Using EHR Data to Detect Prescribing Errors in Rapidly Discontinued Medication Orders

Jonathan D. Burlison, Robert B. McDaniel, Donald K. Baker, Murad Hasan, Jennifer J. Robertson, Scott Howard, James M. Hoffman

Research output: Contribution to journalArticle

Abstract

Background Previous research developed a new method for locating prescribing errors in rapidly discontinued electronic medication orders. Although effective, the prospective design of that research hinders its feasibility for regular use. Objectives Our objectives were to assess a method to retrospectively detect prescribing errors, to characterize the identified errors, and to identify potential improvement opportunities. Methods Electronically submitted medication orders from 28 randomly selected days that were discontinued within 120 minutes of submission were reviewed and categorized as most likely errors, nonerrors, or not enough information to determine status. Identified errors were evaluated by amount of time elapsed from original submission to discontinuation, error type, staff position, and potential clinical significance. Pearson's chi-square test was used to compare rates of errors across prescriber types. Results In all, 147 errors were identified in 305 medication orders. The method was most effective for orders that were discontinued within 90 minutes. Duplicate orders were most common; physicians in training had the highest error rate (p < 0.001), and 24 errors were potentially clinically significant. None of the errors were voluntarily reported. Conclusion It is possible to identify prescribing errors in rapidly discontinued medication orders by using retrospective methods that do not require interrupting prescribers to discuss order details. Future research could validate our methods in different clinical settings. Regular use of this measure could help determine the causes of prescribing errors, track performance, and identify and evaluate interventions to improve prescribing systems and processes.

Original languageEnglish (US)
Pages (from-to)82-88
Number of pages7
JournalApplied Clinical Informatics
Volume9
Issue number1
DOIs
StatePublished - Jan 1 2018

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Chi-Square Distribution
Research Design
Physicians
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All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Computer Science Applications
  • Health Information Management

Cite this

Burlison, J. D., McDaniel, R. B., Baker, D. K., Hasan, M., Robertson, J. J., Howard, S., & Hoffman, J. M. (2018). Using EHR Data to Detect Prescribing Errors in Rapidly Discontinued Medication Orders. Applied Clinical Informatics, 9(1), 82-88. https://doi.org/10.1055/s-0037-1621703

Using EHR Data to Detect Prescribing Errors in Rapidly Discontinued Medication Orders. / Burlison, Jonathan D.; McDaniel, Robert B.; Baker, Donald K.; Hasan, Murad; Robertson, Jennifer J.; Howard, Scott; Hoffman, James M.

In: Applied Clinical Informatics, Vol. 9, No. 1, 01.01.2018, p. 82-88.

Research output: Contribution to journalArticle

Burlison, JD, McDaniel, RB, Baker, DK, Hasan, M, Robertson, JJ, Howard, S & Hoffman, JM 2018, 'Using EHR Data to Detect Prescribing Errors in Rapidly Discontinued Medication Orders', Applied Clinical Informatics, vol. 9, no. 1, pp. 82-88. https://doi.org/10.1055/s-0037-1621703
Burlison, Jonathan D. ; McDaniel, Robert B. ; Baker, Donald K. ; Hasan, Murad ; Robertson, Jennifer J. ; Howard, Scott ; Hoffman, James M. / Using EHR Data to Detect Prescribing Errors in Rapidly Discontinued Medication Orders. In: Applied Clinical Informatics. 2018 ; Vol. 9, No. 1. pp. 82-88.
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