Top amputation surgeon had own legs removed due to fetish. Were patients safe?

📝 Discussion Summary (Click to expand)

Potential surgical fetish & conflict of interest
“Would you be comfortable later finding out that the surgeon who amputated your leg had an amputation fetish?” — simonw
Core concern is insurance fraud, not fetish speculation
“The only issue here is the insurance fraud, if that happened.” — stavros
Media framing and correlation hype
“Could ask this about most news stories these days. They are so bold as to not even hide the opinionated framing anymore.” — tomasphan

🚀 Project Ideas

Generating project ideas…

A web‑based analytics platform that ingests de‑identified surgery logs to flag surgeons with statistically anomalous amputation patterns, helping hospitals detect potential bias or fetish‑related behavior.
Provides an easy‑to‑interpret risk score and visualizations for administrators, insurers, and regulators.

Key	Value
Target Audience	Hospital risk managers, medical board investigators, insurers
Core Feature	Real‑time statistical anomaly detection on amputation rates + drill‑down to surgeon‑level data
Tech Stack	Python (pandas, scikit‑learn), PostgreSQL, React front‑end, Docker deployment
Difficulty	Medium
Monetization	Revenue-ready: $199/month per institution

Directly addresses HN concerns about hidden fetish influence and lack of transparency.
Offers concrete utility for auditing and preventing insurance/medical‑fraud issues.
Sparks discussion on data‑driven accountability in surgery.

Browser extension that scans public forum posts, surgeon profiles, and review sites for fetish‑related terminology while cross‑referencing with professional credentials.
Generates a “risk flag” when suspicious language patterns correlate with a surgeon’s name, alerting users and moderators.

Key	Value
Target Audience	Patients, medical journalists, online community moderators
Core Feature	NLP‑based keyword and sentiment analysis with a reputation scoreboard
Tech Stack	Node.js backend, ElasticSearch, React extension, OpenAI API for context filtering
Difficulty	High
Monetization	Hobby

Mirrors HN users’ frustration about undisclosed fetishes influencing medical decisions.
Provides a practical tool for community self‑policing and early warning.
Encourages dialogue about ethical boundaries in professional communication.

Mobile/web app that presents patients with a concise overview of a surgeon’s amputation history, outcomes, and any flagged anomalies before they consent to surgery.
Integrates risk scores, patient testimonials, and independent expert reviews into a single decision‑support view.

Key	Value
Target Audience	Prospective amputation patients, patient advocates, tele‑health platforms
Core Feature	Interactive risk dashboard with patient‑specific outcome predictions
Tech Stack	Flutter (mobile), Firebase backend, TensorFlow.js for outcome modeling
Difficulty	Medium
Monetization	Revenue-ready: freemium with $9.99/month premium for full analytics

Solves the “why care” question by giving patients actionable data on surgeon behavior.
Generates discussion about informed consent and the need for transparent surgeon vetting.
Potential to reduce fraudulent claims by empowering patients with objective info.