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2026
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| Online Access: | https://doi.org/10.5281/zenodo.20228819 |
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| _version_ | 1866902073942999040 |
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| author | Rice, James P Jr. |
| author_facet | Rice, James P Jr. |
| contents | <p>Hospital charges in the United States bear little relation to costs, negotiated prices, or Medicare reimbursement --- yet no formal model explains the structure of these markups. We develop a log-linear pricing model in which the charge-to-payment ratio (CPR), the ratio of a hospital's listed charge to its Medicare payment, depends on market concentration, private payer share, and regulatory transparency. The model generates testable monotonicity conditions: CPR weakly increases in each channel. We calibrate against 145,879 hospital-DRG pairs across 2,906 hospitals drawn from CMS Medicare Inpatient Provider Utilization and Payment data. The median CPR is 5.63 (95% CI: [5.61, 5.64]), ranging from 1.23 in Maryland --- which operates an all-payer rate-setting system --- to 11.23 in Nevada. Beyond calibration, we derive two overidentifying predictions that the model could fail. First, the model predicts that hospitals with higher volume (a market-power proxy) should exhibit higher CPR; if volume and CPR were uncorrelated or negatively correlated, the opacity-market-power mechanism would be falsified. The observed Spearman correlation is 0.36 [0.32, 0.39] --- the prediction survives. Second, the model predicts that higher-complexity DRGs (greater information asymmetry) should carry higher CPR; kidney transplants (DRG 652) at 12.66x should exceed major joint replacements (DRG 470) at 5.87x. This ordering holds --- the prediction survives. These overidentifying tests transform the model from a curve-fitting exercise into a framework that made specific bets and won them. The contribution is organizational: a common parametric structure unifying four previously disconnected regularities --- the Maryland outlier, Nevada extreme, volume-CPR correlation, and DRG heterogeneity --- into a framework whose falsifiable predictions survived contact with the data.</p> <p>---</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20228819 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | The Opacity Premium: A Model of Hospital Charge-to-Payment Ratios Rice, James P Jr. autonomous research adversarial validation multi-agent systems THESIS framework hospital pricing charge-to-payment ratio health care markets price opacity <p>Hospital charges in the United States bear little relation to costs, negotiated prices, or Medicare reimbursement --- yet no formal model explains the structure of these markups. We develop a log-linear pricing model in which the charge-to-payment ratio (CPR), the ratio of a hospital's listed charge to its Medicare payment, depends on market concentration, private payer share, and regulatory transparency. The model generates testable monotonicity conditions: CPR weakly increases in each channel. We calibrate against 145,879 hospital-DRG pairs across 2,906 hospitals drawn from CMS Medicare Inpatient Provider Utilization and Payment data. The median CPR is 5.63 (95% CI: [5.61, 5.64]), ranging from 1.23 in Maryland --- which operates an all-payer rate-setting system --- to 11.23 in Nevada. Beyond calibration, we derive two overidentifying predictions that the model could fail. First, the model predicts that hospitals with higher volume (a market-power proxy) should exhibit higher CPR; if volume and CPR were uncorrelated or negatively correlated, the opacity-market-power mechanism would be falsified. The observed Spearman correlation is 0.36 [0.32, 0.39] --- the prediction survives. Second, the model predicts that higher-complexity DRGs (greater information asymmetry) should carry higher CPR; kidney transplants (DRG 652) at 12.66x should exceed major joint replacements (DRG 470) at 5.87x. This ordering holds --- the prediction survives. These overidentifying tests transform the model from a curve-fitting exercise into a framework that made specific bets and won them. The contribution is organizational: a common parametric structure unifying four previously disconnected regularities --- the Maryland outlier, Nevada extreme, volume-CPR correlation, and DRG heterogeneity --- into a framework whose falsifiable predictions survived contact with the data.</p> <p>---</p> |
| title | The Opacity Premium: A Model of Hospital Charge-to-Payment Ratios |
| topic | autonomous research adversarial validation multi-agent systems THESIS framework hospital pricing charge-to-payment ratio health care markets price opacity |
| url | https://doi.org/10.5281/zenodo.20228819 |