Appl Clin Inform 2017; 08(03): 910-923
DOI: 10.4338/ACI-2017-01-RA-0006
Research Article
Schattauer GmbH

Clinical decisions support malfunctions in a commercial electronic health record

Steven Z. Kassakian
1  Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon/USA
,
Thomas R. Yackel
1  Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon/USA
,
Paul N. Gorman
1  Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon/USA
,
David A. Dorr
1  Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon/USA
› 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 T15LM007088. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Further Information

Publication History

received: 10 January 2017

accepted in revised form: 21 May 2017

Publication Date:
20 December 2017 (online)

Summary

Objectives: Determine if clinical decision support (CDS) malfunctions occur in a commercial electronic health record (EHR) system, characterize their pathways and describe methods of detection.

Methods: We retrospectively examined the firing rate for 226 alert type CDS rules for detection of anomalies using both expert visualization and statistical process control (SPC) methods over a five year period. Candidate anomalies were investigated and validated.

Results: Twenty-one candidate CDS anomalies were identified from 8,300 alert-months. Of these candidate anomalies, four were confirmed as CDS malfunctions, eight as false-positives, and nine could not be classified. The four CDS malfunctions were a result of errors in knowledge management: 1) inadvertent addition and removal of a medication code to the electronic formulary list; 2) a seasonal alert which was not activated; 3) a change in the base data structures; and 4) direct editing of an alert related to its medications. 154 CDS rules (68%) were amenable to SPC methods and the test characteristics were calculated as a sensitivity of 95%, positive predictive value of 29% and F-measure 0.44.

Discussion: CDS malfunctions were found to occur in our EHR. All of the pathways for these malfunctions can be described as knowledge management errors. Expert visualization is a robust method of detection, but is resource intensive. SPC-based methods, when applicable, perform reasonably well retrospectively.

Conclusion: CDS anomalies were found to occur in a commercial EHR and visual detection along with SPC analysis represents promising methods of malfunction detection.

Citation: Kassakian SZ, Yackel TR, Gorman PN, Dorr DA. Clinical decisions support malfunctions in a commercial electronic health record. Appl Clin Inform 2017; 8: 910–923 https://doi.org/10.4338/ACI-2017-01-RA-0006

Human Subjects Protection

The study was performed in compliance with World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects.