Identifying performance indicators for family practice: assessing levels of consensus.
Bibliographic record
Abstract
OBJECTIVE: To identify performance indicators for family practice that focus on organizational structures and clinical processes of care, to review evidence linking indicators to patient outcomes, to have providers select indicators they consider important for performance assessment, and to obtain provider views on challenges to developing a performance assessment system. DESIGN: Review of published and unpublished literature and contact with international experts resulted in a list of 131 structure and process indicators and associated evidence. This information was used in a two-round modified Delphi consensus process, which was followed by interviews with each of the 12 consensus panel members. SETTING: Ontario family practices. PARTICIPANTS: Eleven family physicians and one nurse practitioner from Ontario. MAIN OUTCOME MEASURES: Survey package with 131 indicators and associated evidence was mailed to panel members who rated each of the indicators on a Likert scale from 1 (not at all important for performance assessment) to 9 (essential for performance assessment). Interviews were conducted with panel members to discuss indicator feasibility and data sources. Consensus score and median importance score for each indicator were main outcome measures; interviews identified barriers to performance assessment. RESULTS: Fifty-one indicators achieved high consensus, 19 moderate consensus, and 38 low consensus. Clinical indicators that reached a high level of consensus were generally supported by grade A or B recommendations and level I to III evidence. Clinical indicators that achieved moderate consensus often had fair support in the literature. Low consensus was mainly associated with fair or equivocal evidence. During follow-up interviews, consensus panel members voiced frustration with inconsistencies in the evidence and practice guidelines upon which indicators are often based, and with poor transfer of patient information between health care providers. Lack of detail in patient care documentation and inconsistent documentation were mentioned frequently as threats to data quality. CONCLUSION: Despite challenges to performance measurement noted by the panel, study results support the continued development, refinement, and testing of primary care performance indicators.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".