Assessment and Measurement of Patient-Centered Medical Home Implementation: The BCBSM Experience
Bibliographic record
Abstract
PURPOSE: Our goal was to describe an approach to patient-centered medical home (PCMH) measurement based on delineating the desired properties of the measurement relative to assumptions about the PCMH and the uses of the measure by Blue Cross Blue Shield of Michigan (BCBSM) and health services researchers. METHODS: We developed and validated an approach to assess 13 functional domains of PCMHs and 128 capabilities within those domains. A measure of PCMH implementation was constructed using data from the validated self-assessment and then tested on a large sample of primary care practices in Michigan. RESULTS: Our results suggest that the measure adequately addresses the specific requirements and assumptions underlying the BCBSM PCMH program-ability to assess change in level of implementation; ability to compare across practices regardless of size, affiliation, or payer mix; and ability to assess implementation of the PCMH through different sequencing of capabilities and domains. CONCLUSIONS: Our experience illustrates that approaches to measuring PCMH should be driven by the measures' intended use(s) and users, and that a one-size-fits-all approach may not be appropriate. Rather than promoting the BCBSM PCMH measure as the gold standard, our study highlights the challenges, strengths, and limitations of developing a standardized approach to PCMH measurement.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".