Enhancing health record digitization using AI-powered ambient clinical intelligence solutions - a pilot study

The digitization of health records plays a crucial role in reducing errors, such as missing data commonly associated with paper records, standardizing care, and facilitating the adoption and monitoring of evidence-based management guidelines. However, the extent of digitization in the Indian healthcare system is limited, primarily due to the high patient volume managed by healthcare professionals. The disparity is evident in the number of physicians per 1,000 population—0.7 in India compared to 3.6 in the United States—resulting in limited time for detailed patient documentation. Thus, there is a pressing need for solutions that enable digitization of medical records without imposing additional manual effort on clinicians and nurses.

To address this, we propose the development of artificial intelligence (AI) models capable of executing speech processing and natural language understanding tasks to generate structured clinical notes based on ambient conversations between clinicians and patients. This study aims to create an AI-driven clinical intelligence solution for real-time digitization of healthcare records during interactions between healthcare professionals and patients.

The study will be conducted in four phases:

Phase I - Audio Capture: Establishing high-fidelity audio capture systems.

Phase II - Multilingual Transcription: Implementing technology for real-time transcription in multiple languages.

Phase III - Clinical Note and Template Generation: Developing AI models to convert transcriptions into structured clinical notes and customized templates.

Phase IV - Deployment and Trial: Integrating and testing the technology within existing healthcare infrastructure.

The proposed plan involves co-developing the application to align with the existing infrastructure and workflows at Tata Memorial Hospital. This will include the installation of robust audio capture systems, the integration of multilingual transcription capabilities, and the generation of clinical notes in preferred formats.

The study will also evaluate the impact of the co-developed technology in terms of its technical robustness and operational efficacy. Key stages of development include setting up audio capture infrastructure, developing technology for audio-to-English translation, creating AI-driven clinical note generation systems, ensuring data privacy and security, and designing custom form templates for clinical documentation.