From evidence to impact: Determining the vital few indicators for systemic treatment computerized prescriber order entry systems (ST CPOE).
Bibliographic record
Abstract
258 Background: It is important to demonstrate that delivery systems contribute to patient outcomes. Quality indicators provide a quantitative, evidence based (EB) foundation for clinicians, organizations, researchers, and administrators to monitor and evaluate performance of these systems. Technologies, such as ST CPOE systems are costly, impact clinician practice and therefore, would benefit from accountability through quality measures. The process for the identification of EB indicators for measuring the impacts of ST CPOE systems is described. Methods: The indicators were developed alongside the ST CPOE Best Practice Guideline. A multi-phased approach included the development of a conceptual framework, defining quality dimensions, literature review, environmental scans, and key informant interviews. An initial list of indicators was identified and aligned with the relevant quality dimensions (i.e. safety, effectiveness, efficiency, integration). A Modified Delphi methodology, involving internal and external content experts in oncology practice, technology, informatics, and human factors, was undertaken to obtain consensus on the vital few. Results: Three rounds of Modified Delphi exercises were conducted with the initial list of 118 indicators. 15 clinical practice indicators (CPI) were deemed as relevant for overall evaluation and research purposes specific to ST CPOE systems. Within this group, four indicators were identified as being valuable for reporting quality monitoring at the organizational, regional and provincial levels. In addition to CPI, 74 system functionality (e.g. usability, alert features, system integration) indicators were identified. Conclusions: Through engagement of experts and evidence review, we have identified a set of quality indicators for ST CPOE systems which will facilitate appropriate evaluation of systems and implementation processes. Operationalizing these indicators for automatic data flow through is planned in a large geographic area.
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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 | 0.251 | 0.503 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.023 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".