The IndiMR Vision
  • A Proposal to Revolutionize India’s Healthcare
  • What Do We Propose?
  • Problems
    • Lack of Medical Facilities and Expertise
    • Lack and Unavailability of Medical Records
    • Lack of Data Standards and Interoperability
    • Increased Costs to People and Organizations
    • Lack of Reliable Data for Policy and Medical Research
    • Poor Spread of Health Insurance
    • Pilferage, Corruption, Fraud and Inefficiencies
  • General Contours of the Proposed Project
    • Why Open Source?
  • India’s Unique Position, Why India? Why Now?
  • Requirements and Unique Challenges
    • mHealth Centric
    • Blockchain Based
    • Knowledge-Based System – Separation of Knowledge from Software
    • Flexible and Composable
    • Collaboration and Workflow Orientation
    • Role of Artificial Intelligence
    • Integration of Miscellaneous Healthcare Associated Processes
    • Force Multiplier Effect – Orchestra Model
  • Benefits for India
    • Improved Healthcare for Indians
    • Public Health Impact
    • Health and Healthcare Policy Research
    • Spurt in Technology Innovation
    • Boon for Private Sector
    • Boost to Insurance Sector
    • Standards-Based Approach
    • Job Creation in Healthcare
    • Centralized Functions with Economies of Scale
    • Increased Soft Clout for India
  • Funding for Pilot Project and the Prototype System
  • Counter Arguments
    • "Indian Healthcare has so many basic problems, why not solve them first?"
    • "But This Has Already Been Done!"
  • Conclusions
  • Authors
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Authors

PreviousConclusions

Last updated 6 years ago

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Hemant Shah

Hemant Shah trained and worked as an Obstetrician and Gynecologist in India, with his chief interests being high risk pregnancies and operative gynecology. While working as an OBGYN physician, he co-founded and was the Executive Director and CTO of a medical informatics company in India. He led the development of several web-based applications for physicians in this role. He also developed an advanced Prenatal Record System to help clinicians better manage their patients.

He is the author of the (http://www.proteme.org) for automated clinical decision support guidelines. Proteus (PROcesses and Transactions Editable by USers) is a model that allows creation of clinical guidelines with modular knowledge components. Each knowledge component represents a clinically identifiable activity and is available to the clinician as executable knowledge. He also leads an open source project to develop software tools based on Proteus.

In 2000, he joined the Lister Hill Center at National Library of Medicine in Bethesda, Maryland, as a Medical Informatics Research Fellow. 2003-2007, he served as Information Scientist at City of Hope National Medical Center (COH). From 2007 to 2017, he worked at Henry Ford Health System (HFHS) as Senior Research Informatician, where he led the project to develop a fully integrated clinical decision support for HFHS's EHR system, within the Semantic Data Capture Initiative project. This system achieved high level of user acceptance while meeting all its technical and research goals and was completed within time and budget.

His special interests in Medical Informatics are technologies, and tools that directly impact clinician’s performance, particularly Clinical Decision Support Systems, Clinical Information Systems and semantic interoperability for clinical systems. He specializes in metadata and ontologies and has led the Metadata project for the caBIG CTMS workspace at COH, as well as contributed to the vocabulary and metadata needs of several projects.

He possesses the range of skills required to create prototype systems, including research, conceptualizing, modeling in UML and writing of programming code, deployment and testing of tools. He is well versed with standards like HL7, ISO/IEC 11179 and controlled medical vocabularies.

He is a certified Epic EMR expert and has the technological know-how to manage its Ambulatory system, as well to create and integrate new solutions with one of the world’s most successful EMR system – Epic.

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Proteus model