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This article was originally published in Rest of World, which covers technology’s impact outside the West. The drone strikes on Amazon Web Services data centres in the Persian Gulf, and Iran’s threats to target tech firms for their involvement in US attacks
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How AI is reducing doctor burnout by fixing healthcare documentation in India
India's healthcare system struggles with a severe shortage of doctors, overloaded hospitals, and fragmented patient data. In a conversation on the ET AI Podcast, Anukriti Chaudhari, co-founder of fosterhealth.ai, explains how AI-driven clinical documentation can address these structural inefficiencies. By focusing on documentation workflows rather than diagnostics, her team is using AI to reduce administrative burden, improve data capture, and enable doctors to spend more time on patient care.
India's healthcare system operates under immense pressure. With a doctor-to-patient ratio of roughly 1:1600 against the World Health Organisation's recommendation of 1:1000, clinicians are stretched far beyond capacity. Most public hospitals see doctors consulting anywhere between 30 and 50 patients a day, leaving little time for documentation, reflection, or structured data capture. This is the core problem that fosterhealth.ai set out to address.
Anukriti Chaudhari, an IIT Bombay graduate, did not begin her career in healthcare. After briefly exploring consulting, she spent two formative years at ISpirit Foundation, where she gained exposure to large-scale digital public infrastructure initiatives such as Aadhaar and UPI. That experience shaped her understanding of systems-level innovation and made her acutely aware of how broken healthcare workflows were at the ground level. While the long-term vision was always healthcare transformation, the immediate challenge was deciding where to start.Rather than tackling complex medical decision-making, fosterhealth.ai focused on a burning and solvable problem: documentation. In Indian hospitals, patient information is often handwritten, fragmented, and lost after consultations. This makes it nearly impossible to analyse trends, conduct research, or even track patient history effectively. The lack of structured data is not due to negligence but due to time constraints and poor infrastructure.
It allows doctors to dictate clinical notes using a simple web or mobile application without requiring any additional hardware. Doctors can record after the patient leaves, which aligns better with real-world workflows where conversations are noisy, interrupted, and often conducted in non-medical language. The AI system processes voice inputs, uploaded reports, and other multimodal data to generate structured outputs such as prescriptions, discharge summaries, and clinical notes.
One of the key insights from on-ground deployments was that Indian accents posed a greater challenge than Indian languages. Doctors often prefer speaking after consultations using medical terminology rather than recording conversations live. Western-trained models struggled with Indian patient names, drug names, and accents, leading the team to rely on open source models customised with Indian datasets. Doctors can also correct vocabulary over time, allowing the system to learn patterns much like predictive text on smartphones.
The value proposition is immediate. Tasks that previously took an hour, such as preparing discharge summaries, can now be completed in two to three minutes. Reviewing patient reports for second opinions, which could take over ten minutes, is reduced to one or two minutes. This efficiency directly translates into doctors being able to see more patients or spend more time on care rather than paperwork.
Given the sensitivity of healthcare, the team built multiple guardrails into the system. The AI does not generate information beyond the source inputs. Every output is traceable back to the original audio or documents, ensuring transparency and preventing hallucinations. Borrowing concepts from autonomous driving, fosterhealth.ai operates at a level where AI assists but does not replace human oversight.
To validate performance, the team conducted a yearlong real-world evaluation with over 300 patients in collaboration with a major cancer hospital. The results showed zero hallucination, a 45 per cent reduction in documentation errors compared to manual notes, and a 79 per cent reduction in documentation time. Doctors rated the outputs highly on familiarity and usability, reinforcing trust in the system.
On the regulatory front, it aligns with principles of data sovereignty, security, and privacy. Patient data remains within Indian geography, follows strict access controls, and complies with frameworks such as HIPAA and India's evolving DPDP guidelines. While AI regulation in healthcare is still maturing, the company prioritises adherence to both the letter and spirit of emerging laws.
Starting with documentation, it demonstrates a pragmatic approach to AI in healthcare. Instead of chasing futuristic promises, it focuses on solving a real problem today, building trust with doctors, and laying the foundation for more advanced healthcare intelligence tomorrow.Add as a Reliable and Trusted News Source Add Now!
Source: EconomicTimes
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