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
1  Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
2  Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
3  Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States
4  Partners eCare, Partners HealthCare System, Boston, Massachusetts, United States
,
Skye Aaron
2  Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
,
Allison B. McCoy
1  Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Robert El-Kareh
5  Department of Medicine, UC San Diego Health, University of California, San Diego, San Diego, California, United States
,
Daniel Fort
6  Center for Outcomes and Health Services Research, Ochsner Health System, New Orleans, Louisiana, United States
,
Steven Z. Kassakian
7  Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States
,
Christopher A. Longhurst
5  Department of Medicine, UC San Diego Health, University of California, San Diego, San Diego, California, United States
,
Sameer Malhotra
8  Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, United States
9  Department of Internal Medicine, NewYork-Presbyterian Hospital, New York, New York, United States
,
Dustin S. McEvoy
4  Partners eCare, Partners HealthCare System, Boston, Massachusetts, United States
,
Craig B. Monsen
10  Center for Informatics, Atrius Health, Boston, Massachusetts, United States
,
Richard Schreiber
11  Physician Informatics and Department of Internal Medicine, Geisinger Holy Spirit, Camp Hill, Pennsylvania, United States
,
Asli O. Weitkamp
1  Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
DuWayne L. Willett
12  Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States
,
Dean F. Sittig
13  School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, United States
› Author Affiliations
Funding Research reported in this publication was supported by the National Library of Medicine of the National Institutes of Health under Award Number R01LM011966. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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.

Protection of Human and Animal Subjects

Human and/or animal subjects were not included in this project.


Authors' Contributions

A.W. wrote the manuscript and conceived the study design. S.A. wrote the logic minimization program. D.F. acted as a tester for the logic minimization program. All authors provided data. All authors provided critical revisions for important intellectual content.




Publication History

Received: 11 August 2020

Accepted: 16 December 2020

Publication Date:
10 March 2021 (online)

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