) /1GB/day. While much has been talked about India as an ecommerce market, adoption of mobile internet for foundational services like education, healthcare and financial services and across different consumer segments is something new and fast catching up. 

India has one doctor for every 1700 people, and if one considers only post-graduates, this ratio is pretty dismal at one doctor for every 5000 people. Access to health services in India is highly inequitable, translating into major disparities in health outcomes along demographic lines. There are 1000 primary healthcare cases & about 100 secondary healthcare cases reported in a hospital everyday and this number is rising.

Spread of chronic and lifestyle diseases is growing at an alarming pace and much of it will end up in tertiary care if not managed and intervened at appropriate time. Focussing on primary and secondary care is important in our country as it pushes a prevention mindset and helps people manage their health spends better. However, we can not depend on physical channels alone for primary care delivery as it is very expensive and also slow to scale. Physical infrastructure both in cities and in rural areas is proving to be an impediment and isn’t able to catch up fast enough, adding to massive delays in patient getting to the point of care. 

Technology is the only viable solution to be able to cope with low doctor-patient ratio, predominantly out-of-pocket spending and inaccessibility of quality care.

We are going to see India leapfrog the methods of healthcare delivery that were adopted in the developed nations, and mobile will be at the centre of this disruption. Let us look at some of the areas where mobile will transform the delivery of primary and secondary healthcare. 

Speed means Quality:  Because in healthcare, early detection and timely intervention helps in avoiding  further complications, prevents additional issues and reduces the use of powerful and/or too many drugs. Cases of patients visiting a doctor after symptoms have worsened and the consequent use of antibiotics is becoming all too common in India. Another important case where speed matters is in the case of  viral epidemics where lack of timely information and intervention leads it to spread like a wildfire.  

Connected care is the right care: We recently saw an example of an elderly woman, diagnosed to have dengue, recovering completely without stepping out of her home. She was able to do so by being continuously connected to the doctor and her treatment being monitored remotely. The current system of care delivery doesn’t leverage the connectivity that’s available to everyone through mobile. Quick reminders for patients to take their  medicines on time, checking with them on how they are recovering from illness and the provider’s continuous vigilance, particularly in chronic conditions and situations like pregnancy and child growth are all possible in this age of always-on connectivity. Such proactive care makes the doctors and the providers  more effective, accessible and reliable. 

More personal Point of Care: Mobile will help reaching the doctor without going to the doctor. From answering to health queries,  providing  serious consultations and enabling long term care, the care starts and sustains through this little device in our hand. The power of this phenomenon is immense. The discreteness and the personalization one needs in healthcare is now possible and caters to all kinds of people and their varied requirements. A couple planning for a baby, a busy working professional, a  young parent or someone taking care of dependents –  we all can attend to our life and to our health with equal priority. When the doctors visit becomes a matter of firing up the app, and taking out few minutes, we will do a much better job of taking care of our health. 

The future is a system where healthcare provided by all the trusted institutions around us  is on-demand and easy to access. It will be exciting to see how mobile will truly empower each one of us to take care of our health.  

*according to latest annual report by the Cellular Operators Association of India (COAI)


 Author

Prasad Kompalli

Entrepreneur. Business leader. Health-tech evangelist.

These are just a few words to describe Prasad. Best known for his leadership role at Myntra, India’s largest e-commerce store for fashion and lifestyle products, Prasad has built and lead large teams from ground-up, in a career spanning more than 20 years.

As the Chief Business Officer of Myntra, Prasad led its growth from being a small ecommerce company to become the largest fashion destination in India, with a revenue nearing 1B. He was responsible for driving Myntra’s business across Category Management, Marketing, Fashion and Revenue. At Myntra, Prasad is credited for bringing several international brands to India, and creating a lasting impact in the industry by driving strategic partnerships and M&As. Prasad is also known to have lead Myntra through several technical innovations such as being mobile first, and creating opportunities at the intersection of data science and fashion. He has been instrumental in driving value creation through deep tech integration with partners in the fashion industry.

Prior to Myntra, Prasad had a long stint at SAP where he was leading an international team of 600 engineers developing key technology products in the areas of mobility, business process management and EAI. He was one of the top 200 global leaders at SAP where he held strategy and general management roles. During his time at SAP, he created several product innovations and has got eight patents under data and mobile technology.

mfine is Prasad’s second entrepreneurial venture, prior to which, he was the cofounder of Indus Bionics – an ambitious attempt to build indigenous low-cost cochlear implants.

Prasad is a big believer in tech-lead transformation of societies and strives to create positive impact for consumers, especially in India, with technology. He sees an unprecedented opportunity with mobile tech and AI to create a high quality healthcare experience that is personal and accessible on-demand. With mfine, Prasad wants to shape the new-age healthcare delivery in India.

Prasad also holds 7 patents in data and mobile technology.

I & L to #AI & #ML in Healthcare by Jyoti Sahai, @jyotisahai

Have you ever wondered why if confronted with any illness symptoms that appear even a bit abnormal, we prefer to consult with a doctor in a large hospital only, even though a more competent doctor may have a clinic next door itself.

And have you ever wondered that what that preference has to do with Artificial Intelligence (AI) and Machine Learning (ML)!
To explain that, let me recount what happened to me twenty-one years back. I vividly remember that incident from 1997 that I can now relate well to the significance AI and ML are having in healthcare currently!

SYMPTOM

I was being examined by a leading physician at Agra (who had an experience of over twenty-five years and had a roaring practice) for a pain near my left toe. The conversation progressed as follows:
I: Doc Sahib, please have a look at my left toe. I am troubled by a severe pain for over last three weeks. I cannot put my foot down or wear the shoes even.
Doctor: Did you ever have this type of pain before?
I: No
Doctor: (Examining the pain area closely) Do you feel any irritation, or feel any urge to scratch that area?
I: No.
Doctor: Do you eat lot of red meat?
I: No! I am a vegetarian.
Doctor: Do you like to eat lot of tomatoes, or cheese, or spinach or any other high protein foods.
I: Yes. Very frequently have cheese-spread, and baked beans in breakfast, and of course tomato in some form is generally there in all meals.
Doctor: (Prepares a slip for the diagnostic lab) Please have the uric acid blood test done as I suspect you have gout.
I: Thanks, Doctor. Will come back later with the test results.
(Later during the day)
I: (Handing over the lab report) Here Doc Sahib. Please have a look at the report.
Doctor: (Going through the test report) That is what I thought. You have gout! Your uric acid level is 12.4 mg/dl which ideally should have been between 3.5 mg/dl – 7.0 mg/dl. I will immediately start the medication.
(The doctor then spent few minutes to explain what gout was and how it impacted my health, and my lifestyle.)
I: Any restrictions on diet?
Doctor: Yes. For the time being completely stop eating your favorites – cheese, tomato, spinach and all dals (lintels) except ‘moong’ dal.
The treatment started that same day, and within three weeks the pain had substantially subsided, and gout was well under control.
What I have narrated above was actually the Step 3 of the treatment plan that I had followed for almost three weeks before I met that doctor at Agra.
  • The symptoms – After having spent more than five years in Bangalore I had just moved to Noida and had started to adjust to a different living (and professional) environment. One day I woke up to acute pain in the area near my left toe. It appeared a little swollen and made it difficult for me to even wear the shoes.
  • Treatment Step 0 – As usually happens with all of us, initially I tried out some home remedies only, like soaking the leg in warm water and taking some pain killers. That was to no avail and the pain persisted.
  • Treatment Step 1 – A few days later I had to attend a family gathering where a relative of mine, fresh out of college after completing her course in medicine, had a look at it and opined that it could be some allergic reaction due to change of location (from Bangalore to Noida) and prescribed some tablets. However, the pain still persisted and even increased after few days of that treatment.
  • Treatment Step 2 – It was then that I decided to consult a practicing physician and went to a clinic just across the road where we lived. He examined the pain area and diagnosed it as some sort of inflammation and advised putting poultice for few days. Even after several days of that treatment, the pain did not subside but actually aggravated.
  • Treatment Step 3 – Experiencing no relief for over three weeks, I finally decided to consult my younger brother, a leading plastic surgeon at Agra, who took me to one of his colleagues who was a leading physician. What happened next, I have already stated above.

DIAGNOSIS

Now after twenty-one years when I analyze that line of treatment, I realize that
  1. The young doctor who first advised me maybe had never seen such symptoms earlier and thus was not able to diagnose correctly.
  2. The physician I consulted next might have seen only a few patients with a similar set of symptoms that I had (but not with the same illness), therefore was not able to formulate the right questions to ask that could have led to the correct diagnosis from a set of possible outcomes arising from similar symptoms.
  3. However, the doctor at Agra with his vast experience, had obviously seen those set of symptoms several times earlier and had acquired sufficient I and L to treat such cases effectively.
  4. Thus, though all the three doctors were surely competent, what the first two doctors obviously lacked were
  • the ability to apply their I (Intelligence) in (a) arriving at the correct diagnosis based on the symptoms they were presented with, and (b) subsequently determining an appropriate line of treatment; and
  • the extent of L (Learning) that comes with experience of treating hundreds and thousands of patients with various types of symptoms possible that brings in the knowledge that what could be the possible diagnoses and what treatments worked or did not, and why or why not?

LINE OF TREATMENT

By applying AI and ML techniques and solutions in healthcare it may now become possible to make available the accumulated I and L – resulting from the large number of successful (and unsuccessful) treatments by various experienced doctors – to those competent but less experienced physicians.
With access to an appropriate AI/ML system, even a physician in a small clinic in a remote location could
  • draw upon the accumulated experience of other successful doctors;
  • be guided properly to arrive at the correct diagnosis and subsequently to determine an appropriate line of treatment; and
  • confirm that the planned line of treatment is suitable for the medical profile of the patient. In case the patient’s medical profile is not readily available (like in case of emergency patients or admittances to trauma centers), AI/ML systems could caution the first medical responders on the possible complications (if any) associated with any planned line of treatment.

OUTCOME

Effective use of AI/ML systems in healthcare can deliver sustained benefits for all relevant stakeholders:
For the patient:
  • Assurance that the physician would arrive at a correct diagnosis, and would propose an appropriate and effective line of treatment with less or almost no margin of error;
  • Obviating the need to rush to a larger hospital/clinic just because the symptoms are a bit abnormal; and
  • Faster and more effective response from medics in emergency cases.
For the healthcare provider:
  • Increased efficiency with lower turnaround time for patients;
  • Faster and accurate diagnosis and effective treatment;
  • Substantial reduction in unfair treatment cases; and
  • Substantially faster and accurate response by first medical responders in emergency cases.

FOLLOW-UP

In the face of massive disruption taking place in healthcare space, and the frantic pace of medical data generation, any AI/ML system is likely to be soon become outdated, ineffective and irrelevant, if it is not constantly updating its Intelligence and is not constantly Learning.
Thus, it is imperative that all instances of successes and failures, arising out of using any AI/ML system, are fed back into that system to ensure constant refinement of its algorithms. That will result in it providing even more accurate outcomes for future users.

Conclusion

From the above it is evident that an AI/ML system can be a powerful ally of a physician and its deployment should not be termed as “man against machine” by any means.
In my opinion, AI/ML technologies are still meant to assist the medical fraternity and are not really likely to replace doctors (at least in foreseeable future)!
The article has been republished here with the authors permission. The article was first published in the authors’ linkedin pulse page.

Author
Jyoti Sahai

Chairman and Managing Director at Kavaii Business Analytics India Pvt. Ltd. Jyoti Sahai has over 42 years of experience in banking and IT industry, and is currently the CMD of Kavaii Business Analytics India. Kavaii provides analytic solutions in Healthcare and IT Services domains.

Natural Language Processing #NLP – Giving doctors the freedom to write what they want by Dr. Anuradha Monga

Healthcare produces the highest quantity of data records as compared to any other industry. There has been a substantive shift in the provider workflows from capturing data in paper based records to electronic modes and storage in the past few decades.

Natural Language Processing: Giving doctors the freedom to write what they want

Electronic health records (EHRs) have clearly emerged as an innovative technology to facilitate the transition. However despite of the advancements, EHRs have not been able to achieve credible benefits in areas of population health management, health information exchange, patient care coordination and clinical analytics. 

One of the biggest barriers in achieving success with EHRs has been the disparate forms of data which are difficult to aggregate and analyze. Doctors feel comfortable writing notes along with the flow of their clinical thoughts, however EHRs are not designed to capture medical information in a doctor’s natural language. This inability many a times leads to poor EHR usability. As a result, a lot of valuable information is left out from the ambit of analysis. With the advent of newer technologies, now it may be possible to plug such gaps. NLP (natural language processing) is one such technology which providers are now adopting with an anticipation to improve clinical outcomes and for the simplification of the daunting task of data entry in a computer.

Clinical data is not consistent, making analysis difficult

An EHR captures data in primarily four ways:

  • Clinical Data is directly entered in pre-structured templates 
  • Scanned documents are uploaded in the system 
  • Text reports are transcribed by speech recognition technology or by dictation and manual data entry.
  • Data is purged into an EHR by interfacing it with other information systems like laboratory systems, radiology systems, or monitoring devices. 


Clinical data is usually presented in a structured or unstructured format. Selective choices for capturing data in the form of templates like physician order sets, drop down menus, check boxes etc constitute structured data. Aggregation, analysis and reporting from structured data is easier but doesn’t provide an individualized, customized identity to an EHR. On the other hand, unstructured data constitutes free text narratives and clinical notes i.e doctor’s notes, patient encounters, patient health records etc and enable the physicians and patients to get their observations, complaints and concepts recorded in their own parlance. The unstructured data is a rich source of information about a patient’s health but it’s a challenge to transform it into structured and analyzable data that can be used for improving care outcomes. This challenge can be overcome with the technology of natural language processing.

Unstructured clinical notes are a mine of golden data; the wait to explore them ends with NLP


NLP is a data science based technology that can extract data from free text. NLP can be used by clinicians to convert medical notes into formats which are structured and standardized. Auto-processing of textual data can help providers in making use of clinical documentation data for a variety of purposes including but not limited to:

  • Improving communications between healthcare teams and thus help improve outcomes
  • Reduce overhead costs of clinical documentation
  • Improve revenues by automation of the coding and documentation


Computers can be given the ability to infer the intended meaning of words, thus enabling them to identify trends and patterns in huge datasets. 

NLP can change the course of the way chronic diseases are managed:

One of the most promising area for exploring use cases of NLP in healthcare includes predictive analytics and risk scoring. Carefully deployed AI tools can be used for risk stratification and determination of hotspots in chronic diseases. 

NLP can be used to tag socioeconomic terms hidden in free text notes to identify the social determinants of health. This can be augmented with machine learning to develop risk scores by proactive identification of trends from clinical and social data, laboratory reports, diagnoses etc. It is possible to create algorithms and train them on clinical record data to identify disease symptoms accurately. 

Clinical records are a rich source of information regarding the symptoms of many diseases. Grouping of such similar symptoms can help in syndrome identification on the basis of disease presentation. As a result, it may be possible to unearth clusters which may otherwise not be suspected. Routinely available information in electronic health records, such as demographic and geographical location data and primary care free-text clinical records should be leveraged while making use of such algorithms.  

Why off the shelf NLP engines may not be what the doctors want:


While it sounds easy, healthcare free text data comes with its own challenges. Word sense ambiguity is perhaps one of the most challenging problems in the noise of free text clinical notes. Accurate translation of the structured patient information pertaining to medical procedures, symptoms, tests etc depends on the algorithm’s ability to assign correct interpretations to the relevant medical words. For example, the acronym RA can be used in different contexts with different meaning by doctors. RA can be interpreted as right atrium, right arm or rheumatoid arthritis depending on the case presentation and clinical context. 

Disambiguating the senses of acronyms, symbols and words that are used in a doctor’s clinical notes can significantly ease the burden on human effort needed to develop more accurate systems. A data-driven approach which involves development of any algorithm that infers patterns should consist of a supervised and unsupervised learning phase to yield benefits. In supervised learning every data item of the training data is labeled with the correct answer. Unsupervised learning on the other hand is a process where the computer recognizes patterns automatically. The true potential of an NLP and machine learning algorithm can only be harnessed when the data is trained in the provider’s environment.

Word sense disambiguation based NLP pays a significant role in improved analytics and patient outcomes:

Word sense ambiguation based language processing ability of the computer for accurate mining of clinical documents can bridge the gaps in documentation and aid clinical decision support and clinical documentation improvement programs. 

More insightful extraction of data is possible with a decreased ambiguity in clinical data. When the computer has the ability to infer the intended meaning of words, it can find useful patterns in heaps of data easily. IBM’s Watson Supercomputer technology is an apt example of how NLP can facilitate meaningful analytics, by identifying such patterns. IBM’s content analytics process is used for collection and analysis of structured and unstructured data, and its similarity analytics makes use of NLP and machine learning technology for analysis of a large number of variables in a patient’s medical history and present condition to identify patterns and draw a comparison with similar conditions and potential outcomes. 

There is no doubt that word sense disambiguation enabled NLP technology can have a potentially huge on impact clinical data analytics with its superior ability to infer meanings of extracted data more accurately. Data analytics for improved patient outcomes is not the only benefit of this technology, it can also support accuracy of billing. With its ability to support clinical documentation improvement programs, it can also help in improving clinical workflows.

SymptomAI by “PredictDisease” is a healthcare analytics platform that is driven by artificial intelligence, NLP and machine learning to assist patients and primary care physicians by measuring the potential risk of a chronic disease that starts with minor symptoms. The platform leverages data from lifestyle activities, social media/website forums, scientific research papers, and family history, matching these with known signs/symptoms and other demographic characteristics for the early detection of the chronic disease. It takes into account, social and biologic determinants of health to predict the risk score. Visit us at www.predictdisease.com or write to us at info@predictdisease.com for more info.

References: 
[1]. Auto Coding and NLP: 
http://www.himss.org/content/files/AutoCodingandNaturalLanguageProcessing(WhitePaper).pdf

[2]: Dooling, Julie A. “Advancing Technology Connects Transcription and Coding: The Developing Role of NLP, NLU, and CAC in HIM.” Journal of AHIMA 83, no.7 (July 2012): 52-53

[3]: Goldberg, Michael. “IBM Makes New Health Care Push with Predictive Analytics, Process Management.” Data Informed. http://data-informed.com/ibm-makes-new-health-care-push-with-predictive-analytics-process-management/


Author
Dr. (Maj) Anuradha Monga

A versatile military veteran with expertise in healthcare management, Anuradha has acquired real world experience in areas of Hospital operations, Health insurance claims management and mass insurance, Healthcare IT, NABH implementation and digital marketing.

Human Factors in Healthcare by Dr. Ruchi Dass, @drruchibhatt


Stare in the middle of the image below..as you move your eyes around you will see black dots flashing. now you know that there is no flashing possible here but it tricks your brain.

Human eyes could be easier to trick than you might think.A Japanese professor, Kokichi Sugihara, created sculptures that trick the mind to see the impossible.
He was the winner of the Best Illusion of the Year Contest in 2010 and 2nd place in 2016.
Check out some of Sugihara’s best brain-bending illusions:
Like when that thing goes there, isn’t it supposed to go over there and not over there? Shouldn’t the little balls be rolling to the right instead of the left? How did that thing do the thing? Huh?!

Let us talk about Healthcare now. Illusions occur when brain tries to make sense out of conflicting information based on his/her experience. Many of you would have heard about Conformational bias.

Conformational Bias 
Human sight is falliable and images can easily be misread leading to errors in processing information. For practising Physicians as well, we have seen such conformational bias. When a new piece of information is presented which is not in tandem and consistent with someone’s current mental model, there are chances that it will be disregarded. Let us think of a mental model including the three units in Patient management-
Experience– Expectation—Briefing
My cousin got admitted in the Emergency department Saturday night and the duty doctor made a presumptive diagnosis. The next day this diagnosis was handed over to the head of the department as a part of patient’s history. The Physician here adopted the briefing, experience and expectation of the Emergency department Physician to his mental model and prescribed medication- possibly got blinded to realise a differing picture.
That is why in Psychology of Healthcare, a shared mental model design is necessary. There are a lot of factors that can influence situation awareness. Mental model sharing includes:
1. members of the team holding the least possible/least consistent mental model as their approach to avoid underestimation, conditioned thinking and oversight.
2. Effective communication between all team members
3. active monitoring of team member’s actions, understanding, data processed/evidence available through the senses -also for patient, equipment, summary, documentation and instruments.
Schizophrenia 
The important principle to understand is that our perceptions are not the same thing as stimuli that are picked up by our sensory receptors. Think of hallucinations in individuals experiencing mental illness like schizophrenia.
Hallucinations are involuntary and can occur in the absence of an external stimuli. That means a person sees or hear something that is not there. In the neuroimaging studies, scientists have gathered information around normal and abnormal conciousness and unconcious brains states.
Brain can pretty much create its own realities and illusions. That means brain can respond differently to external and internal stimulus.
In the 19th century, Dr. E. Babbit, M.D. proved that colored light was capable of healing through the effect on the autonomic nerve fibers in the skin and via the nerves from the eye to the brain. Dr. Spitler proved in the 1930’s that psychiatric illnesses could be cured or improved by using a visual colored light source. It is now known that there are at least four effects from light. These are: 
1. The optic nerve to the pituitary gland, temporal lobe, and occipital lobe of the brain. This information affects the conscious part of the brain without interpretation. 
2. A second nerve bundle from the retina to the hypothalamus, which is a major control area for both the sympathetic and parasympathetic nerves. Dr. Fritz Hollwich, M.D. has shown that color affects neurotransmitter and hormone levels in the brain and spinal cord, which in turn affect the rest of metabolism and biochemistry. 
3. This path goes from the retina to the midbrain, and then to the superior cervical ganglion to the brainstem and then to the pineal gland. This area controls, among other things, our circadian (sleep/wake) cycle. 
4. The last is a direct effect of the light upon particles that travel in the lymph, blood, and nerves. Researchers at the University of Vienna found that albumin is one of the particles able to be charged by light. It is then able to deliver this charge to tissues at distant locations (tissues) in the body. 
Sources and recommended reads

Using Magic to Throw Light on Tricky Healthcare ;Systems: Patient Safety Problem Solving ;Linda C. Williams 

User-Driven Healthcare: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications:

https://play.google.com/store/books/details/Management_Association_Information_Resources_User?id=mcSeBQAAQBAJ

Communication Skills for the Health Care Professional: Concepts, Practice, and Evidence: 

https://books.google.co.in/books/about/Communication_Skills_for_the_Health_Care.html?id=oWluAAAAQBAJ

The Article was first published on Dr. Ruchi’s LinkedIn Pulse Blog, here. The article is republished here with the Author’s permission.

Author

Article By: Dr. Ruchi Dass

Digital Health Influencer & Health Innovator (HIT, Big Data, IoT, Analytics and Cloud)| TED speaker | Investor and Mentor

Application of Design Thinking in the Healthcare- Survey results by Vishnu Saxena, @vishnu_saxena

Design thinking is gaining it’s rightful prominence in the Healthcare as a valuable approach to solve range of healthcare issues and redesign care delivery. However, application of Design thinking principles are still not mainstream. NEJM surveyed 625 of their council experts from executives, clinical leaders to clinicians and come-up with their finding on the state of Design thinking in the healthcare.

Here is the catching observation from the report:

  • Majority of the respondent (39% ) said they only “Occasionally” employ principles/techniques of design thinking while only 20% said they do it “mostly”. With 21% said “seldom”. Clearly Design thinking still has not won hearts and minds of decision makers. Point 6 informs why.
  • Top five issues that would benefit to the “larger healthcare industry” from Design thinking are: 1- Care Co-ordination; 2- Integration and funding of SDoH; 3- Technology use, integration & improvement ; 4- Payment reform; 5- Patient Engagement .
  • Top five issues that would benefit most to their “respected organization” from the design thinking approaches: 1- Staff and provider flow & Collaboration; 2- Scheduling patient appointment and reducing no-shows; 3- Patient adherence/ compliance with Therapy 4- Patient Satisfaction score ; 5- Patient flow during office visits & procedures
  • There is a greater consensus among NEJM council members that Design Thinking is useful in “Healthcare Industry“ with 44% saying it is extremely useful while 36% saying it is extremely useful for “their organization”. 
  • Clinical Leaders ( 45%), Executives (37%), and Clinicians(33%) are the most appropriate champions of Design Thinking.
  • This is important- Top four barriers to Applying Design thinking in the Healthcare identified as: 1- Limited buy-in from Stakeholders; 2- Limited understating of Design; 3- Insufficient training in Design and 4- Uncertainty about ROI. In my opinion, more awareness on ‘2’ and ‘4’ can quickly change ‘1’. 
As Healthcare moves towards consumerization and patient centricity, Design thinking can considerably improve health care experience and outcomes. Digital Health assets/solutions built using Design thinking approach ( Design 1st, Technology 2nd) provide better experience and have far greater rate of adoption, adherence that result in effective engagement.  Is Design thinking part of your strategy..?

The article was first published on the Author’s Linkedin Pulse Blogs, its republished here with the Author’s permission.

Author
Vishnu Saxena

Vishnu is an Industry connected transformational healthcare Leader with an impressive reputation for building high growth, high value healthcare business practices, turning around digital health startups and advising accelerators, innovators. Proven track record in partnering with healthcare stakeholders (Providers, payers, pharmaceuticals), ISV’s, med devices companies and helping them succeed across digital medicine innovation, patient/member engagement strategy, redefining patient experience thru design thinking, value based care transformation, technology consulting, Digital integration and Data-Analytics goals

New Data Protection Law Proposed in India! Flavors of GDPR by TMT Practice Team at Nishith Desai Associates



NEW DATA PROTECTION LAW PROPOSED IN INDIA! FLAVORS OF GDPR 
The much-awaited Personal Data Protection Bill, 2018 (“Draft Bill”) was released by the Committee of Experts entrusted with creating a Data Protection Framework for India (“Committee”) on Friday evening.


The Committee, chaired by retired Supreme Court judge, Justice Srikrishna, was constituted in August 2017 by the Ministry of Electronics & Information Technology, Government of India (“MeitY”) to come up with a draft of a data protection law. After over a year of deliberations and a series of a public consultations followed by release of a white paper with preliminary views, the Committee has released a Draft Bill. The Draft Bill is accompanied by its report titled “A Free and Fair Digital Economy Protecting Privacy, Empowering Indians” (“Report”) which provides context to the deliberations of the Committee.

MeitY as the nodal ministry may accept, reject or alter such Draft Bill. Thereafter, the Draft Bill would need to be approved by the Union Cabinet before it is introduced in the Parliament for deliberations.

Some of the key highlights of the Draft Bill are:

  • Extra-territorial application i.e. the Draft Bill is to apply to foreign data processors in so far as they have a business connection to India or carry on activities involving profiling of individuals in India.
  • Differential obligations imposed based on criticality of data, i.e. differing obligations for Personal Data and Sensitive Personal Data;
  • Obligations of the Data Processor : Notice (that is clear, concise and comprehensible), Purpose Limitation and Collection Limitation, maintaining data quality, storage limitation;
  • Grounds for processing in addition to consent include use for employment purposes as well as emergencies.
  • Intended to be made applicable to the State as well as private parties.
  • Child Rights: Child is defined as someone who is less than 18 years of age. Profiling, tracking or behavioral monitoring of or targeted advertising towards children is not permitted.
  • Rights of the Data Subject: Include Data Portability, Right to be forgotten as well as the right to correction of the data etc.
  • Concept of Privacy by design and a data breach notification have also been introduced;
  • High Risk Data Processors – A mandatory registration requirement has been imposed on data processors who conduct high risk processing. Such processors are required to implement: Trust Scores, Data Audits as well as a Data Protection Impact Assessment
  • Data Localisation: A copy of all Personal Data must be stored in India; additionally the Government may notify certain types of personal data that should be mandatorily be processed only in India. The Government has retained with itself the power to exempt storage of copies of Sensitive Personal Data, in some cases.
  • Cross Border Data Flows: In addition to consent cross border transfers would also require the use of (a) model clauses; and (b) possible adequacy requirements, i.e. transfer to jurisdictions approved by the Government;
  • The Data Protection Authority of India (“Authority”) appointed under the Act will provide or endorse Codes of Practices.
  • GDPR Style Penalties: Upto 4% of global turnover in some cases;
  • Criminal penalties also introduced for limited cases;
  • Phased manner of implementation once the law is implemented.


To summarize, whilst we believe that the Draft Bill does have its share of positives, in several places the Draft Bill is either ambiguous / not clear or imposes excessive obligations on Data Fiduciaries and prescribes disproportionate punishments. Several factors are left to be determined through Codes of Practices or to be determined by the Government at a later stage. Therefore, at this stage the full impact of the proposed law cannot be comprehended in entirety.

In several respects, we note the Draft Bill appears to have borrowed heavily from the recently notified E.U. General Data Protection Regulation (“GDPR”). Given the infancy at which the GDPR is at this stage, it would be imperative that law makers provide for enough flexibility for the law to be altered on the basis of global experiences. Further, we find that even the current basic law under the Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information) Rules, 2011 (“2011 Rules”) has yet not been implemented fully even after 7 years. Therefore, implementation will be key to this fairly detailed and somewhat cumbersome law.

We hope that the law is made more balanced by diluting some of the draconian provisions as well as by issuing clarifications on the points that are not clear, after public consultation. Therefore, ideally, once the MeitY finalizes the draft, it should place such law in the public domain and provide stakeholders an opportunity to provide further inputs, before the law is placed before parliament.

We have set out in our detailed analysis below the possible implications that it may have on businesses, including offshore companies doing business in India. As we continue to read, debate and delve deeper into the wording of the law, our views on several of these issues may evolve.

To summarize, while the Draft Bill does have its share of positives, in several places the Draft Bill is either ambiguous / not clear or imposes excessive obligations on Data Fiduciaries and prescribes disproportionate punishments. It also seems to have certain unintended consequences for start ups/non digital businesses in terms of imposing exposing them to excessive compliances. 

Our detailed analysis of the Draft Bill is available here.

Please do join us this Tuesday (31Jul 2018) and / or Wednesday (01 Aug 2018) at our Webinar where we discuss the impact that the Draft Bill may have. The registration link for the same is available here.

Email the Technology & Privacy Law Team and You can direct your queries or comments to the authors

The article was first published here,  its been republished on the HCITExperts Blog with the authors permission. 

Additional Reading:
1. Regulatory Essentials for eHealth in India by Dr. Milind Antani, Nishith Desai Associates: 
https://blog.hcitexpert.com/2018/03/regulatory-essentials-for-e-health-in-india-Dr-milind-antani.html

Author
TMT Practice Team at Nishith Desai Associates

Nishith Desai Associates is a research-based Indian law firm with offices in Mumbai, Silicon Valley, Bangalore, Singapore, Mumbai BKC, Delhi, Munich and New York that aims at providing strategic, legal and tax services across various sectors; some of which are IP, pharma and life-sciences, corporate, technology and media

#Blockchain for HealthCare Equity by Arnab Paul, @iArnabPaul

In a digital Age when cars drive themselves and CEOs hold meetings across continents in virtual reality conference rooms, engagement of the disenfranchised is a less attractive endeavor than the sleek apps, making it an outlier in the realm of tech solutions.

In our endeavour to promote digital india it should be our collective effort to bring healthcare to the disenfranchised and to the people who slip out of the cracks.

Most of us just don’t bother to take care of the elephant in the room, its time all the stakeholders joined hands and come up with a solution. For me personally Equity is of paramount importance in healthcare.The lack of focus on vulnerable populations in patient safety discounts the significance of the many lives lost, all precious to those who love them. we have yet to place strategic emphasis on the need to protect all. A man’s life lost to medical error then disguised as a heart attack, either intentionally or because of unconscious prejudice about the depth of his pocket, is more than a patient safety event. 
For the millions of people who have been exposed to discrimination based on their spending capacity and limited access to resources and denial of equality in humanity, such an event adds insult to tragic injury.We must connect in ridding our health system of all forms of inequality and ensuring that all people are protected from harm equally.
As hospitals and care systems work to improve quality of care and prepare for coming changes in the health care field, the ability to fully understand their patient populations and communities is critical. Collecting and using ethnicity, language, spending capacity data will help hospitals and care systems understand their patient populations and address health care disparities. While many hospitals are successfully collecting REAL data, fewer are effectively stratifying the data to shed light on health care disparities,
We need to systematically collect REAL preference data on all patients. We need to use REAL data to look for variations in clinical outcomes, resource utilization, length of stay and frequency of readmissions within our hospital. We need to compare patient satisfaction ratings among diverse groups and act on the information. Above all we need to actively use REAL data for strategic and outreach planning for the underprivileged.
Patient satisfaction is not a clearly defined concept, although it is identified as an important quality outcome indicator to measure success of the services delivery system
There is no clear consensus between the literatures on how to define the concept of patient satisfaction in healthcare.
In Donabedian’s quality measurement model
patient satisfaction is defined as patient-reported outcome measure while the structures and processes of care can be measured by patient-reported experiences
For everything in life we need some kind of metrics, some tools to measure the clinical outcome and the patient satisfaction. So to make up for it may I suggest we incorporate Tech enabled, Blockchain optimized patient feedback mechanism.
So what is the solution, how do we propose to go about it, well unlike Press Ganey & HCAHPS (the Hospital Consumer Assessment of Healthcare Providers and Systems), Press Ganey has stated that a minimum of 30 survey responses is necessary to draw meaningful conclusions from the data it receives and that it will not stand behind statistical analysis when less than 30 responses are received. 
If we all incorporate a blockchain Ecosystem & go truly real time in the patient feedback mechanism it would greatly enhance the whole patient experience and maybe help to manage solve some of the issues in real time. Wouldn’t it be just great if we incorporate Blockchain in the patient feedback loop, we wouldn’t have to wait for 30 odd surveys to be analyzed we could just go ahead and fix the situation right away if it warrants an action.
Another major issue is NO show and Missed Appointments
One study estimates, in US alone missed appointments cost US healthcare providers up to $150 billion a year.There have been instances that a Clinic loses money because of No Showand missed appointments.Patients not showing up can be costly to the health-care system. Offices lose out on revenue, and delaying care can lead to more expensive treatments later on.

“We very much believe it’s going to take a collaborative effort, and we think that this kind of technology integration is going to be a critical path for being successful in terms of breaking down those barriers for access to transportation for the patient community.”  David Baga, CBO, Lyft

Allscripts, Lyft and few other companies have joined hands to address this problem. The companies said they hope working together will reduce the number of people who miss medical appointments because of transportation issues.
But interesting it was found in another study giving poor people free use of ridesharing services like Uber and Lyft for doctor appointments doesn’t make them any less likely to become no-shows than patients who have to find their own way there, a U.S. study suggests.
So what are we missing here, I believe incentivising ( tokens ) is the key and Blockchain could play a major role. Blockchain in itself is not a panacea for all things healthcare but it certainly holds the key to transform the current healthcare service delivery mechanism and make it more transparent and efficient.
Ehealth or no ehealth, if its not able to solve the issues of equity & empathy than its no value prop only noise, maybe it would help become a excellent facilitator in healthcare delivery but it sadly would not be able to solve the core issue of equity and empathy.
The concluding part follows:
How Blockchain could be a gamechanger for healthcare

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Arnab Paul, CEO, Patient Planet

Globally-minded systems thinker, action-oriented and inspired toward optimizing health outcomes through innovation, creativity, cooperation. Passionate about facilitating the alignment among technology, people and processes to ultimately improve patient experience and the functioning of healthcare.

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Zen Clinicals: An Activity & Workflow based solution (2 of 4)



Part 2 of 4

Now that we have defined the various actors and the activities that they could be performing. It becomes to important to define the guidance as to how these activities will be delivered to their respective audiences. 

In the Zen Clinical System 

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