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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".