Foster AI Targets Clinical Documentation Gaps in India’s Public Hospitals

Clinical documentation remains a persistent challenge across India’s public hospitals, where high patient volumes, staffing constraints and fragmented record systems often place pressure on clinicians. Delays or inaccuracies in documentation can affect clinical decision-making, medication safety and the quality of data used for research and policy planning.

Foster AI, a two-year-old health technology startup, is attempting to address this issue through an AI-based clinical documentation platform designed specifically for public-sector hospital environments. According to the company, its product processes multiple forms of clinical input—including PDFs, handwritten notes, typed electronic health record (EHR) entries and voice recordings—and converts them into structured clinical notes and forms.

Development and early deployment

The startup said its technology was developed in close collaboration with Tata Memorial Hospital, where it was initially piloted and refined. As per Foster AI, the system has since been deployed across four public health institutions. The company added that documentation quality was evaluated through a prospective clinical study covering 300 patient encounters, in which clinicians independently assessed the AI-generated notes.

Following the Tata Memorial engagement, Foster AI said it has secured contracts with other public institutions, including AIIMS Delhi, PGI Chandigarh and Lady Hardinge Medical College. Adoption of digital health tools in the public sector is often slowed by lengthy procurement processes and multi-layered approvals, making scale beyond pilots relatively uncommon.

Focus on documentation

The founders said the company’s early strategy was to focus narrowly on documentation rather than broader clinical decision-support tools. “We focused on documentation first because it is foundational to everything else,” said Anukriti Chaudhari, co-founder of Foster AI.

During its initial phase, the startup chose to work with a single institution instead of pursuing multiple pilots in parallel. According to the company, the team spent extended periods observing outpatient department workflows, testing transcription accuracy, refining note structures and adapting the system to fit clinical routines. Early development was centred on OPD use cases before being evaluated across a broader set of encounters.

When expanding beyond the pilot, Foster AI deliberately limited its focus to public-sector hospitals. The company noted that public institutions differ significantly from private healthcare settings in patient volumes, infrastructure constraints and incentive structures. Public-sector clinicians often see significantly more patients per day and operate within tighter operational and procurement frameworks.

The founders acknowledged that implementation cycles in public health are typically long, often stretching from 12 to 24 months and involving multiple stakeholders. According to the company, its current deployments reflect the first phase of adoption, laying the groundwork for wider and more mature rollouts.

What comes next

Looking ahead, Foster AI said it is exploring additional use cases such as disease registries, clinical trial documentation, discharge summaries and tumour board reporting. The company maintains that sustained engagement with clinicians and administrators will be essential for broader adoption within India’s public healthcare system.