Adam Wright1,2,3,4, Skye Aaron2, Allison B McCoy1, Robert El-Kareh5, Daniel Fort6, Steven Z Kassakian7, Christopher A Longhurst5, Sameer Malhotra8,9, Dustin S McEvoy4, Craig B Monsen10, Richard Schreiber11, Asli O Weitkamp1, DuWayne L Willett12, Dean F Sittig13. 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. 5. Department of Medicine, UC San Diego Health, University of California, San Diego, San Diego, California, United States. 6. Center for Outcomes and Health Services Research, Ochsner Health System, New Orleans, Louisiana, United States. 7. Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States. 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. 10. Center for Informatics, Atrius Health, Boston, Massachusetts, United States. 11. Physician Informatics and Department of Internal Medicine, Geisinger Holy Spirit, Camp Hill, Pennsylvania, United States. 12. Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States. 13. School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, United States.
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. Thieme. All rights reserved.
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. Thieme. All rights reserved.
Authors: Adam Wright; Joan S Ash; Skye Aaron; Angela Ai; Thu-Trang T Hickman; Jane F Wiesen; William Galanter; Allison B McCoy; Richard Schreiber; Christopher A Longhurst; Dean F Sittig Journal: Int J Med Inform Date: 2018-08-02 Impact factor: 4.046
Authors: Dingcheng Li; Cory M Endle; Sahana Murthy; Craig Stancl; Dale Suesse; Davide Sottara; Stanley M Huff; Christopher G Chute; Jyotishman Pathak Journal: AMIA Annu Symp Proc Date: 2012-11-03
Authors: John D McGreevey; Colleen P Mallozzi; Randa M Perkins; Eric Shelov; Richard Schreiber Journal: Appl Clin Inform Date: 2020-01-01 Impact factor: 2.342
Authors: Adam Wright; Thu-Trang T Hickman; Dustin McEvoy; Skye Aaron; Angela Ai; Jan Marie Andersen; Salman Hussain; Rachel Ramoni; Julie Fiskio; Dean F Sittig; David W Bates Journal: J Am Med Inform Assoc Date: 2016-03-28 Impact factor: 4.497
Authors: Lisa A Grohskopf; Leslie Z Sokolow; Karen R Broder; Emmanuel B Walter; Alicia M Fry; Daniel B Jernigan Journal: MMWR Recomm Rep Date: 2018-08-24