Evaluation of Clinical Decision Support to Reduce Sedative-Hypnotic Prescribing in Older Adults

Natasha N. Joglekar; Yatindra Patel; Michelle S. Keller

doi:10.1055/s-0041-1730030

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2021; 12(03): 436-444
DOI: 10.1055/s-0041-1730030

Research Article

Evaluation of Clinical Decision Support to Reduce Sedative-Hypnotic Prescribing in Older Adults

Natasha N. Joglekar

¹Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Boston, Massachusetts, United Sates

,

Yatindra Patel

²Division of General Internal Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United Sates

,

Michelle S. Keller

²Division of General Internal Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United Sates

³Division of Informatics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, United Sates

⁴Department of Health Policy and Management, Fielding School of Public Health, University of California-Los Angeles, Los Angeles, California, United Sates

› Author Affiliations

Abstract

Objective We sought to characterize the performance of inpatient and outpatient computerized clinical decision support (CDS) alerts aimed at reducing inappropriate benzodiazepine and nonbenzodiazepine sedative medication prescribing in older adults 18 months after implementation.

Methods We reviewed the performance of two CDS alerts in the outpatient and inpatient settings in 2019. To examine the alerts' effectiveness, we analyzed metrics including overall alert adherence, provider-level adherence, and reasons for alert trigger and override.

Results In 2019, we identified a total of 14,534 and 4,834 alerts triggered in the outpatient and inpatient settings, respectively. Providers followed only 1% of outpatient and 3% of inpatient alerts. Most alerts were ignored (68% outpatient and 60% inpatient), while providers selected to override the remaining alerts. In each setting, the top 2% of clinicians were responsible for approximately 25% of all ignored or overridden alerts. However, a small proportion of clinicians (2% outpatient and 4% inpatient) followed the alert at least half of the time and accounted for a disproportionally large fraction of the total followed alerts. Our analysis of the free-text comments revealed that many alerts were to continue outpatient prescriptions or for situational anxiety.

Conclusion Our findings highlight the importance of evaluation of CDS performance after implementation. We found large variation in response to the inpatient and outpatient alerts, both with respect to follow and ignore rates. Reevaluating the alert design by providing decision support by indication may be more helpful and may reduce alert fatigue.

Keywords

implementation and deployment - testing and evaluation - clinical decision support - alert fatigue - medication management

Full Text

References

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