Playing by the Rules? Tracking U.S. Hospitals' Responses to Federal Price Transparency Regulation
Bibliographic record
Abstract
GOAL: As of January 1, 2021, the Centers for Medicare & Medicaid Services requires most U.S. hospitals to publish pricing information on their website to help consumers make decisions regarding services and to transform negotiations with health insurers. For this study, we evaluated changes in hospitals' compliance with the federal price transparency rule after the first year of enactment, during which the Centers for Medicare & Medicaid Services increased the penalty for noncompliance. METHODS: Using a nationally representative random sample of 470 hospitals, we assessed compliance with both parts of the hospital transparency rule (publishing a machine-readable price database and a consumer shopping tool) in the first quarter of 2022 and compared its baseline level in the first quarter of 2021. Using data from the American Hospital Association and Clarivate, we next assessed how compliance varied by hospital factors (ownership, number of beds, system membership, teaching status, type of electronic health record system), market factors (hospital and insurer market concentration), and the estimated change in penalty for noncompliance. PRINCIPAL FINDINGS: By early 2022, 46% of hospitals had posted both machine-readable and consumer-shoppable data, an increase of 24% from the prior year. Almost 9 in 10 hospitals had complied with the consumer-shoppable data requirement by early 2022. Larger hospitals and public hospitals had lower probabilities of baseline compliance with the machine-readable and consumer-shoppable requirements, respectively, although public hospitals were significantly more likely to become compliant with the consumer-shoppable requirement by 2022. Higher hospital market concentration was also associated with higher baseline compliance for both the machine-readable and consumer-shoppable requirements. Furthermore, our analyses found that hospitals with certain electronic health record systems were more likely to comply with the consumer-shoppable requirement in 2021 and became increasingly compliant with the machine-readable requirement in 2022. Finally, we found that hospitals with a larger estimated penalty were more likely to become compliant with the machine-readable requirement. PRACTICAL APPLICATIONS: Longitudinal analyses of compliance with the federal price transparency rule are valuable for monitoring changes in hospitals' behavior and assessing whether compliance changes vary systematically for specific types of hospitals and/or market structures. Our results suggest a trend toward increased hospital compliance between 2021 and 2022. Although hospitals perceive the consumer-shopping tools as being the most impactful, the value of this information depends on whether it is comprehensible and comparable across hospitals. The new price transparency rule has facilitated the creation of new data that have the potential to significantly alter the competitive landscape for hospitals and may require hospital leaders to consider how their organizational strategies change concerning their engagement with payers and patients. Finally, greater price transparency is likely to bolster national policy discussions related to price variation, affordability, and the role of regulation in healthcare markets.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".