Can the Quality of Care in Family Practice Be Measured Using Administrative Data?
Bibliographic record
Abstract
OBJECTIVE: To explore the feasibility of using administrative data to develop process indicators for measuring quality in primary care. DATA SOURCES/STUDY SETTING: The Population Health Research Data Repository (Repository) housed at the Manitoba Centre for Health Policy which includes physician claims, hospital discharge abstracts, pharmaceutical use (Drug Program Information Network (DPIN)), and the Manitoba Immunization Monitoring Program (MIMS) for all residents of Manitoba, Canada who used the health care system during the 2001/02 fiscal year. Family physicians were identified from the Physician Resource Database. Indicators were developed based on a literature review and focus group validation. DATA COLLECTION/EXTRACTION METHODS: Data files were extracted from administrative data available in the Repository. We extracted data based on the ICD-9-CM codes and ATC-class drugs prescribed and then linked them to the Physician Resource Database. Physician practices were defined by allocating patients to their most responsible physician. Every family physician in Manitoba that met the inclusion criteria (having either 5 or 10 eligible patients depending on the indicator) was 'scored' on each indicator. Physicians were then grouped according to the proportion of the patients allocated to their practice who received the recommended care for the specific indicator. PRINCIPAL FINDINGS: Using administrative health data we were able to develop and measure eight indicators of quality of care covering both preventive care services and chronic disease management. The number of eligible physicians and patients varied for each indicator as did the percent of patients with recommended care, per physician. For example, the childhood immunization indicator included 544 physicians who, on average, provided immunization for 65 percent of their patients. CONCLUSIONS: Quality of care provided by family physicians can be measured using administrative data. Despite the limitations addressed in this paper, this work establishes a practical methodology to measure quality of care provided by family physicians that can be used for quality improvement initiatives.
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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 | 0.094 | 0.421 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".