OpenHealthAdvocate_Meera
Digital Health Researcher · IIT background
@FamilyDoc_Srinivas this is such a valid point and there ARE some efforts — ABDM (Ayushman Bharat Digital Mission) is supposed to create an interoperable health ID system that even small clinics can plug into. eSanjeevani already has basic AI triage features for teleconsultation. But youre right, most AI in healthcare India investment goes to diagnostics for private chains. WhatsApp-based AI symptom checkers are actually gaining traction in semi-urban areas — low infrastructure, high penetration. That might be the actual path for tier 3 and below.
FamilyDoc_Srinivas
General Practitioner · Semi-urban clinic · Andhra Pradesh
All this sounds great but what about AI in primary care at smaller setups? We dont have big EHR systems, half our records are still paper. The digital health divide in India between metro hospitals and rural/semi-urban clinics is massive. AI clinical tools keep getting built for AIIMS-level institutions. What about a GP with 80 patients a day and one computer? Is there any low-cost AI health tool actually targeting this segment or everyone just chasing the premium market?
BuildHealthIT_Kavya
Health IT Developer · EHR integrations
NLP in EHR is genuinely one of the most underrated AI healthcare applications right now. We built an NLP layer that reads free-text doctor notes and auto-populates structured fields — saves avg 40 mins of documentation per physician per day. That's the AI physician burnout solution nobody's talking about. Physician burnout costs US healthcare an estimated $4.6B/year — if ambient AI documentation tools like Nuance DAX and Suki AI can cut admin load, the ROI is massive. Way more impactful than another dashboard IMO.
ThomasWren_MD
Internal Medicine Physician · USA
70% override rate is actually... pretty normal unfortunately. Studies on CDSS alert fatigue consistently show override rates of 60-90% across US hospitals. The problem isnt the AI per se — its that most alerts are set to maximum sensitivity during deployment because no one wants liability for a missed warning. You need a clinical informaticist to go through and ruthlessly cut low-value alerts. The JAMA Network published a good piece last year showing that reducing alerts by 40% actually improved response rate to the remaining ones by 22%. Less is more.
HospitalAdmin_Geetha OP
Hospital Administrator · 300-bed private hospital
We upgraded our EHR (Electronic Health Records) system to include AI-powered CDSS (Clinical Decision Support System) 6 months ago. Doctors are complaining about alert fatigue — too many pop-up warnings that they end up clicking through without reading. Our chief of medicine says 70% of alerts are being overridden. Is this a config problem or is AI in clinical decision making just not mature enough for real hospital workflows? Would love to hear from anyone who's actually cracked this.
