Digital Health in India on 2022

Welcome to a New Year 2022. I am joining as Professor and Head, Department of Digital Health Research at Santiniketan Digital Research Centre, attached to the Santiniketan Medical College, Bolpur, West Bengal, India. This has been a long journey since I had been involved with the Centre for Digital Health in Amrita Hospitals, Kochi, There we had started the first MSc (Medical Informatics) course in India from 2005. However, as it was way ahead of the times, it had to be discontinued by three years.

Now, with India embracing the Ayushman Bharat Digital Mission (ABDM), which aims to develop the backbone necessary to support the nationally integrated digital health infrastructure, it is expected to bridge the existing gap amongst different stakeholders of Healthcare ecosystem through digital highways. The National Resource Centre for EHR standards (NRCeS) at C-DAC, Pune is working very hard to offer practical solutions to the teething problems of adopting EHR Standards and the National Digital Health Blueprint (NDHB). These are all the manifestations of the third edition of the National Health Policy (2017). Apart from the EHR Standards recommended by MoHFW (2nd ed, 2016), NDHB had also recommended HL7 FHIR R4. The FHIR (India) Profiles for ABDM Health Data Interchange Specifications, which are being developed by a very active FHIR India community, are being vetted and maintained by NRCeS.

Another encouraging development has been the enactment of National Commission for Allied and Healthcare. Professions (NCAHP) Act, 2021. Under the National Commission there will be ten National Councils of which the tenth one is the National Council for Health Information Management and Health Informatic Professionals.  Prior to this the erstwhile Medical Council of India had proposed the Competency based Medical Education (CBME) for the Indian Medical Graduate. There one of the goals (2.3.2) of the learner is to be a leader and member of the healthcare team and system with capabilities to collect, analyze, synthesize and communicate health data appropriately.

Therefore, I would like to welcome 2022 as a landmark year that will usher digital health in India in a big way.

SNOMED CT and Nursing

 Introduction

To provide better and cost effective patient care, one needs to exchange healthcare information. For this to happen seamlessly, there is a dire need of Standards that facilitate this interoperability ^1,2. A Standard denotes the ability of two or more systems or components to exchange information (structural or syntactic interoperability) and to (meaningfully) use the information that has been exchanged (functional or semantic interoperability). For semantic interoperability and getting better insights into clinical data, SNOMED CT is preferred.

SNOMED CT, is accepted as the global common language for clinical terms, otherwise known as a Clinical Terminology System. SNOMED CT based clinical information benefits individual patients and clinicians as well as populations while supporting evidence-based care. SNOMED CT enables the full benefits of EHRs to be achieved by supporting both clinical data capture, and the effective retrieval and reuse of clinical information. Therefore, SNOMED-CT enabled EHR systems can lead to better informed and analyzed patient outcomes.

Earlier Efforts

Earlier, the use of a common list of SNOMED CT concepts was thought to maximize data interoperability among institutions. However, local problem list vocabularies often needed to be expanded to satisfy specific user needs. Users had to check to see if SNOMED CT contains terminology for concepts they need to fulfill their local requirements and use case. The UMLS Terminology Services (UTS) from the National Library of Medicine (NLM) ³, USA, includes a SNOMED CT browser that may be used for this purpose. The SNOMED CT Browser is available through the SNOMED CT menu of the UTS.

If needed concepts are not found in the terminology, users are encouraged to submit them for inclusion in the US Extension of SNOMED CT. To submit a new nursing problem concept, one can go to U.S. SNOMED CT Content Request System (USCRS). Additional assistance on submitting requests can be found on the USCRS User Guide page. This is how the link to the Nursing Problem List concepts is maintained and divergence of problem list vocabularies can be minimized. Institutions that use their own problem list vocabularies are encouraged to map them to SNOMED CT with a focus on the Nursing Problem List concepts to facilitate data interoperability.

Recent Happenings

The 2020 agreement ⁴ between SNOMED International and the International Council of Nurses (ICN) highlights the clinical work of nursing. The ICN is a federation of more than 130 national nurses’ associations worldwide. Under the agreement, the International Classification for Nursing Practice (ICNP) will be incorporated into SNOMED CT, which has more than 350,000 active concepts, and SNOMED International will manage, produce and release the ICNP on behalf of ICN, which will maintain governance and ownership of the ICNP content.

National and international stakeholders that use SNOMED CT can seamlessly integrate ICNP concepts into their SNOMED CT-enabled EHRs or electronic health records. The countries that presently use both ICNP and SNOMED CT will no longer need to engage in mapping activities between these two code systems. The agreement coincides with the World Health Organization (WHO) declaring 2020 as the Year of the Nurse and Midwife, and it also builds on SNOMED International’s commitment to global interoperability as part of the Joint Initiative Council for Global Health Informatics Standardization.

This collaboration has advanced interoperability, and also supported disease surveillance in real time. ICNP concepts represent the practice of 20 million nursing professionals worldwide. Interoperability is more than just health information systems exchanging information. It also means that EHR systems can exchange data with unambiguous, shared meaning and that the clinicians who document and interpret those concepts can then understand and utilize them in the same way across practices and clinical settings, transcending geographical and time barriers.

With ICNP, one can measure nursing-related indicators; then use that information to improve global, national and regional health care systems; and use that data to produce scientific evidence that will inform health system transformation for enabling global health.

COVID-19 pandemic has renewed the international focus on public health infrastructure and measures, and globally health care agencies are re-investing in community-based nursing activities including prevention, surveillance and immunization. The relationship between ICNP and SNOMED CT makes it possible for reporting of COVID-19 public health surveillance at regional, national and international levels. As nurses do the bulk of sample collection, this partnership could enable public health officials to have access to near real-time testing data that has been reliably documented by regulated health professionals, as well as subsequent additional analyses about nursing care. More significantly, the improvements in patient outcomes and the ability to meet the health needs of patients are possible through integrated health and social care systems.

Conclusions and Way Forward

While efforts are ongoing to ensure adequate coverage of nursing in SNOMED CT, there have been no studies indicating the use of SNOMED CT in nursing practice. The authors ⁵ recommend for achieving the widespread collection of interoperable SNOMED CT coded nursing data in clinical applications to evaluate nursing’s impact on patient outcomes. This is the right time to encourage the advocacy and use of SNOMED CT for enhancing the quality of nursing services.

References

1.    Sarbadhikari SN, The Role of Standards for Digital Health and Health Information Management, JBCR, 2019, 6(1):1: https://jbcr.net.in/JBCR-VOL-6-issue-1-2019-20/current-issues-volume-VI-issue-1-1.html

2.    Sarbadhikari SN, Health Data Analytics and clinical terminology systems like SNOMED CT, https://supten.blogspot.com/2020/03/terminology-standards-for-health.html

3.    National Library of Medicine, Nursing Problem List Subset of SNOMED CT, https://www.nlm.nih.gov/research/umls/Snomed/nursing_problemlist_subset.html

4.    Davison K and Kuru K, Integration of ICNP into SNOMED CT brings voice to nursing practice, https://www.snomed.org/news-and-events/articles/integration-ICNP-SNOMEDCT-brings-voice-to-nursing

5.    Junglyun Kim, Tamara G.R. Macieira, Sarah L. Meyer, Margaret Ansell (Maggie), Ragnhildur I. Bjarnadottir (Raga), Madison B. Smith, Sandra Wolfe Citty, Denise M. Schentrup, Rose Marie Nealis, Gail M. Keenan, Towards implementing SNOMED CT in nursing practice: A scoping review, International Journal of Medical Informatics, 2020, 134: 103045, https://doi.org/10.1016/j.ijmedinf.2019.104035

Standards for (Human) Brain Machine (Computer) Interface

 

A brain‐machine interface (BMI), also known as a Human-Computer Interface (HCI), is a system that establishes a direct communication channel between the human (or animal) brain and a computer or an external device. BMIs record or stimulate activity of the central (CNS) or peripheral nervous system (PNS) to replace, restore, enhance, supplement, or improve natural output/input. Thereby the BMI is able to change the ongoing interactions between the CNS and its external or internal environment. BMIs usually measure neural activity through sensors placed inside the brain or body (invasive or implanted technologies) or external sensors (non‐invasive technologies). This activity is processed in real‐time to extract information about the intentions or states of the subject, and then generate an action or stimulus in the external world that is provided as direct or indirect feedback to the user.

BMI systems are the product of integrating multiple technologies. They consist of systems for the acquisition and decoding of neural and biophysical signals to actuators providing sensory, mechanical, and electrical feedback to the user.

Ethical and social / legal concerns are being addressed by related IEEE guidelines for ethically aligned‐Intelligent systems, and the IEEE Brain Neuroethics framework.

BMI standardization should also consider regulatory frameworks for technology‐based systems (both clinical and consumer oriented) in fields like AI, IoT, and cybersecurity. A particular challenge in this aspect is the global disparity in regulatory approaches.

Standards for neurotechnologies

Currently, sensing and actuation technologies (e.g., IEEE 21451-1-2010 - ISO/IEC/IEEE Standard for Information technology -- Smart transducer interface for sensors and actuators -- Part 1: Network Capable Application Processor (NCAP) information model) are well standardized. These standards are mainly focused on safety aspects of those technologies. Data management Standards (e.g., various Standards from ISO / JTC 1/SC 32) cover aspects such as cybersecurity and data representation in medical applications.

On the other hand, system‐level aspects of BMI such as user needs and performance assessment require standards. Although there are existing standards regarding human factors and usability, they are not widely applied by the BMI research and development community.

Remarkably, recent advances in the field and the prospect of commercialization of both clinical and consumer‐oriented applications have motivated multiple efforts to develop guidelines and standards. An important milestone is the release of the FDA draft guideline on implanted brain‐computer interfaces in spring 2019.

To bring forth concerted efforts of Standardization, IEEE Working Group P2731 has recently been established to create a standard for Unified Terminology for Brain‐Computer Interfaces, while IEEE Working Group P2794 is working to formulate a Reporting Standard for in vivo Neural Interface Research (RSNIR), to serve as a framework for the precise, comprehensive reporting of human and animal research throughout the growing ecosystem of neurotechnology.

 

References

1.    K. Bowsher et al., “Brain‐computer interface devices for patients with paralysis and amputation: a meeting report.,” J Neural Eng, vol. 13, no. 2, p. 23001, Feb. 2016.

2.    Food and Drug Administration (FDA), “Implanted Brain‐Computer Interface Devices for Patients With Paralysis or Amputation—Nonclinical Testing and Clinical Considerations.” 2019.

3.    IEEE SA, Standards Roadmap: Neurotechnologies for Brain-Machine Interfacing, IEEE SA Industry Connections Activity No. IC17-007,New York, USA, 2020.

Terminology Standards for Health Information Exchange in the times of SARS-Cov2

To provide better and cost effective patient care, one needs to exchange healthcare information. For this to happen seamlessly, there is a dire need of Standards that facilitate this interoperability.

A Standard denotes the ability of two or more systems or components to exchange information (structural or syntactic interoperability) and to (meaningfully) use the information that has been exchanged (functional or semantic interoperability).

The EHR Standards, 2^nd edition, were notified by the Ministry of Health and Family Welfare, Government of India (MoHFW) in December 2016.

Subsequently, to give a boost to implementation of digital health in India, the National Digital Health Blueprint (NDHB) has been finally notified, and it also mentions a minimal set of standards to be used. It tries to define the standards required for ensuring interoperability within the National Digital Health Eco-system.

Also, now the Telemedicine Practice Guidelines have been notified by the MoHFW and NITI Aayog.

Categories of Standards for exchange of Health Information

The broad categories for Standards mentioned in the NDHB are those for Consent, (Clinical) Content, Privacy and Security, Patient Safety and Data Quality.

Currently for epidemiological purposes, all countries send reports to the WHO using the ICD classification system (current version is ICD-10, while ICD-11 has been formally released last year and will be applicable from January 2022). However, for getting better insights into the clinical data, SNOMED CT (a clinical terminology system) is the globally preferred standard and India has been a country member of SNOMED International since 2014. The basic differences of these two systems are summarized below.

•     ICD (International Statistical Classification of Diseases) codes, from the WHO, have limited scope and granularity, summarizes and aggregates data into broad categories (for epidemiological purposes), and are mono-hierarchical (Each code is grouped into a single grouping) –

•        No links to body sites or causes

•        Groups multiple clinical meanings  together using a single code

•        Does not always represent sufficient detail for clinical purposes

•      SNOMED CT is broader in scope, more granular, allows data to be grouped and aggregated in different ways (poly-hierarchical), and to be queried, based on Relationships between the Concepts. Also, since it is inherently logical, developing Clinical Decision Support Systems (CDSS) is also relatively easier with SNOMED-CT enabled systems.

Presently mappings are available from to SNOMED CT to ICD-10 and its various adaptations. Therefore, if any system is SNOMED CT enabled, it is possible to report according to ICD-10 or 11 as may be the statutory requirement for epidemiological and public health purposes.

Updating for SARS-Cov2

Now, with the world being gripped by a new Pandemic, the SDOs (Standards Development Organizations) have also geared up and come up with pertinent standards for this novel Corona virus or Covid-19 (Corona virus Disease 2019) or SARS-Cov2 (Severe Acute Respiratory Syndrome Coronavirus-2). The World Health Organization (WHO) has named the syndrome caused by this coronavirus “COVID-19”, and the International Committee on Taxonomy of Viruses (ICTV) has named the virus SARS-CoV-2.

The COVID-19 disease outbreak has been declared a public health emergency of international concern. The WHO has included it into the ICD system:

o    An emergency ICD-10 code of ‘U07.1 COVID-19, virus identified’ is assigned to a disease diagnosis of COVID-19 confirmed by laboratory testing.

o    An emergency ICD-10 code of ‘U07.2 COVID-19, virus not identified’ is assigned to a clinical or epidemiological diagnosis of COVID-19 where laboratory confirmation is inconclusive or not available.

o    Both U07.1 and U07.2 may be used for mortality coding as cause of death

o    In ICD-11, the code for the confirmed diagnosis of COVID-19 is RA01.0 and the code for the clinical diagnosis (suspected or probable) of COVID-19 is RA01.1.

A more detailed breakup for ICD-10 is available at: https://www.who.int/classifications/icd/COVID-19-coding-icd10.pdf?ua=1

SNOMED International has come out by placing the concept under the parent Human Coronavirus (Organism): Severe acute respiratory syndrome coronavirus 2 (organism) – SCTID: 840533007

840533007 | Severe acute respiratory syndrome coronavirus 2 (organism) |

  en   Severe acute respiratory syndrome coronavirus 2 (organism)
  en   2019-nCoV
  en   Severe acute respiratory syndrome coronavirus 2
  en   SARS-CoV-2
  en   2019 novel coronavirus

And, under Coronavirus infection (Disorder): Disease caused by severe acute respiratory syndrome coronavirus 2 (disorder) – SCTID: 840539006

840539006 | Disease caused by severe acute respiratory syndrome coronavirus 2 (disorder) |

  en   Disease caused by severe acute respiratory syndrome coronavirus 2
  en   COVID-19
  en   Disease caused by 2019 novel coronavirus
  en   Disease caused by 2019-nCoV
  en   Disease caused by severe acute respiratory syndrome coronavirus 2 (disorder)

The Regenstrief Institute that develops the LOINC codes, is developing Special Use codes in response to an urgent or emergent situation. These codes are based on the most up to date information available at the time of their creation. They have undergone the normal QA terminology process. LOINC supports their use in the unique situation that resulted in their rapid creation. However, be aware that downstream users may not be ready to handle prerelease codes until they are published in an official release. The emerging codes for Covid-19 are available at: https://loinc.org/sars-coronavirus-2/

Conclusion

The pandemic of SARS-Cov2 is evolving, and, so are the Standards related to the exchange of health information because of the disorder and / or organism. Once the situation stabilizes a bit, the unambiguity in the semantic exchange of such information will also become clear.

References

1. .      National Health Portal, Ministry of Health and Family Welfare, Government of India, EHR Standards. Available from: https://www.nhp.gov.in/ehr-standards-helpdesk_ms
2. .      Ministry of Health and Family Welfare, Government of India, National Digital Health Blueprint, 2019, Available from: https://main.mohfw.gov.in/sites/default/files/Final%20NDHB%20report_0.pdf (A compressed version is available at: https://main.mohfw.gov.in/sites/default/files/Final%20Report%20-%20Lite%20Version.pdf )
3. .      Ministry of Health and Family Welfare, Government of India, Telemedicine Practice Guidelines: https://www.mohfw.gov.in/pdf/Telemedicine.pdf
4. .      Sarbadhikari SN, The Role of Standards for Digital Health and Health Information Management, JBCR, 2019, 6(1):1: https://jbcr.net.in/JBCR-VOL-6-issue-1-2019-20/current-issues-volume-VI-issue-1-1.html

5. Sarbadhikari SN, Digital Health in India - as envisaged by the National Health Policy (2017), Guest Editorial, BLDE University Journal of Health Sciences, 2019, 4: 1-6.
6. SNOMED International, SNOMED CT Basics: https://confluence.ihtsdotools.org/display/DOCSTART/4.+SNOMED+CT+Basics
7. WHO, ICD-10: https://www.who.int/classifications/icd/covid19/en/
8. SNOMED International: http://www.snomed.org/news-and-events/articles/snomed-loinc-coronavirus-collaboration
9. SNOMED International: https://browser.ihtsdotools.org/
10. Regenstrief Institute, LOINC codes: https://loinc.org/prerelease/

Health Data Analytics and clinical terminology systems like SNOMED CT

Healthcare, with its inherent complexity, deals with large volumes of data coming in. Well designed and used EMRs (Electronic Medical Records) can collect huge amounts of data. However, neither the volume nor the velocity of data in traditional modern healthcare may qualify as big data now. Only a small fraction of the tables in an EMR database may be relevant to the current practice of medicine and its corresponding analytics use cases.

Certainly there will be variety in the data, but most of the EMR systems collect very similar data objects and models. However, new use cases supporting genomics and Internet of Medical Things (IoMT) will certainly require a big data approach.

Healthcare (data) analytics describes healthcare analysis activities that can be undertaken as a result of data collected from four areas within healthcare:

• Claims and cost data
• Pharmaceutical and research and development (R&D) data
• Clinical data (collected from electronic medical records (EMRs))
• Patient behavior and sentiment data (patient behaviors and preferences)

In health information management (HIM) — and in coding, specifically — the HIM professional must understand the importance of their role in interpreting and abstracting the data to be collected and analyzed. In other words, (health) data literacy is essential. While this data is used primarily in reimbursement and claims activities, it also plays a much larger role in clinical data analysis performed in facilities for quality of care reporting, disease management, and best care practices.

HIM Professionals are implementing coding data analytics to continually monitor their coding teams and cost-justify ongoing educational investments. Coding data analytics is a long-term commitment to improve coding performance for productivity and accuracy.

Elements that impact coding productivity data include: the type of electronic health record (EHR) used, the number of systems accessed during the coding process, clinical documentation improvement (CDI) initiatives, turnaround time (TAT) for physician queries, and the volume of non-coding tasks assigned to coding teams.

Accuracy should never be compromised for productivity. That may lead to denied claims, payer scrutiny, reimbursement issues, and other negative financial impacts. Instead, a careful balance between coding productivity and accuracy is considered best practice. Both data sets must be assessed simultaneously. The most common way to collect coding accuracy data is through coding audits and a thorough analysis of coding denials.

The fully-electronic 11th edition of the International Statistical Classification of Diseases (ICD-11) from World Health Organization (WHO) contains (epidemiological or morbidity and mortality causes) 55,000 codes, compared to the 14,400 in ICD-10.

SNOMED CT (India is a member of SNOMED CT and therefore it is available for use by anyone in India, free of cost) from SNOMED International contains 311,000 clinical concepts (including anatomical sites, disease diagnosis and procedures), with their descriptions, and more importantly, poly-hierarchical relationships.

To quote SNOMED CT:
"SNOMED CT is a clinically validated, semantically rich, controlled terminology designed to enable effective representation of clinical information. SNOMED CT is widely recognized as the leading global clinical terminology for use in Electronic Health Records (EHRs). SNOMED CT enables the full benefits of EHRs to be achieved by supporting both clinical data capture, and the effective retrieval and reuse of clinical information.

The term 'analytics' is used to describe the discovery of meaningful information from healthcare data. Analytics may be used to describe, predict or improve clinical and business performance, and to recommend action or guide decision making.

Using SNOMED CT to support analytics services can enable a range of benefits, including:

• Enhancing the care of individual patients by supporting:
• Retrieval of appropriate information for clinical care
• Guideline and decision support integration
• Retrospective searches for patterns requiring follow-up
• Enhancing the care of populations by supporting:
• Epidemiology monitoring and reporting
• Research into the causes and management of diseases
• Identification of patient groups for clinical research or specialized healthcare programs
• Providing cost-effective delivery of care by supporting:
• Guidelines to minimize risk of costly errors
• Reducing duplication of investigations and interventions
• Auditing the delivery of clinical services
• Planning service delivery based on emerging health trends

SNOMED CT has a number of features, which makes it uniquely capable of supporting a range of powerful analytics functions. These features enable clinical records to be queried by:

• Grouping detailed clinical concepts together into broader categories (at various levels of detail);
• Using the formal meaning of the clinical information recorded;
• Testing for membership of predefined subsets of clinical concepts; and
• Using terms from the clinician's local dialect.

SNOMED CT also enables:

• Clinical queries over heterogeneous data (using SNOMED CT as a common reference terminology to which different code systems can be mapped);
• Analysis of patient records containing no original SNOMED CT content (e.g. free text);
• Powerful logic-based inferencing using Description Logic reasoners;
• Linking clinical concepts recorded in a health record to clinical guidelines and rules for clinical decision support; and
• Mapping to classifications, such as ICD-9 or ICD-10, to utilize the additional features that these provide.

Analytics tasks, which may be enabled or enhanced by the use of SNOMED CT techniques, can be considered in three broad categories:

1. Point-of-care analytics, which benefits individual patients and clinicians. This includes historical summaries, decision support and reporting.
2. Population-based analytics, which benefits populations. This includes trend analysis, public health surveillance, pharmacovigilance, care delivery audits and healthcare service planning, and
3. Clinical research, which is used to improve clinical assessment and treatment guidelines. This includes identification of clinical trial candidates, predictive medicine and semantic searching of clinical knowledge. 

While the use of SNOMED CT for analytics does not dictate a particular data architecture, there are a few key options to consider, including:

• Analytics directly over patient records;
• Analytics over data exported to a data warehouse;
• Analytics over a Virtual Health Record (VHR);
• Analytics using distributed storage and processing; and
• A combination of the above approaches.

Practically all analytical processes are driven by database queries. To get the most benefit from using SNOMED CT in patient records, record-based queries and terminology-based queries must work together to perform integrated queries over SNOMED CT enabled data. To this end, SNOMED International is developing a consistent family of languages to support a variety of ways in which SNOMED CT is used. Clinical user interfaces can also be designed to harness the capabilities of SNOMED CT, and to make powerful clinical querying more accessible. Innovative data visualization and analysis tools are becoming more widespread as the capabilities of SNOMED CT content are increasingly utilized."

Therefore, now it is possible to make every kind of analytics and reporting results much more detailed than it used to be. Billing and coding companies that have adopted predictive analysis tools have received a considerably higher value return from mining their data.

HIM professionals must encourage the administration and policymakers to adopt SNOMED-CT enabled systems to get better informed and analyzed outcomes.

References:

1. https://www.healthcatalyst.com/big-data-in-healthcare-made-simple
2. https://www.healthcareittoday.com/2017/11/15/opening-the-door-to-data-analytics-in-medical-coding-him-scene/
3. https://www.osplabs.com/insights/data-mining-in-medical-coding-and-billing/
4. https://bok.ahima.org/doc?oid=302591#.Xj-2xvkzbDc
5. https://confluence.ihtsdotools.org/display/DOCANLYT/Data+Analytics+with+SNOMED+CT
6. https://confluence.ihtsdotools.org/display/DOC
7. https://jbcr.net.in/JBCR-VOL-6-issue-1-2019-20/current-issues-volume-VI-issue-1-1.html
8. https://www.healthcatalyst.com/the-case-for-healthcare-data-literacy
9. https://nnlm.gov/data/guides/data-literacy/course-materials
10. https://confluence.ihtsdotools.org/display/DOCANLYT/1+Executive+Summary

Professional Education for Digital Health

The term digital health is rooted in eHealth, which is defined as “the use of information and communications technology in support of health and health-related fields”. Mobile health (mHealth) is a subset of eHealth and is defined as “the use of mobile wireless technologies for public health”.

The newly proposed Global Strategy for Digital Health from the WHO is trying to define Digital Health as “the field of knowledge and practice associated with any aspect of adopting digital technologies to improve health, from inception to operation.”

Digital health interventions are applied within a country context and a health system, and their implementation is made possible by a number of factors. These include:

     (i)            the health domain area and associated content;

   (ii)            the digital intervention itself (i.e. the functionality provided);

 (iii)            the hardware, software and communication channels for delivering the digital health intervention; and, mediated within

 (iv)            a foundational layer of the ICT and enabling environment, characterized by the country infrastructure, leadership and governance, strategy and investment, legislation and policy compliance, workforce, standards and interoperability, and common services and other applications.

The National Health Policy 2017 (NHP-2017) of India correctly identified the need for creating many new institutions like the National Digital Health Authority (NDHA). Also, Health informatics education must be embedded as an integral part for health and hospital management. That will ensure a smooth adoption of digital health in India. India will then be recognized as a significant global player in digital health.

India has hosted the 4^th Global Digital Health Partnership Summit and the International Digital Health Symposium in the last week of February 2019. This also shows the commitment of India towards strengthening Digital Health Globally. Here the “Delhi Declaration” was adopted to accelerate and implement the appropriate Digital Health interventions to improve health of the population at national and sub-national levels, as appropriate according to national context.

Soon after, on 16^th April 2019, the World Health Organization (WHO) has released its recommendations of ten ways that countries can use digital technologies that people can improve their lives and essential services. Therefore, there is an imminent need for people, trained in digital health management, who can confidently handle a multitude of software services and help medical professionals, hospitals, healthcare organizations and common people. Courses on digital health are very new even globally and the career opportunities for early entrants are enormous.

Here I propose a 2-day or 12-hour interactive modular course for initiating health professional educators and administrators to the concepts and practice of digital health. I have been offering this course on-site, with suitable customization according to the needs of the institutes.

Course Objectives and Competencies

References

1.  Sarbadhikari SN, Sood JM. Gamification for nurturing healthy habits. Natl Med J India 2018; 31: 253-4 / Sarbadhikari SN, Sood JM. Gamification for nurturing healthy habits. Natl Med J India Available from: http://www.nmji.in/text.asp?2018/31/4/253/258236
2. Sarbadhikari SN, Will Health Informatics gain its rightful place for ushering in Digital India?, Indian Journal of Community Medicine, 2018, 43 (2): 126–127.
3. Sarbadhikari SN & Srinivas M, Health Informatics and Health Information Management, In, Gyani G & Thomas A, Eds, Handbook of Healthcare Quality and Patient Safety, Jaypee, New Delhi, 2^nded, 2016, Sec. 4, Ch. 17: 206-216.
4. Sarbadhikari SN, Medical Informatics: A Key Tool to Support Clinical Research and Evidence-based Medical Practice (Ch 15), In, Babu AN, Ed, Clinical Research Methodology and Evidence-based Medicine, 2^nd Ed, 2015: 179-191.
5. Balsari S, Fortenko A, Blaya JA, Gropper A, Jayaram M, Matthan R, Sahasranam R, Shankar M, Sarbadhikari SN, Bierer BE, Mandl KD, Mehendale S and Khanna T. Re-imagining health data exchange: An API-enabled roadmap for India. J Med Internet Res [Impact Factor 4.7], 2018. doi:10.2196/10725.
6. Ministry of Health and Family Welfare, Government of India, National Health Policy 2017. Available from: https://www.nhp.gov.in//NHPfiles/national_health_policy_2017.pdf
7. Sarbadhikari SN. Digital health in India – As envisaged by the National Health Policy (2017). BLDE Univ J Health Sci 2019;4: [In Press]
8. Sarbadhikari SN. Available from: https://blog.hcitexpert.com/2018/04/how-can-digital-health-be-implemented-in-NHP2017-Prof-Supten-Sarbadhikari.html - republished with permission from: http://supten.blogspot.com/2018/03/how-can-digital-health-be-implemented.html
9. National Health Portal, Ministry of Health and Family Welfare, Government of India, EHR Standards. Available from: https://www.nhp.gov.in/ehr-standards-helpdesk_ms
10. Ministry of Health and Family Welfare, Government of India and World Health Organization, India. Available from: https://www.gdhpindia.org/
11. Global Digital Health Partnership, Delhi Declaration, Available from: https://s3-ap-southeast-2.amazonaws.com/ehq-production-australia/25eb0facd90ee547c03071b005807288dbeac40b/documents/attachments/000/099/429/original/GDHP-Delhi_Declaration_Final.pdf?1551307009
12. World Health Organization; WHO guideline: recommendations on digital interventions for health system strengthening. Geneva: 2019. Available from: https://apps.who.int/iris/bitstream/handle/10665/311941/9789241550505-eng.pdf

“National” in my life!

After completing my +2 from St. Lawrence High School, I got admission to Calcutta National Medical College in 1984. Our classes started on the auspicious occasion of Teachers’ Day viz., 5^th September.

During my college years, I was fortunate to become the Senior Class Assistant in Physiology and Junior Class Assistant in OBG. Apart from that I also used to participate and conduct quizzes, and, conduct many perpendicular events during AGON. Along with Anindya Chaudhuri, I had represented our college in the very first Quiz Time (1985) conducted by Siddharth Basu in Door Darshan.

By the time we passed out, by July 1989, and then completed our internship, by July 1990, most of us were sure of our possible future careers.

Although I did house job (junior residency) in General (Internal) Medicine from August 1990 to July 1991, that discipline was my seventh career option! Somehow or other I felt that those twelve months were the best in my life. Later on I penned down that experience in a booklet – “হাসপাতালে একবছর” from মনফিকরা (July 2016).

Psychiatry was higher in my option list so I went to the Central Institute of Psychiatry, Kanke, Ranchi for junior residency during August – September 1991.

However, my topmost option was making a career in medical informatics. Unfortunately, in the early 90s, there was no training available for informatics in India. The foreign institutes that I wrote to wanted me to know the basics of computers. So I was thinking of getting enrolled in NIIT.

Meanwhile, the School of Biomedical Engineering, Institute of Technology, Banaras Hindu University, (then IT-BHU, now IIT-BHU), Varanasi was offering Senior Research Fellowships to MBBS graduates for pursuing PhD in Biomedical Engineering. Fortunately, I was selected to join the institute in October 1991 and got registered for the PhD from the session of March 1992. I could choose my PhD topic related to development of an (artificial neural network based) clinical decision support system for depression.

During this period (March 1992 – March 1995), I once came to visit our Alma Mater and met many of our teachers. One of the teachers whom I met, used to love me a lot, especially since Amit Behl and myself had represented our college twice – in the 4^th and 5^th years, in the Nestle Pediatric Quiz. He asked me that since now I am pursuing PhD in Biomedical Engineering, what will I do after that? I answered that I don’t know. I was shocked by his response to my answer. Generally a mild spoken person, he got very angry and told me that I should have never chosen a career where the future is uncertain!

I submitted my PhD thesis in March 1995 and was invited to write a Guest Editorial for JIMA (Journal of the Indian Medical Association). I wrote in May 1995 [Vol. 93(5): pp.165-6. PubMed PMID: 8834135] – Medical informatics – are the doctors ready?

Meanwhile I joined the Machine Intelligence Unit of Indian Statistical Institute in Calcutta as a Research Associate in a CSIR project under Prof. Sankar K Pal. Subsequently, I also did general practice in Durgapur for about a year and half, while my better half Dr. Anindya was a Medical Officer in CRPF, posted in Durgapur. I did a couple of short term contract jobs with South Eastern Railways and Bankura Sammilani Medical College during 1998-99.

The first formal academic job that I got was in Sikkim Manipal University from January 2000 I had joined as Assistant Professor in Biophysics in the department of Physiology. I was fortunate to teach the first batches of both MBBS (2001-02) and B.Tech (1997-98) – for the latter an elective in the 8^th Semester on Neuro-fuzzy computing. I was also the first Coordinator of the Distance Education Directorate there.

From there I joined as the first faculty member (Assistant Professor) of the School of Medical Science and Technology (SMST) in IIT Kharagpur in August 2002. Then I joined the Amrita University, Coimbatore in July 2004 as Associate Professor and started two courses – M.Sc. (Medical Informatics) in 2005 (the first of its kind in India) and M.Tech (BME) in 2007. In 2008 I became the Founding Chair of Biomedical Informatics in PSG Institute of Medical Sciences and Research, Coimbatore. There I started offering online courses on Health Informatics and had more students from abroad than from India.

During 2011-12 I became a Visiting Professor in Health Informatics in Bangladesh, with the support of the Rockefeller Foundation, for starting a Masters Course in Health Informatics there.

From January 2013, I joined as the first Project Director of the Centre for Health Informatics, National Health Portal, under the Ministry of Health and Family Welfare, Government of India. Apart from developing the National Health Portal from scratch, I was also instrumental in coordinating the eHealth activities. I have been a member of the EHR Standards Committee since 2010. I was also the Chairperson for revising the Concept Note on the proposed National eHealth Authority of India. Consequently, the National Health Policy of 2017 clearly mentioned the setting up of the National Digital Health Authority to facilitate the adoption of Digital Health in India. On another note I have also been associated with the inclusion Software as a Medical Device in the Medical Devices Act of 2017. I have also been actively associated with the Bureau of Indian Standards for adopting and developing Standards for Health Informatics and Active Assisted Living. In other words, the “National” of my college is still inspiring me to undertake many activities of “National” importance.

In August 2017, I briefly returned to academics as Dean (Academics and Student Affairs) and Professor (Health Informatics) in the International Institute of Health Management Research, Delhi.

I have been appointed to the Board of Governors of the Washington Medical Science Institute, Saint Lucia, English Caribbean. I am also IMA Honorary Professor (2017-20) and Distinguished Fellow, HITLAB. I am a Fellow and Faculty of PSG-FAIMER Regional Institute, and have been Chair of HL7 India (2011-13), and, President of the Indian Association for Medical Informatics (IAMI – 2016). I’m a Founder Member of HL7 FHIR Foundation.

Now I am an independent consultant on Digital Health Standards and look forward to reinforcing the Digital Health Activities in India and elsewhere.