Using automated methods to detect safety problems with health information technology: a scoping review

Abstract

Objective To summarize the research literature evaluating automated methods for early detection of safety problems with health information technology (HIT). Materials and Methods We searched bibliographic databases including MEDLINE, ACM Digital, Embase, CINAHL Complete, PsycINFO, and Web of Science from January 2010 to June 2021 for studies evaluating the performance of automated methods to detect HIT problems. HIT problems were reviewed using an existing classification for safety concerns. Automated methods were categorized into rule-based, statistical, and machine learning methods, and their performance in detecting HIT problems was assessed. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses extension for Scoping Reviews statement. Results Of the 45 studies identified, the majority (n = 27, 60%) focused on detecting use errors involving electronic health records and order entry systems. Machine learning (n = 22) and statistical modeling (n = 17) were the most common methods. Unsupervised learning was used to detect use errors in laboratory test results, prescriptions, and patient records while supervised learning was used to detect technical errors arising from hardware or software issues. Statistical modeling was used to detect use errors, unauthorized access, and clinical decision support system malfunctions while rule-based methods primarily focused on use errors. Conclusions A wide variety of rule-based, statistical, and machine learning methods have been applied to automate the detection of safety problems with HIT. Many opportunities remain to systematically study their application and effectiveness in real-world settings.

Trend Analysis of Inpatient Medical Adverse Events in Taiwan (2014–2020): Findings From Taiwan Patient Safety Reporting System

“…Medication error–related events constitute the most frequently reported because it is particularly likely to be discovered by error detection algorithms in hospital information systems, 25 which may explain why such events did not exhibit a significant decreasing trend. However, the automation of drug prescription and delivery has the potential to further reduce medication errors, 25,26 as evidenced by the severity of medication errors becoming milder in recent years. Similar to this study’s findings, a 2022 report from the Office of the Inspector General found medication events as the most common type 27 and another study adopting the Medicare Patient Safety Monitoring System found a large reduction in its rate 28 .…”