The potential of artificial intelligence to improve patient safety: a scoping review

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.

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

“…The authors found that such complications reduce patient satisfaction, incur cost, and worsen outcomes for patients [ 139 , 140 ]. Brain tumour surgery may also benefit from greater AI integration by helping to predict and mitigate the development of numerous other typical post-operative complications, including adverse drug events [ 141 ], venous thromboembolism [ 142 ], development of pressure ulcers [ 143 , 144 ], falls [ 145 ], and hypoglycaemia [ 146 , 147 ]. These complications are all-too-often preventable, and significantly affect patient outcomes.…”

Health in Digital World: A Regulatory Overview in United States

“…IBM Watson is an example of an AI-based system used in healthcare for various use also in clinical trials [37]. As phase IV clinical trial is very beneficial for safety, effectiveness, and post approval assessments also Known as Postmarketing surveillance (PMS) [36] AI can improve patient safety including ADR monitoring [38]. VigiRank, VigiMatch are examples of AIbased databases for PV [35].…”

Clinical Decision Support Systems

“…Nonknowledge-based systems require a sufficient data source in order to use machine learning and statistical pattern recognition, which currently drive a strong artificial intelligence movement, to develop recommendations [4-6]. These algorithms have the great advantage that, as learning systems, they can improve their recommendations as the volume of data increases.…”

“…However, the correct dosage of the corresponding (alternative) medication also harbors considerable sources of error. Bates et al [6, 43] found that almost a third of the ADEs were in principle preventable [44].…”