What is a star worth to Medicare beneficiaries? A discrete choice experiment of hospital quality ratings
Bibliographic record
Abstract
Hospital quality ratings are widely available to help Medicare beneficiaries make an informed choice about where to receive care. However, how beneficiaries' trade-off between different quality domains (clinical outcomes, patient experience, safety, efficiency) and other considerations (out-of-pocket cost, travel distance) is not well understood. We sought to study how beneficiaries make trade-offs when choosing a hypothetical hospital. We administered an online survey that included a discrete choice experiment to a nationally representative sample of 1025 Medicare beneficiaries. On average, beneficiaries were willing to pay $1698 more for a hospital with a 1-star higher rating on clinical outcomes. This was over twice the value of the patient experience ($691) and safety ($615) domains and nearly 8 times the value of the efficiency domain ($218). We also found that the value of a 1-star improvement depends not only on the quality domain but also the baseline level of performance of the hospital. Generally, it is more valuable for low-performing hospitals to achieve average performance than for average hospitals to achieve excellence.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".