Approaches to Identify Nursing Home Specialists Using Medicare Claims Data
Bibliographic record
Abstract
BACKGROUND: Physicians and advanced practice clinicians who practice in nursing homes (NHs) are becoming increasingly specialized. Studies have identified clinicians as NH specialists using multiple data sources; yet, researchers' access to several sources may be limited due to required data purchases. OBJECTIVE: Examine the concordance of 2 approaches to measure NH specialization versus a standard approach using clinician-level Medicare Data on Provider Practice and Specialty (MD-PPAS). These alternative approaches leveraged: (1) publicly available clinician-level Medicare Part B data; and (2) patient-level Medicare Part D Event claims linked to publicly available clinician-level Medicare Part D prescribers data. RESEARCH DESIGN: Yearly cross-sections from 2016 to 2020. SUBJECTS: Physicians and advanced practice clinicians with at least one Medicare-paid service to NH residents and at least 100 total services in a given year. MEASURES: Nursing home specialists were classified as clinicians with ≥90% of annual services provided to NH residents. RESULTS: Between 2016 and 2020, NH specialists comprised 49,542 of 321,267 eligible clinician-years (15.4%) in MD-PPAS data; 35,983 of 189,992 eligible clinician-years (18.9%) in Part B data; and 31,148 of 1,101,484 eligible clinician-years (2.8%) in Part D data. Compared with the MD-PPAS approach, the concordance was greater for the Part B approach (sensitivity 71.8%, specificity 99.7%) than the Part D approach (39.4%, 97.6%). CONCLUSIONS: There were large differences in the numbers of eligible clinicians and NH specialists identified by 3 approaches. The Part B approach was reasonably concordant with the MD-PPAS approach and could be considered by researchers without the financial resources required to purchase MD-PPAS data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".