Abstract
Artificial intelligence (AI) represents a valuable tool that could be used to improve the safety of care. Major adverse events in healthcare include: healthcare-associated infections, adverse drug events, venous thromboembolism, surgical complications, pressure ulcers, falls, decompensation, and diagnostic errors. The objective of this scoping review was to summarize the relevant literature and evaluate the potential of AI to improve patient safety in these eight harm domains. A structured search was used to query MEDLINE for relevant articles. The scoping review identified studies that described the application of AI for prediction, prevention, or early detection of adverse events in each of the harm domains. The AI literature was narratively synthesized for each domain, and findings were considered in the context of incidence, cost, and preventability to make projections about the likelihood of AI improving safety. Three-hundred and ninety-two studies were included in the scoping review. The literature provided numerous examples of how AI has been applied within each of the eight harm domains using various techniques. The most common novel data were collected using different types of sensing technologies: vital sign monitoring, wearables, pressure sensors, and computer vision. There are significant opportunities to leverage AI and novel data sources to reduce the frequency of harm across all domains. We expect AI to have the greatest impact in areas where current strategies are not effective, and integration and complex analysis of novel, unstructured data are necessary to make accurate predictions; this applies specifically to adverse drug events, decompensation, and diagnostic errors.
Highlights
Adverse events related to unsafe care represent one of the top ten causes of death and disability worldwide, and a third to a half appear preventable[1]
The scoping review included studies that focused on the application of artificial intelligence (AI) for prediction, prevention, and/or early detection of events in each of the harm domains in hospital, outpatient, community, and home settings
The incidence, cost, and preventability of events for each harm domain are presented in Healthcare-associated infections
Summary
Adverse events related to unsafe care represent one of the top ten causes of death and disability worldwide, and a third to a half appear preventable[1]. Investments in reducing harm can lead to substantial savings, and more importantly improve patient outcomes. The application of artificial intelligence (AI) has tremendous potential as a tool for improving safety, both inside and outside of the hospital, by providing solutions to predict harms, collect a variety of data including both new and already-available data, and as part of quality improvement initiatives. AI can provide decision support by identifying patients at high risk of hospital harm to guide prevention and early intervention strategies. When coupled with digital approaches, these technologies can improve communication between patients and healthcare providers to reduce the frequency of preventable harms. While existing data will be helpful, new data will be available through technologies like sensors which should improve predictions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
