Bibliographic record
Abstract
Performance of Emergency Department (ED) physicians (MDs) is multi-faceted since it impacts multiple dimensions such as health outcomes of patients, utilization of resources, throughput of patients and timeliness of care. Therefore, the assessment of their performance demands the use of a tool that allows considering multiple evaluation criteria. However, commonly used multi-criteria evaluation methods often require assigning weights to dimensions in order to define their relative importance on a final performance score. This feature introduces subjectivity in the development of weights and has the potential to produce biased results. The purpose of this thesis research is to develop a multi-dimensional evaluation tool for evaluating performance of ED MDs. The proposed evaluation tool relies on a mathematical programming model known as Data Envelopment Analysis (DEA). The use of DEA does not ask for subjective weighting assignments for each dimension that describe the ED MDs’ performance. It is capable of considering multiple heterogeneous performance measures to identify benchmark practice and the individual improvements leading to best practice of each evaluated unit. The DEA model described here was developed from real data to assess the performance of 20 PED MDs from the Children’s Hospital of Eastern Ontario (CHEO). Multiple evaluations were run on stratified data in order to identify benchmark practice in each of seven categories of patients’ complaints and to determine the impact of accompanying MD trainees on PED MDs’ performance. For each PED MD, performance scores and improvements in each category of patients’ complaints (i.e. respiratory, trauma, abdominal, fever, gastroenterology, allergy and Ear-Nose-Throat complaints) were determined. This helped identifying the required improvements that would lead PED MDs to achieve benchmark performance. Regarding the influence of MD trainees on PED MDs’ performance, results show that most PED MDs (15 out of 20) perform better when they are not accompanied by a trainee which motivates further research to assess trade-offs between teaching and clinical performance. In summary, DEA proved to be an appropriate tool for performance evaluation of PED MDs because it helped to identify benchmark performers and provided information for performance improvements under a multi dimensional performance evaluation framework.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".