IHI Lucian Leape Institute – Patient Safety and Artificial Intelligence: Opportunities and Challenges for Care Delivery (May 2024)

Summary

In January 2024, the Institute for Healthcare Improvement (IHI) Lucian Leape Institute convened an expert panel to explore the promise and potential risks for patient safety from generative artificial intelligence (genAI). This report is based on the expert panel’s review and discussion.

Content

This report summarises three user cases that highlight areas where genAI could significantly impact patient safety:

1. Documentation support – including developing patient history summaries, supporting patient record reconciliation (including medication reconciliation), ambient recording of patient-clinician conversations, and drafting documentation.
2. Clinical decision support - including providing diagnostic support and recommendations, offering early detection or warning on changes to patient condition, and developing potential treatment plans.
3. Patient-facing chatbots - including acting as a data collector to support triage, interacting with patients and responding to their questions and concerns, and supporting care navigation.

The report provides a detailed review of mitigation and monitoring strategies and expert panel recommendations; and an appraisal of the implications of genAI for the patient safety field. The expert panel's recommendations are:

• Serve and safeguard the patient. Disclose and explain the use of patient-facing AI-based tools to patients.
• Learn with, engage, and listen to clinicians. Equip clinicians with general knowledge on genAI and related ethical issues, as well as specific instruction on how to use available AI-based tools.
• Evaluate and ensure AI efficacy and freedom from bias. Establish an evidence base of rigorously tested and validated AI-based tools, including the results of their use in real-life clinical situations.
• Establish strict AI governance, oversight, and guidance both for individual health delivery systems and the federal government.
• Be intentional with the design, implementation, and ongoing evaluation of AI tools. Follow human-centered design principles, actively engage end users in all phases of design, and validate models and tools with small-scale tests of real-world clinical uses.
• Engage in collaborative learning across health care systems.