Month: December 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.

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