A General Method for Identifying Excess Revisit Rates: The Case of Hypertension
Bibliographic record
Abstract
OBJECTIVE: To provide a description and application of a novel methodology for comparing actual to expected visit rates at the physician level (controlling for patient characteristics) that could be employed in healthcare monitoring and management. DATA SOURCES/STUDY SETTING: Two fiscal years (1997/1998 and 1998/1999) of health utilization data extracted from linked administrative data sets on a population-based cohort of 13,688 patients (aged 25+ with hypertension) involving 157 physicians. STUDY DESIGN: We re-analyzed data from a previously published retrospective cohort study to develop and apply a new methodology for identifying higher or lower than expected physician visit rates for hypertension. DATA COLLECTION/EXTRACTION METHODS: We matched each study physician's hypertensive patients on the basis of age, sex, income and co-morbidity to an equal number of control patients drawn from the cohort. We then compared visit rates between the actual practice and the matched control practice. PRINCIPAL FINDINGS: Although the correlation between the visit rates of the two groups of practices was high (r=.87), there were notable differences in rates, suggesting substantial discretionary practice among physicians. CONCLUSIONS: The methodology outlined in this paper provides a basis for identifying variations in visit levels related to discretionary practice patterns and patient preferences. Deviation from expected visit rates provides a potentially useful measure for performance feedback and quality improvement activities.
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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 | 0.033 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".