INNO 76240.1 DocuQuality
There is a consistent increase in the clinical documentation burden. In this project, we want to explore the potential of generative artificial intelligence (AI) to support the clinical documentation process.
Factsheet
- Schools involved School of Engineering and Computer Science
- Institute(s) Institute for Patient-centered Digital Health (PCDH)
- Research unit(s) PCDH / AI for Health
- Funding organisation Innosuisse
- Duration (planned) 04.02.2025 - 03.08.2025
- Head of project Prof. Dr. Kerstin Denecke
- Partner Cistec AG
- Keywords Large language model, Medical documentation, Artificial Intelligence
Situation
Time efforts spent into documentation tasks limit the time available for clinical decision making, and interaction with patients. This excessive focus on documentation often interferes with direct patient care, with approximately 80 % of physicians acknowledging that the time and effort required for these tasks interferes with their ability to provide quality care. On average, physicians spend nearly two hours per day on documentation outside of regular working hours, with those participating in value-based payment models reporting even higher burdens.
Course of action
The project will answer three questions: 1) How effectively can large language models (LLMs) identify and address missing, incorrect, or contradictory data needed to generate accurate and high-quality medical reports? This includes the development of a conversational, agent-based tool for supporting the generation of clinical documents and measure quality as well as potential resource savings. 2) What are the key considerations and challenges in developing and prototyping such a tool in a specific domain? We will test a prototypical implementation in real-life clinical context, focusing on a specific clinical domain (e.g., emergency or internal medicine) in collaboration with a Swiss hospital. 3) What are the trade-offs between locally hosted non-proprietary LLMs and proprietary solutions (e.g., Microsoft Azure) in terms of accuracy, adaptability, and cost-efficiency for clinical documentation tasks? This analysis will consider accuracy, adaptability, and cost-effectiveness.
