Monsoon 2024

History of Biomedicine: Germ Theory to Digital Health

Semester- Monsoon 2024 | HIS-2511

Course Instructor- Professor Projit Bihari Mukharji

Course Overview:

This course explored the history of modern biomedicine over the last 150 years. Its intention, however, was not simply to provide a chronology of technological advances. Rather, it showed how historical analysis can open up new ways of thinking about the relationship between medicine and society. We saw during the recent pandemic that medical matters are seldom, if ever, only about developments in the laboratory. 

The clinician, the nurse, the pharmacist, the vaccine manufacturer, the politician, the policeman and even the odd religious figure, not to mention the patient and her family, are all crucial to the way medical events play out in society. Each of these characters and their mutual relationships are structured by history. It is these historically evolving relationships that this course explored. 

Additionally, a sub-theme of the course was also to look at the role of technology in mediating the relationship between health and society. It did this by using a small set of case studies around key moments in modern medical history such as the development of Germ Theory, Randomized Clinical Trials, Transplants, Genetic Screening, MRIs, Telemedicine and Digital Health.

Introduction to Health Informatics

Semester- Monsoon 2024 | BIO- 3515/BIO-6515-1

Course Instructors- Vibhu Agarwal- Visiting Faculty at KCDH-A
Dr. Rintu Kutum- Faculty Fellow at KCDH-A

Course Overview–

This was an introductory course on Health Informatics, where students covered existing health informatics tools within hospitals to capture and safeguard patient health data to build innovations (research and development) for better patient care. This course provided an introductory level understanding of health informatics such as electronic medical records (EMR), electronic health records (EHR), medical vocabularies (SNOMED CT, LIONIC, ICD9 etc), the data model for interoperability, and most importantly how to integrate innovations (research and development) on top EHR systems. 

Additionally, students learnt how health informatics is an integral part of medicine and biology concerning terminologies, ontologies, vocabularies, and interoperability, and informatics is an essential component towards knowledge synthesis and novel insight generation towards better understanding human health and disease in clinical and non-clinical settings. Also, students learnt about various statistical machine learning methods used for health research, their pros and cons; along with a couple of advanced machine learning methods that can be applied to clinical decision support systems and clinical trials. 

The lectures were segmented into technology [T] and innovation [I] on top of the technology used in health informatics, along with a couple of introductory advanced topics [*].

Learning Outcome-

• Students learned about existing information technology for healthcare such as electronic health records (EHR), and electronic medical records (EMR); and how they work from the point of view of hospital administration, physicians/clinicians, nurses and patient care with the hospital ecosystem and beyond hospital ecosystem services such as mobile technologies.

• Based on the learning, hands-on programming session and resources discussed, students in groups (maximum of 3 students) needed to build any components of a synthetic EHR system as a prototype using existing open source technologies available based on the group’s strength, motivation and ambition.

• Appreciated the need for reliable evidence-generation methodology that can inform
  clinical decision-making

• Understood the differences between different study designs and the methods of interpreting a study results and the statistical principles underlying such inference
  • Prospective versus Retrospective
  • Experimental versus Observational

• Appreciated the pros and cons of observational studies as methods for generating medical evidence, the challenges in inferring causality

• Appreciated how experimental results may be interpreted within a probabilistic framework, the limitations of the experimental method and safeguards needed for experimenting with human subjects.

• Understood commonly used observational designs, their strengths and limitations and key statistical considerations in their design.

• Appreciated and got an overview of common approaches for adjusting for exposure likelihood and confounding.