Delivery of Preventive Services to Older Adults by Primary Care Physicians
Bibliographic record
Abstract
CONTEXT: Rates of preventive services remain below national goals. OBJECTIVE: To identify characteristics of physicians and their practices that are associated with the quality of preventive care their patients receive. DESIGN: Cross-sectional analysis of data on US physician respondents to the 2000-2001 Community Tracking Study Physician Survey linked to claims data on Medicare beneficiaries they treated in 2001. Physician variables included training and qualifications and sex. Practice setting variables included practice type, size, sources of revenue, and access to information technology. Analyses were adjusted for patient demographics and comorbidity, as well as community characteristics. SETTING AND PARTICIPANTS: Primary care delivered by 3660 physicians providing usual care to 24 581 Medicare beneficiaries aged 65 years and older. MAIN OUTCOME MEASURES: Proportion of eligible beneficiaries receiving each of 6 preventive services: diabetic monitoring with hemoglobin A(1c) measurement or eye examinations, screening for colon or breast cancer, and vaccination for influenza or pneumococcus in 2001. RESULTS: Overall, the proportion of beneficiaries receiving services was below national goals. Physician and, more consistently, practice-level characteristics were both associated with differences in the delivery of services. The strongest associations were with practice type and the percentage of practice revenue derived from Medicaid. For instance, beneficiaries receiving usual care in practices with less than 6% of revenue from Medicaid were more likely than those with more than 15% of revenue derived from Medicaid to receive diabetic eye examinations (48.9% vs 43%; P = .02), hemoglobin A1c monitoring (61.2% vs 48.4%; P<.001), mammograms (52.1% vs 38.9%; P<.001), colon cancer screening (10.0% vs 8.5%; P = .60), and influenza (50.2% vs 39.2%; P<.001) and pneumococcal (8.2% vs 6.4%; P<.001) vaccinations. Other variables associated with delivery of preventive services after adjustment for patient and geographic factors included obtaining usual health care from a physician who worked in group practices of 3 or more, who was a graduate of a US or Canadian medical school, or who reported availability of information technology to generate preventive care reminders or access treatment guidelines. CONCLUSIONS: Delivery of routine preventive services is suboptimal for Medicare beneficiaries. However, patients treated within particular practice settings and by particular subgroups of physicians are at particular risk of low-quality care. Profiling these practices may help develop tailored interventions that can be directed to sites where the opportunities for quality improvement are greatest.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".