Opinions on provider profiling: Telephone survey of stakeholders
Bibliographic record
Abstract
The views of producers, purchasers, and users of provider profiling concerning this practice were studied. A snowball sample of individuals representing seven groups with a stake in retrospective provider profiling were interviewed by telephone over a 12-week period in 1997. Participants were asked what they believed were the most important uses for profiles, who should receive copies of profiles, and what the limitations of profiles are. A semi-structured format was used to ensure that each interview was comparable and complete. The responses were aggregated, and qualitative research approaches were used to analyze them. A total of 40 people were interviewed. A majority of the respondents cited physician education, changing physician behavior, and monitoring and improving the quality of care as valid uses of provider profiles. A majority believed that the recipients of profile data should include the individual providers being profiled, medical administrative staff, people directly involved in the profiling program, pharmacists, and health plan administrators. The respondents acknowledged many limitations of profiles, with the top concern being inherent problems in the use of billing and administrative databases for profiling. Interviews with stakeholders in provider profiling yielded insights into the strengths and weaknesses of profiling, as well as echoing findings reported elsewhere. Health system administrators and health care professionals need to be aware of these issues as they use and modify profiling.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".