Impact of Artificial Intelligence on Patient Safety Events: Preliminary Exploration of Events Reported to the PA-PSRS Database

Matthew A. Taylor, PhD, Patient Safety Authority

ORCID iD​  

Matthew A. Taylor (MattTaylor@pa.gov) is a research scientist on the Data Science & Research team at the Patient Safety Authority, where he conducts research, uses data to identify patient safety concerns and trends, and develops solutions to prevent recurrence.

Artificial intelligence (AI) is reshaping healthcare and expected to have a dramatic impact on patient safety as AI is further developed and refined.^1–7 AI is expected to improve patient safety in key areas, such as by increasing efficiency of clinical decisions and care,^2,4,6 reducing human error,^2,3,6 offering risk prediction and early detection of change in patients’ condition,^2–4 supporting system-level safety,2,4 and offering insights by aggregating many data sources.^2,4 Despite the optimism surrounding AI, the following patient safety concerns have been raised: AI models that are trained on biased or incomplete data,^1,2,4,6–8 staff become overly reliant on and biased toward the recommendations provided by AI,^1,6,8 staff not trusting the “black box,”^2,6,7 AI has given erroneous recommendations for individual patients with incomplete and/or inaccurate records,6,8 staff are overwhelmed by being given too much information/notifications,² and AI-based tools that are implemented prior to sufficient testing and validation.^4,6–8

Keywords
​AI, machine learning, ML, predictive modeling, generative, risk, harm