Patterns and Perceptions of Standard Order Set Use Among Physicians Working Within a Multihospital System: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Electronic standard order sets automate the ordering of specific treatment, testing, and investigative protocols by physicians. These tools may help reduce unwarranted clinical variation and improve health care efficiency. Despite their routine implementation within electronic medical records (EMRs), little is understood about how they are used and what factors influence their adoption in practice. OBJECTIVE: This study aims to (1) describe the patterns of use of standard order sets implemented in a widely used EMR (PowerPlans and Cerner Millennium) within a multihospital digital health care system; (2) explore the experiences and perceptions of implementers and users regarding the factors contributing to the use of these standard order sets; and (3) map these findings to the Capability, Opportunity, and Motivation Behavior (COM-B) model of behavior change to assist those planning to develop, improve, implement, and iterate the use of standard order sets in hospital settings. METHODS: Quantitative data on standard order set usage were captured from 5 hospitals over 5-month intervals for 3 years (2019, 2020, and 2021). Qualitative data, comprising unstructured and semistructured interviews (n=15), were collected and analyzed using a reflexive thematic approach. Interview themes were then mapped to a theory-informed model of behavior change (COM-B) to identify determinants of standard order set usage in routine clinical practice. The COM-B model is an evidence-based, multicomponent framework that posits that human actions result from multiple contextual influences, which can be categorized across 3 dimensions: capability, opportunity, and motivation, all of which intersect. RESULTS: The total count of standard order set usage across the health system during the 2019 observation period was 267,253, increasing to 293,950 in 2020 and 335,066 in 2021. There was a notable shift toward using specialty order sets that received upgrades during the study period. Four emergent themes related to order set use were derived from clinician interviews: (1) Knowledge and Skills; (2) Perceptions; (3) Technical Dependencies; and (4) Unintended Consequences, all of which were mapped to the COM-B model. Findings indicate a user preference for customized order sets that respond to local context and user experience. CONCLUSIONS: The study findings suggest that ongoing investment in the development and functionality of specialty order sets has the potential to enhance usage as these sets continue to be customized in response to local context and user experience. Sustained and continuous uptake of appropriate Computerized Provider Order Entry use may require implementation strategies that address the capability, opportunity, and motivational influencers of behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".