Format

Send to

Choose Destination
J Am Med Inform Assoc. 2018 May 1;25(5):496-506. doi: 10.1093/jamia/ocx106.

Clinical decision support alert malfunctions: analysis and empirically derived taxonomy.

Author information

1
Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
2
Department of Medicine, Harvard Medical School, Boston, MA, USA.
3
Clinical and Quality Analysis, Partners Healthcare, Somerville, MA, USA.
4
Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA.
5
Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA.
6
Department of Pathology, University of Illinois at Chicago, Chicago, IL, USA.
7
Regenstrief Institute, Indianapolis, IN, USA.
8
Department of Medicine, Pharmacy Practices, and Pharmacy Systems, Outcomes and Policy, University of Illinois at Chicago, Chicago, IL, USA.
9
Department of Biomedical Informatics, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
10
Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, Boston, MA, USA.
11
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
12
Department of Medicine and Information Technology, Holy Spirit Hospital - A Geisinger Affiliate, Camp Hill, PA, USA.
13
Department of Medical Informatics, Memorial Hermann Health System, Houston, TX, USA.
14
Department of Biomedical Informatics, University of Texas Health Science Center at Houston, TX, USA.

Abstract

Objective:

To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions.

Materials and Methods:

We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions.

Results:

We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common.

Discussion:

Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS.

Conclusion:

CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center