Algorithmic Detection of Boolean Logic Errors in Clinical Decision Support Statements

Adam Wright; Skye Aaron; Allison B. McCoy; Robert El-Kareh; Daniel Fort; Steven Z. Kassakian; Christopher A. Longhurst; Sameer Malhotra; Dustin S. McEvoy; Craig B. Monsen; Richard Schreiber; Asli O. Weitkamp; DuWayne L. Willett; Dean F. Sittig

doi:10.1055/s-0041-1722918

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2021; 12(01): 182-189
DOI: 10.1055/s-0041-1722918

Research Article

Algorithmic Detection of Boolean Logic Errors in Clinical Decision Support Statements

Adam Wright

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States

²Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States

³Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States

⁴Partners eCare, Partners HealthCare System, Boston, Massachusetts, United States

,

Skye Aaron

²Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States

,

Allison B. McCoy

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States

,

Robert El-Kareh

⁵Department of Medicine, UC San Diego Health, University of California, San Diego, San Diego, California, United States

,

Daniel Fort

⁶Center for Outcomes and Health Services Research, Ochsner Health System, New Orleans, Louisiana, United States

,

Steven Z. Kassakian

⁷Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States

,

Christopher A. Longhurst

⁵Department of Medicine, UC San Diego Health, University of California, San Diego, San Diego, California, United States

,

Sameer Malhotra

⁸Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, United States

⁹Department of Internal Medicine, NewYork-Presbyterian Hospital, New York, New York, United States

,

Dustin S. McEvoy

⁴Partners eCare, Partners HealthCare System, Boston, Massachusetts, United States

,

Craig B. Monsen

¹⁰Center for Informatics, Atrius Health, Boston, Massachusetts, United States

,

Richard Schreiber

¹¹Physician Informatics and Department of Internal Medicine, Geisinger Holy Spirit, Camp Hill, Pennsylvania, United States

,

Asli O. Weitkamp

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States

,

DuWayne L. Willett

¹²Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States

,

Dean F. Sittig

¹³School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, United States

› Author Affiliations

Abstract

Objective Clinical decision support (CDS) can contribute to quality and safety. Prior work has shown that errors in CDS systems are common and can lead to unintended consequences. Many CDS systems use Boolean logic, which can be difficult for CDS analysts to specify accurately. We set out to determine the prevalence of certain types of Boolean logic errors in CDS statements.

Methods Nine health care organizations extracted Boolean logic statements from their Epic electronic health record (EHR). We developed an open-source software tool, which implemented the Espresso logic minimization algorithm, to identify three classes of logic errors.

Results Participating organizations submitted 260,698 logic statements, of which 44,890 were minimized by Espresso. We found errors in 209 of them. Every participating organization had at least two errors, and all organizations reported that they would act on the feedback.

Discussion An automated algorithm can readily detect specific categories of Boolean CDS logic errors. These errors represent a minority of CDS errors, but very likely require correction to avoid patient safety issues. This process found only a few errors at each site, but the problem appears to be widespread, affecting all participating organizations.

Conclusion Both CDS implementers and EHR vendors should consider implementing similar algorithms as part of the CDS authoring process to reduce the number of errors in their CDS interventions.

Keywords

clinical decision support - electronic health records and systems - alerting - decision support algorithm - efficiency improvement

Full Text

References

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