The Role of Decision Models in Health Care Policy
Bibliographic record
Abstract
BACKGROUND: In 2009, the Centers for Medicare and Medicaid Services (CMS) underwent a National Coverage Determination on computed tomography colonography (CTC) to screen for colorectal cancer. The Cancer Intervention & Surveillance Network developed decision models to inform this decision. The purpose of our study was to investigate the role of models in this decision. METHODS: We performed a descriptive case study. We conducted semistructured telephone interviews with members of the CMS coverage and analysis group (CAG) and Medicare Coverage and Analysis Advisory Committee (MEDCAC) panelists. Informed by previously published literature, we developed a coding scheme to analyze interview transcripts, MEDCAC meeting transcripts, and the final CMS decision memo. RESULTS: Four members of the CAG and 8 MEDCAC panelists were interviewed. The total number of codes across all study documents was 772. We found evidence that decision makers believed in the adequacy of models to inform decision making. In interview transcripts, the code Models Are Adequate to Inform was more frequent than the code Models Are Inadequate to Inform (47 times v. 5). Discussion of model conceptualization dominated the MEDCAC meeting (Model Conceptualization assigned 113 times) and was frequently discussed during interviews (Model Conceptualization assigned 84 times). We also found evidence that the models helped to focus the policy discussion. Across study documents, the codes Focus on Cost, Focus on Clinical-Health Impact, and Focus on Inadequacy of Evidence Base were assigned 99, 98, and 97 times, respectively. CONCLUSIONS: Decision makers involved in the CTC decision believed in the adequacy of models to inform coverage decisions. The model played a role in focusing the CTC coverage policy discussion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".