Summary:
The MD thesis project system in tier 2 medical colleges in India is increasingly challenged, with students and faculty struggling to navigate the process. A proposed solution involves optimizing study design, prospective data capture, and thematic analysis using collective medical cognition tools and AI-driven qualitative thematic analysis.
Key words:
1. MD thesis projects
2. India
3. Study design optimization
4. Prospective data capture
5. Collective cognition tools
6. AI-driven qualitative thematic analysis
7. Medical education
8. Research methodology
UDLCO Keyword glossary:
MD thesis projects submitted in India has become a quagmire and often hated by faculty and students both as a sore point that continues to be enforced by policy makers as a mandatory requirement to obtain the MD degree and most tier 2 medical colleges in India just pay superficial lip services to these thesis projects as also shared earlier in the UDLCO linked here: https://
One of the biggest reasons for the problem is that faculty and students are largely left to their own survival strategies to execute their thesis workflow and learn to fend for themselves, anecdotally often believed to learn to utilise short cuts that do more harm than good to their overall learning experience.
Solutions:
1) Optimizing Study Design:
Real patient centered qualitative study design with quantitative descriptions (mixed methods) that enables easier integration with existing patient care workflows.
Background theory: https://
2) Optimizing prospective data capture utilising collective cognition tools:
This tackles one of the biggest issues of the system's current inability to offer the lonely post graduate resident learner a collective cognitive support that is also transparent and accountable.
Each patient data is begun to be captured as soon as the first encounter happens in the outpatient or inpatient setting and the patient is followed up through a system of PaJR and CBBLE where a team of patient centered advocates, faculty, students, interns and post graduate resident learners make the data grow with time through team based learning as illustrated here:
Sample PaJR data of individual thesis patients in NMC dynamic E logged case report links:
The above single patient's data with clinical complexity and comorbidities can fit into multiple ongoing thesis projects as illustrated here:
1) Factors influencing sepsis outcomes:
2) Trunkal obesity and biopsychosocial factors influencing outcomes:
3) Diagnostic and therapeutic factors influencing outcomes of patients with anemia in chronic renal failure
4) Spectrum of clinical presentations in diabetes with multimorbidities and factors influencing their outcomes
The above single patient's data with clinical complexity and comorbidities can fit into multiple additional ongoing thesis projects as illustrated here:
4) Factors influencing the development of heart failure and other outcomes in patients with suspected chronic CAD
6) Factors influencing recovery outcomes in patients with respiratory failure
More ongoing thesis here: https://
More about NMC dynamic E logs here:
More about PaJR and CBBLE here:
3) Thematic analysis sample drafts of thesis projects analysed and submitted to university:
Inspite of the above simple strategy that involves regular participation through a collectively supported ecosystem it's still cumbersome for post graduate residents to warm up to the above collectively supported transparent and accountable workflow and many of them simply resort to collecting very sketchy data alone in limited paper based
case report forms and then retrospectively analysing those limited data where large part of the patient's could be missing. Last year we managed to upgrade this paper based limited thesis data collection by utilising EMR summaries prepared by the interns during each patient's discharge and while these were still far from the quality of data desired it was very useful in faster thematic data analysis using current AI LLM tools.
Sample final assessment and certification of the thesis:
Previous templates: https://
Submitted template:
This is to certify that this dissertation titled ‘‘... outcomes in patients with ... disease in chronic ... disease" is a bonafide research work done from Mid 20xy - Mid 20xz under my guidance by Dr. ABC ( Reg No: abcefgD )
The quality and validity of the data captured from each case, including patient outcomes in the study, may have been enhanced if it had been shared with our team regularly from the first and subsequent clinical encounters and not just before final submission deadline. This work was done in partial fulfilment of regulations laid down by xyz University of Health Sciences for MD General Medicine Degree Examination to be held in mm/DD/yy
Date:
Place:
Professor
Department of General Medicine
Thematic Analysis of the entire above write up:
The conversation can be grouped into several themes:
1. _Problem Statement_: The MD thesis project system in India is flawed, with students and faculty struggling to navigate the process.
2. _Solutions_: Optimizing study design, prospective data capture, and thematic analysis using collective cognition tools and AI-driven qualitative thematic analysis.
3. _Collective Cognition Tools_: Utilizing tools like PaJR and CBBLE to facilitate collective cognition and support students in their thesis projects.
4. _AI-Driven Qualitative Thematic Analysis_: Leveraging AI tools to support thematic analysis and improve the quality of thesis projects.
5. _Medical Education and Research Methodology_: The need for improved medical education and research methodology in India, particularly in the context of MD thesis projects.
Codes:
1. MD thesis projects
2. Study design optimization
3. Prospective data capture
4. Collective cognition tools
5. AI-driven qualitative thematic analysis
6. Medical education
7. Research methodology
8. Problem statement
9. Solutions
Insights:
1. The MD thesis project system in India is in need of reform.
2. Optimizing study design, prospective data capture, and thematic analysis can improve the quality of thesis projects.
3. Collective cognition tools and AI-driven qualitative thematic analysis can support students in their thesis projects.
4. Improved medical education and research methodology are essential for producing high-quality thesis projects.
Learning Points:
1. The importance of optimizing study design, prospective data capture, and thematic analysis in MD thesis projects.
2. The potential benefits of using collective cognition tools and AI-driven qualitative thematic analysis in supporting students in their thesis projects.
3. The need for improved medical education and research methodology in tier 2 medical colleges in India, particularly in the context of MD thesis projects.
4. The importance of addressing the problem statement and finding solutions to improve the MD thesis project system in India.
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