Evaluating the implementation of computerized prescriber order entry (CPOE) for systemic treatment (ST) in Ontario.
Bibliographic record
Abstract
54 Background: A successful effort was led in Ontario to increase ST CPOE adoption from 73% to 92% to support safety of patients receiving complex systemic treatment. This work included implementation at 19 hospitals from 2011-2013. The implementation process included identification of multidisciplinary team champions, baseline workflow analysis and development of a future desired state. Findings from this project’s benefits evaluation are presented. Methods: Each hospital was required to collect pre and post implementation measurements for medication errors, transcription errors and order clarity/completeness. Six months post implementation, a semi-structured telephone interview was conducted with representative hospitals (9/19) to obtain qualitative feedback on how implementation impacted workflow, inter-professional practice and workload. Results: Hospitals that implemented showed: 1) decrease in overall medication error rates. 2) decrease in transcription error rates and 3) decrease in number of unclear or incomplete orders. Qualitative feedback from hospital leads indicated that the “future desired” workflow was achieved by all. Most hospital respondents indicated that clarity of inter-professional communication regarding orders improved and in most cases overall workload did not increase. Conclusions: The findings reinforce the benefit associated with implementation of ST CPOE in outpatient settings. Lessons learned in Ontario can be leveraged to support successful implementation of ST CPOE in other jurisdictions. Critical implementation success factors included: 1) leadership and multi-disciplinary involvement; 2) provision of funding and cost sharing; 3) facilitation of process improvement through established methodologies, 4) knowledge transfer and peer group led education and training sessions. [Table: see text]
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".