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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".