Byline:
By focusing on one of healthcare’s most overlooked problems—documentation—Foster AI is using AI to reduce doctor burnout and quietly build India’s healthcare data foundation.
Article:
India’s healthcare system does not suffer from a lack of intent. It suffers from a lack of time.
With a doctor-to-patient ratio of roughly 1:1600—far below the World Health Organisation’s recommended 1:1000—clinicians in public hospitals routinely see 30 to 50 patients a day. In that environment, documentation becomes the casualty. Notes are rushed, handwritten, fragmented or lost entirely, not because doctors don’t care, but because the system leaves no room for anything beyond immediate care.
This is the problem Foster AI decided to tackle—not diagnostics, not predictive medicine, but documentation.
Anukriti Chaudhari, Foster AI’s co-founder, did not start her career in healthcare. An IIT Bombay graduate, she spent time in consulting before working at the iSpirit Foundation, where she was exposed to India’s large-scale digital public infrastructure efforts such as Aadhaar and UPI. That experience shaped her understanding of how foundational systems enable scale—and how absent such foundations are in healthcare.
“The long-term vision was always healthcare,” Chaudhari has said. “But it became clear that without structured data, everything else is built on sand.”
Instead of attempting to replace doctors or automate clinical decision-making, Foster AI took a narrower—and more pragmatic—approach. The company built an AI-driven documentation platform that allows doctors to dictate notes after consultations, using a simple mobile or web interface. The system processes voice inputs, uploaded reports and other clinical data to generate structured outputs such as prescriptions, discharge summaries and clinical notes.
One insight from early deployments stood out: accents were a bigger challenge than languages.
Doctors often prefer to speak after consultations, using medical terminology rather than recording conversations live. Western-trained models struggled with Indian accents, patient names and drug references. Foster AI responded by customising open-source models trained on Indian datasets, while allowing doctors to correct vocabulary over time—similar to how predictive text adapts to individual users.
The payoff was immediate. Tasks that previously took up to an hour, such as preparing discharge summaries, could be completed in minutes. Reviewing patient histories for second opinions dropped from over ten minutes to one or two. For doctors, this translated directly into reduced administrative burden and more time for patient care.
Given healthcare’s sensitivity, Foster AI built strict guardrails into the system. The AI does not generate information beyond source inputs, and every output is traceable back to the original audio or documents. The company describes its approach as assistive rather than autonomous—borrowing from the human-in-the-loop philosophy used in autonomous driving.
To validate its impact, the team conducted a year-long real-world evaluation involving more than 300 patients in collaboration with a major cancer hospital. According to the company, the study showed zero hallucinations, a 45 percent reduction in documentation errors and a 79 percent reduction in documentation time compared to manual notes.
Regulatory alignment was treated as a design constraint rather than an afterthought. Patient data remains within Indian geography, follows strict access controls and aligns with frameworks such as HIPAA and India’s evolving DPDP guidelines.
Foster AI’s decision to focus on public hospitals was deliberate. Nearly 80 percent of India’s patients receive care through government institutions, where scale, complexity and data gaps are most pronounced. The company is now expanding deployments beyond its initial pilot at Tata Memorial Hospital to institutions including AIIMS, PGI and JIPMER.
In an ecosystem often driven by rapid scaling and headline-grabbing claims, Foster AI represents a quieter model of healthcare innovation—one that prioritises trust, workflow fit and measurable impact. By fixing documentation first, the company is laying the groundwork for a future where India’s healthcare system finally has a reliable memory.
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This story was distributed as a release by Sanya Kapoor under HackerNoon’s Business Blogging Program.
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