Friday 13 September 2024

Doctor's Moat Over Time, While Saving Lives

My town.


1960 - Local Vaidhya

- Moat: The only available healer within a 20 km radius.  

- Insight: Exclusivity based on access and trust in traditional knowledge.


1980 - First College-Educated Doctor (BAMS)  

- Moat: Higher education credentials in Ayurveda, standing out as the first formally trained doctor in town.  

- Insight: Early adopters of formal education create a new level of trust.


1980s - Public Health Center (PHC)

- Moat: Government-provided healthcare with free services.  

- Insight: Accessibility to formal care is democratized, but only for basic treatments.


1990 - Occasional Visiting MBBS Doctor

- Moat: Limited competition, as visiting MBBS doctors are rare in small towns.  

- Insight: Scarcity of modern medicine professionals retains exclusivity.


2000 - Local Mix of Doctors (BAMS, BHMS, MBBS)

- Moat: Relationships and trust-building become critical as the number of local doctors grows.  

- Insight: Personal connections with patients ensure loyalty amid growing competition.


2010 - Introduction of Specialists

- Moat: Specialized care draws patients to cities with an increasing density of clinics offering niche treatments.  

- Insight: Specialization and centralization begin to take hold, shifting focus away from generalists.


2020 - Patient Shift to City Centers

- Moat: Marketing, celebrity status, and referral commissions differentiate successful doctors in competitive urban environments.  

- Insight: Patients opt for expertise over convenience, even battling to get appointments.


2025 - Arrival of Large Corporate Hospital Franchises 

- Moat: Comprehensive care under one roof with standardized quality and reputation.  

- Insight: Corporate setups cater to the patient’s need for assurance, convenience, and variety in one place.


2030 - Corporate Competition Intensifies

- Moat: Modular, process-based setups emphasize efficiency, insurance funding, marketing, and referral networks.  

- Insight: Patient interaction with the system rather than specific doctors; process consistency becomes key.


2035 - AI and Automation in Healthcare  

- Moat: Cost-cutting and speed via automation ensure higher patient flow without compromising care quality. Patients who desire more human interaction pay a premium for personal expertise.  

- Insight: The system becomes highly efficient, but personalized care turns into a luxury, available only at a higher cost.


2040 - The Future of Healthcare


Let see how it develops in coming years

Sunday 8 September 2024

Just another LLM analytics but for our highly complex case of a rare unidentified illness.

 Keywords

- Clinical complexity

- Undiagnosed illness

- Slow medicine

- Narrative History


Access detailed case record here - https://classworkdecjan.blogspot.com/2019/05/42-f-with-severe-regular-edema-with_17.html


Summary of analysis

Giving my opinion as scoring for performance of ChatGPT along with consideration of bias (limitation) that it have to reply in just a few short paragraphs.


Thematic analysis - 3/5

Critical analysis - 3/5

Research questions - 2/5


Subjective ratings, cutting marks for 

- losing themes of emotional pain

- critical analysis no evidence/ insights about any interventions

- no new Research questions other than what's directly noticeable in text.


Giving marks for

- good summary and prioritization of themes.

- covering wide range of important aspects in critical analsys.

- able to formulate research questions that are prioritized in text.


Usability -

- For usefulness to get clinical understanding/outline quickly - 4/5.

- For usefulness to get actionable (preferably evidence backed) insight - 1/5

Weakness - brute force strategy of summarizing literature, but no individualized exploration.


For speed - 100/5 🤣