TY - JOUR
T1 - Algorithmic Detection of Boolean Logic Errors in Clinical Decision Support Statements
AU - Wright, Adam
AU - Aaron, Skye
AU - McCoy, Allison B.
AU - El-Kareh, Robert
AU - Fort, Daniel
AU - Kassakian, Steven Z.
AU - Longhurst, Christopher A.
AU - Malhotra, Sameer
AU - McEvoy, Dustin S.
AU - Monsen, Craig B.
AU - Schreiber, Richard
AU - Weitkamp, Asli O.
AU - Willett, Duwayne L.
AU - Sittig, Dean F.
N1 - Publisher Copyright:
© 2021 BMJ Publishing Group. All rights reserved.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - 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.
AB - 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.
KW - alerting
KW - clinical decision support
KW - decision support algorithm
KW - efficiency improvement
KW - electronic health records and systems
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U2 - 10.1055/s-0041-1722918
DO - 10.1055/s-0041-1722918
M3 - Article
C2 - 33694144
AN - SCOPUS:85102468525
SN - 1869-0327
VL - 12
SP - 182
EP - 189
JO - Applied Clinical Informatics
JF - Applied Clinical Informatics
IS - 1
ER -