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When order sets do not align with clinician workflow: assessing practice patterns in the electronic health record
  1. Ron C Li1,
  2. Jason K Wang1,
  3. Christopher Sharp2,
  4. Jonathan H Chen1
  1. 1 Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, California, USA
  2. 2 Stanford Health Care, Stanford, California, USA
  1. Correspondence to Dr Ron C Li; ronl{at}stanford.edu

Abstract

Background Order sets are widely used tools in the electronic health record (EHR) for improving healthcare quality. However, there is limited insight into how well they facilitate clinician workflow. We assessed four indicators based on order set usage patterns in the EHR that reflect potential misalignment between order set design and clinician workflow needs.

Methods We used data from the EHR on all orders of medication, laboratory, imaging and blood product items at an academic hospital and an itemset mining approach to extract orders that frequently co-occurred with order set use. We identified the following four indicators: infrequent ordering of order set items, rapid retraction of medication orders from order sets, additional a la carte ordering of items not included in order sets and a la carte ordering of items despite being listed in the order set.

Results There was significant variability in workflow alignment across the 11 762 order set items used in the 77 421 inpatient encounters from 2014 to 2017. The median ordering rate was 4.1% (IQR 0.6%–18%) and median medication retraction rate was 4% (IQR 2%–10%). 143 (5%) medications were significantly less likely while 68 (3%) were significantly more likely to be retracted than if the same medication was ordered a la carte. 214 (39%) order sets were associated with least one additional item frequently ordered a la carte and 243 (45%) order sets contained at least one item that was instead more often ordered a la carte.

Conclusion Order sets often do not align with what clinicians need at the point of care. Quantitative insights from EHRs may inform how order sets can be optimised to facilitate clinician workflow.

  • decision support, computerized
  • information technology
  • near miss
  • medication safety
  • computerized

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Contributors RCL led the conception and execution of the project, including writing of the manuscript. JKW assisted in the analysis. CS assisted in the review of the manuscript. JHC supervised the study as the PI.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval The study was approved by the institutional review board.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available on reasonable request.

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