Physician Experience with Electronic Medication Order Sets: Introduction
Bibliographic record
Abstract
BACKGROUND: Electronic medical record (EMR) and electronic health record (EHR) are used interchangeably to describe a computerized medical information system that collects, stores and displays patient information (Boonstra and Broekhuis 2010). Blumenthal and Tavenner (2010) suggested that computerized medical implementation improves decision-making and patient management. As part of its EMR, Humber River Hospital has implemented electronic order sets (EOSs) by building them into the computerized physician order entry (CPOE) system. Electronic prescribing renders paper prescriptions obsolete as it reduces errors; increases accuracy; and enhances efficiency, compliance and record-keeping (Canada Health Infoway 2017). OBJECTIVE: The aim of this research was to explore physicians' perspectives and experiences using EOSs. METHODS: This qualitative study examined the perceptions of various physicians on the impact of EOSs. Data were collected through semi-structured, in-depth interviews with eligible physicians. Domains explored included usability, efficiency, safety and implications for the physician profession. RESULTS: Major themes that emerged included usability, efficiency and safety. Several implications for physician practice were also revealed. CONCLUSION: The findings from our study support previous studies that describe the benefits of EOSs, including ease of use and efficiency, real-time information that is evidence-based, increased safety and minimization of memory burden. EOSs were not perceived to be a replacement for clinical reasoning.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".