Tag: Dr. Pramod Jacob

#Podcast S1 E2: Dr. Pramod David Jacob and Dr. Thanga Prabhu, @thangas discuss the impact of the National Digital Health Blueprint

In our podcast today, I ask our two eminent experts about their experiences of enabling DigitalHealth in India and more recently about the National Digital Health Blueprint and what it means for the ecosystem in India.

Continue reading “#Podcast S1 E2: Dr. Pramod David Jacob and Dr. Thanga Prabhu, @thangas discuss the impact of the National Digital Health Blueprint”

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. 

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. 


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.

The Integrated Disease Surveillance Program (IDSP ) of India story by Dr. Pramod Jacob

Considering the Nipah virus containment story recently, I thought it would be appropriate to write about the IDSP program in India, as it had a major role in this containment.

The Integrated Disease Surveillance Project (IDSP) was launched in November 2004 with the assistance of the World Bank, to identify and respond to disease outbreaks and epidemics at an early stage, preferably before an event becomes an epidemic.

There were 4 Components:
a. Decentralisation and integration of surveillance activities through establishment of surveillance units at district, state and central levels
b. Human Resource Development with training of State Surveillance Officers, District Surveillance Officers, Rapid Response Teams and other relevant staff
c. Use of information technology for collection, compilation, analysis and dissemination of data and
d. Enhancement of Public Health Laboratories.

The following Objectives were to be met
1. Cover limited number of diseases of public health importance which needed public health response
2. Implement multiple methods of surveillance
3. Be a proactive program with timely response at all levels i.e. Be an Early Warning and Response (EWAR) program
4. Use Information Technology to facilitate information gathering, collation, analysis and dissemination
5. Decentralise and have states take ownership and
6. Centre be responsible for coordination, quality control, policy formulation, finance management and technical assistance.

It was realised that though the healthcare infrastructure in India had grown over the years, disease surveillance had not got the required attention in the past, resulting in late detection of disease outbreaks with related morbidity and mortality. One of the main reasons for this shortcoming was the time-consuming and labour-intensive manual methods of data collection, transmission, analysis and feedback for response with paper. 

Hence a countrywide Information and Communication Technology (ICT) network was established under IDSP with the help of National Informatics Centre (NIC) and Indian Space Research Organisation (ISRO). This IDSP network connects the District Surveillance Offices to the State Surveillance Offices which then connects to the Central Surveillance Office at the National Centre for Disease Control (NCDC). 

The network is also presently being deployed to CHC and even PHC levels in some states. The network is used for data entry, compilation, analysis and feedback from data coming in from the sub centre level and above. It has video conferencing ability to help in meetings and training sessions. Furthermore, there is an IDSP portal ( www.idsp.nic.in ), which is a one-stop portal for data entry, reports, outbreak reporting, data analysis and training modules related to disease surveillance.

The IDSP program has three methods of surveillance 
1.  Indicator Based Surveillance 
2.  Event Based Surveillance and 
3.  Media Surveillance. 

Briefly describing each of these: –

Indicator Based Surveillance

There are three levels where these indicators are collected: – 

S form – this form is filled by the sub-center health worker and collects collated details on conditions such as Fever, Cough, Loose watery stools, Jaundice and Acute Flaccid Paralysis (AFP). These forms are submitted to the supervising primary care centre once a week and fed into the IDSP system via the District Surveillance Unit (DSU).

P form – this form is filled by the primary care providers and collects collated data on about 20 different conditions including the above plus additional conditions such as Pertussis, Diphtheria, Leptospirosis etc. This is also submitted on a weekly basis and uploaded into the IDSP system weekly via the DSU.

L form – this is the form collected from public health (and private) Labs for positive test results for specific diseases such as Dengue, Japanese encephalitis , Cholera etc. The difference in these forms are that for each positive result, further details such as the patient name, age, address, test done and lab confirmation diagnosis is also recorded. These forms also go into the IDSP network via the DSU on a weekly basis.

For more details about information on each of these forms please visit the following link 

From the DSU the information gets instantaneously transmitted to the State Surveillance Unit (SSU) and then the Central Surveillance Unit (CSU) via the IDSP network. This network has been effectively working since 2010. The result is that the CSU based in the National Centre for Disease Control (NCDC), has been publishing nationwide outbreaks of these specific diseases on a week by week basis about a month later (On June 20th could see the outbreaks that occurred in the week of May 14th to May 20th) – here is the link 

Event Based Surveillance

Since public health only covers about 30 percent of the population – it was realized that there had to be a mechanism in place to identify and respond to events such as suspected outbreaks like epidemics or events that could endanger the public.
An incident maybe reported through the rumour registry or through review of indicator-based surveillance data or through the media.  Whenever there is such an incident reported and verified, there is an Early Warning Signal (EWS) protocol carried out, to which a Rapid Response Team (RRT) of a district investigates and takes the appropriate action.
The Rapid Response Team in a district is a multi-faceted team looking into various aspects of a potential outbreak. The suggested members would be an epidemiologist, a clinician and a microbiologist/virologist. The RRT is not a permanent team but is formed when the need arises from existing resources in the concerned district under the aegis of the DSU. After an initial investigation by the concerned Medical Officer with filing of an Early Warning Signal/Outbreak report, the RRT verifies the outbreak through Medical and Lab investigations, with the epidemiologist studying the epidemiological and environmental aspects of the outbreak including source of the problem and routes of transmission.
Once the answers for the causal agent, source of infection, transmission pattern and people at risk are found, the RRT comes up with the specific recommendations and action plan to curtail the outbreak to be implemented by the concerned public health staff which can include state and central (NCDC) levels if needed. This may include steps such as identifying infection isolation points, enforcing infection control protocols, organizing logistics such as special protective gear, burial protocol and sites, tracking and quarantine of contacts, ensuring disease awareness and precautions to be taken by the public. It was this mechanism that played a major role in control and containment of the recent Nipah virus outbreak in the country.

Media Surveillance

NCDC and some states have a Media Scanning and Verification Cell that monitors Global, National and Regional electronic and print media for reports on suspected outbreaks or unusual health events. It disseminates such reports to the concerned districts digitally for verification and follow up. The major part of the screening is manual with a process in place for filtering genuine from fake news. There are plans afoot to bring in automation into the screening process.

In summary it is a combination of all the above three methods that bring about the Early Warning mechanism for outbreaks and potential epidemics in India. While there is much room for improvement- the IDSP program has proven the effectiveness of a nationwide IT network and in-fact can potentially be upgraded to be the Healthcare IT highway for the country.  

“Dedicated to the IDSP program and public health staff of India – who do so much with so little. Often criticized, seldom appreciated, a big heartfelt thank you” – Dr. Pramod Jacob

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.

Why India needs Healthcare Information Technology (HIT) by Dr Pramod D. Jacob

India with its vast population of over 1.3 billion firstly has a challenge in keeping a track of this vast population’s health, much less keep them healthy.  One of the major reasons for this is lack of timely, accurate and reliable healthcare information in today’s paper world

State of Health in India

In healthcare India ranks very poorly, even compared to our neighbouring countries. For example in the following health indicators: –

Maternal Mortality Rate (year 2015): defined as number of women who die during pregnancy and childbirth, per 100,000 live births. India has a rate of 174 maternal deaths per 100,000 live births, which is worse than Bhutan (148 /  100,000) or Sri Lanka (30 / 100,000 ). China which also has a large population is much better (27 / 100,000) 

Infant Mortality Rate (year 2017): defined as number of children who die less than one year of age per 1000 live births. In India the figure is 39 per 1000 live births, behind Bangladesh ( 32 / 1000 ) and Nepal ( 28 / 1000 ). China is 12 / 1000.

State of healthcare information collection for events like epidemics in India

Before 2010, it would take about six months for the health information to be collected, collated and analysed to prove that a given region in India had an epidemic as the entire process was paper based. By that time the disease (with most being self-limiting) would have struck, had its toll of morbidity and mortality and run its course. With most data collection being paper based this delay costs India loss of lives and productivity with high morbidity, especially in rural areas ( in urban areas- private hospitals and clinics have a process of notifying the public health authorities for notifiable diseases, hence epidemics are identified earlier in urban areas) .

To top it all there is general disbelief in the official published health statistics in India. For example, official data claimed that Malarial deaths in India was only 1,023 in 2010, however a Lancet published study showed the figure to be actually 46,800. Following the Lancet article, the official data agreed that they had their figures off by twenty to thirty times.  Even for a common disease like Cholera, which strikes every monsoon in endemic areas along the Ganges and Brahmaputra, the official estimate for India is 3,631 cases per year, while research has shown this to be about 22,200 per year.   

While the immediate reaction is to blame the public health authorities and Government in India, one must understand the limitations in a paper world to collect health information of 1.3 billion people across 3,200,000 square kilometres. Compare that to collection of information electronically – an electron can travel around the world in about 19 seconds. 

The solution – Healthcare Information Technology (HIT)

The solution is to produce healthcare information in a timely manner with accuracy and reliability. To achieve speed, it is best to do so with Information Technology – hence HIT. To achieve accuracy and reliability, it is best if the patient’s data is put into the HIT system by the providers of healthcare such as doctors, nurses, pharmacist etc at the point of care. This patient level data can then be collated and processed to get timely, accurate and reliable population-based healthcare information.

 In addition, HIT systems provides the power of IT to healthcare such as giving alerts for drug-drug interactions, duplication in lab tests and bringing about efficiency in processes and workflows in a healthcare setting, producing reports quickly which will help in planning and deployment of healthcare. It is estimated that healthcare doubles in knowledge every few months and it is difficult for doctors to keep up. With HIT it will be possible to keep up with the latest and deploy best practice evidence-based medicine applicable for India.

The proof of HIT bringing exponential improvement in speed and access to important healthcare information like epidemics even in Indian public health, is best exemplified by the IDSP program. The IDSP program has gone digital from district level upwards to state and then to the National Centre for Disease Control (NCDC), Delhi. As a result, the NCDC now publishes data on epidemics and events on a month to month basis and will soon be publishing it on a weekly basis. Will cover the details of this program in a future write up. 

This article has been republished here with the author’s permission. The article was first published here.

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 has been a Healthcare Information Technology consultant to Benton County, Oregon and Santa Cruz County, California. In 2007 he relocated to India and did consultancy work for the state governments of Tamil Nadu and Himachal Pradesh. He was a member of the HIMSS Global EHR Task Force and the lead for India in the task force.

At present he is the Chief Medical Officer of dWise Healthcare IT solutions, involved in the designing and implementation of Clinical Information Systems and the EHR for the company. He is also a consultant for WHO India in the IDSP project and for PHFI for a Non Communicable Diseases Decision Support Application.