Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication

Chun-You Chen; Ya-Lin Chen; Jeremiah Scholl; Hsuan-Chia Yang; Yu-Chuan Jack Li

doi:10.1016/j.cmpb.2023.107869

Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication

Comput Methods Programs Biomed. 2024 Jan:243:107869. doi: 10.1016/j.cmpb.2023.107869. Epub 2023 Oct 16.

Authors

Chun-You Chen¹, Ya-Lin Chen², Jeremiah Scholl³, Hsuan-Chia Yang⁴, Yu-Chuan Jack Li⁵

Affiliations

¹ College of Medical Science and Technology, Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan; Department of Radiation Oncology, Taipei Municipal Wan Fang Hospital, Taipei 110, Taiwan; Information Technology Office in Taipei Municipal Wan Fang Hospital, Taipei Medical University, Taipei 110, Taiwan; Artificial Intelligence Research and Development Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
² College of Medical Science and Technology, Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan; Department of Biomedical Informatics and Medical Education, University of Washington, United States.
³ AESOP Technology, Songshan District, Taipei 105, Taiwan.
⁴ College of Medical Science and Technology, Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan; Research Center of Big Data and Meta-analysis, Wanfang Hospital, Taipei Medical University, Taipei, Taiwan.
⁵ College of Medical Science and Technology, Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan; Research Center of Big Data and Meta-analysis, Wanfang Hospital, Taipei Medical University, Taipei, Taiwan; Department of Dermatology, Wanfang Hospital, Taipei Medical University, Taiwan. Electronic address: jack@tmu.edu.tw.

PMID: 37924770
DOI: 10.1016/j.cmpb.2023.107869

Abstract

Background and objective: The overall benefits of using clinical decision support systems (CDSSs) can be restrained if physicians inadvertently ignore clinically useful alerts due to "alert fatigue" caused by an excessive number of clinically irrelevant warnings. Moreover, inappropriate drug errors, look-alike/sound-alike (LASA) drug errors, and problem list documentation are common, costly, and potentially harmful. This study sought to evaluate the overall performance of a machine learning-based CDSS (MedGuard) for triggering clinically relevant alerts, acceptance rate, and to intercept inappropriate drug errors as well as LASA drug errors.

Methods: We conducted a retrospective study that evaluated MedGuard alerts, the alert acceptance rate, and the rate of LASA alerts between July 1, 2019, and June 31, 2021, from outpatient settings at an academic hospital. An expert pharmacist checked the suitability of the alerts, rate of acceptance, wrong-drug errors, and confusing drug pairs.

Results: Over the two-year study period, 1,206,895 prescriptions were ordered and a total of 28,536 alerts were triggered (alert rate: 2.36 %). Of the 28,536 alerts presented to physicians, 13,947 (48.88 %) were accepted. A total of 8,014 prescriptions were changed/modified (28.08 %, 8,014/28,534) with the most common reasons being adding and/or deleting diseases (52.04 %, 4,171/8,014), adding and/or deleting drugs (21.89 %, 1,755/8,014) and others (35.48 %, 2,844/ 8,014). However, the rate of drug error interception was 1.64 % (470 intercepted errors out of 28,536 alerts), which equates to 16.4 intercepted errors per 1000 alerted orders.

Conclusion: This study shows that machine learning based CDSS, MedGuard, has an ability to improve patients' safety by triggering clinically valid alerts. This system can also help improve problem list documentation and intercept inappropriate drug errors and LASA drug errors, which can improve medication safety. Moreover, high acceptance of alert rates can help reduce clinician burnout and adverse events.

Keywords: Alert fatigue; Clinical decision support system; Drug-disease alerts; Overrides.

MeSH terms

Decision Support Systems, Clinical*
Drug Interactions
Humans
Medical Order Entry Systems*
Medication Errors / prevention & control
Prescriptions
Retrospective Studies

Substances

lipid-associated sialic acid