Tag: GUEST BLOG

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.

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.

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

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

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

Author

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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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