INTRODUCTION
Starting January 1, 2021, most hospitals in the USA have been required by federal regulation publicly to post machine-readable data files of previously confidential prices.1 Initial compliance with the regulation has been low, with early reports indicating compliance levels of 17–35%.2, 3
This study aims to assess changes in hospital price transparency compliance over time in an effort to (a) update the earlier literature and (b) provide evidence that can potentially guide policymakers seeking to better incentivize compliance with this regulation.
METHODS
This study used Turquoise Health data on price transparency compliance from September 29 in 2021 and 2022. Turquoise Health data has been increasingly used in the price transparency literature.4, 5 Turquoise Health reports a compliance score for each hospital ranging from 1 to 5. A score of 1 means no machine-readable standard charge is available, while a score of 5 represents a machine-readable file with all required data elements present, as mandated per the regulation.6 This definition has not changed over time. We defined compliance as a 0/1 indicator variable that equals 1 for a given hospital if the compliance score is 5, and 0 otherwise.
We created a longitudinal sample by linking these cross-sections to assess compliance score changes at the hospital level. We used the CMS Provider of Services file (Q2 2022) to determine ownership type (private non-profit, private for-profit, government, and other), bed size (100 or less, 101 to 250, and 251 or more), rural/urban status, and census region and the 2020 Medicare Cost Reports to determine system affiliation. We used multivariable logistic and linear regressions on the sample of non-compliant hospitals in 2021 to assess correlates of becoming compliant as of 2022. This study was considered exempt by our IRB. All analyses were conducted in Stata 15.0.
RESULTS
Our analytical dataset consisted of 4834 hospitals. Overall compliance improved over time, with 23.8% of hospitals compliant with the regulation in 2021 and 35.9% in 2022 (Table 1). This was driven by 725 hospitals (15.0%) becoming compliant. We also found that 139 hospitals (2.9%) transitioned away from being compliant to becoming non-compliant.
Table 1.
Compliance Snapshots, by Year and Characteristic
| Overall | Compliant in 2021 | Compliant in 2022 | |
|---|---|---|---|
| Hospitals, no. (%) | 4834 | 1149 (23.8%) | 1735 (35.9%) |
| By bed size, no. (%) | |||
| 100 or fewer | 2595 | 521 (20.08%) | 737 (28.40%) |
| 101–250 | 1121 | 321 (28.64%) | 503 (44.87%) |
| 251+ | 1118 | 307 (27.46%) | 495 (44.28%) |
| Ownership, no. (%) | |||
| For-profit | 1137 | 216 (19%) | 389 (34.21%) |
| Government | 939 | 200 (21.30%) | 246 (26.20%) |
| Non-profit | 1916 | 553 (28.86%) | 813 (42.43%) |
| Other | 842 | 180 (21.38%) | 287 (34.09%) |
| Region | |||
| Midwest | 1377 | 398 (28.90%) | 510 (37.04%) |
| Northeast | 576 | 135 (23.44%) | 216 (37.50%) |
| Pacific | 43 | 9 (20.93%) | 19 (44.19%) |
| South | 1939 | 395 (20.37%) | 670 (34.55%) |
| West | 899 | 212 (23.58%) | 320 (35.60%) |
| Setting | |||
| Rural | 1595 | 399 (25.02%) | 565 (35.42%) |
| Urban | 3239 | 750 (23.16%) | 1170 (36.12%) |
| System-owned | |||
| Yes | 3065 | 805 (26.26%) | 1326 (43.26%) |
| No | 1769 | 344 (19.45%) | 409 (23.12%) |
The analytic sample consists of 4834 hospitals for which there is non-missing compliance score data in 2021 and 2022 and for which hospital characteristics could be located. Compliance in each year is defined as meeting the maximum score of 5
For the 3685 hospitals that were non-compliant in 2021, system affiliation and bed size (101–250 and 251+) were associated with higher likelihoods of becoming compliant (Table 2). System affiliation displayed the largest effect size: initially non-compliant hospitals affiliated with a health system were 3.62 times—or 18.9 percentage points—more likely to become compliant than unaffiliated hospitals. Meanwhile, urban status and public ownership (relative to non-profit) were associated with a lower likelihood of becoming compliant. There was no association between change in compliance and region.
Table 2.
Characteristics Associated with Improving Compliance
| Characteristic | Logistic regression model Odds ratio (95% CI) | Linear regression model Coefficient (95% CI) |
|---|---|---|
| Bed size (ref: 100 or fewer beds) | ||
| 101–250 | 2.43** (1.66–3.55) | 0.13** (0.06–0.19) |
| 251+ | 3.07** (1.83–5.14) | 0.16** (0.08–0.25) |
| Ownership structure (ref: non-profit) | ||
| For-profit | 0.75 (0.46–1.22) | −0.05 (−0.13 to 0.03) |
| Government | 0.46** (0.32–0.65) | −0.09** (−0.13 to −0.04) |
| Other | 0.82 (0.63–1.08) | −0.04 (−0.08 to 0.01) |
| Region (ref: northeast) | ||
| Midwest | 0.84 (0.52–1.35) | −0.02 (−0.09 to 0.05) |
| Pacific | 1.71 (0.19–15.12) | 0.08 (−0.31 to 0.48) |
| South | 0.89 (0.54–1.46) | −0.01 (−0.09 to 0.06) |
| West | 0.70 (0.34–1.41) | −0.04 (−0.14 to 0.05) |
| Setting (ref: rural) | ||
| Urban | 0.57** (0.42–0.78) | −0.08** (−0.12 to −0.04) |
| System-owned (ref: no) | ||
| Yes | 4.62** (3.26–6.56) | 0.19** (0.15–0.23) |
This table presents multivariable logistic regression and multivariable linear regression results on the sample of 3685 hospitals in our analytic sample that were not compliant in the baseline period. Standard errors are clustered at the state level. *p<0.1; **p< 0.05
DISCUSSION
Compliance with the Hospital Price Transparency Rule improved overall and across all categories of bed size, ownership type, region, urban location, and system affiliation but remains low. Of initially non-compliant hospitals, large hospitals and system-affiliated hospitals were most likely to become compliant, while government-owned hospital and urban hospitals were least likely to become compliant.
The strong compliance improvements for large and system-affiliated hospitals are encouraging for researchers and policymakers seeking to use hospital price transparency files to examine pricing drivers for facilities that contribute substantially to health expenditure.
Moreover, the differential rate of transition from non-compliance to compliance suggests that there is room for policy-based incentives. The relative lag of small and government-owned hospitals may reflect a lack of information technology (IT) capacity at these institutions. Regulators should be mindful of this potential limitation and policies providing appropriate IT resources to certain hospitals may facilitate greater compliance.
Declarations
Conflict of Interest
The authors have no conflicts of interest to declare.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Department of Health and Human Services Department. Medicare and Medicaid Programs: CY 2020 Hospital Outpatient PPS Policy Changes and Payment Rates and Ambulatory Surgical Center Payment System Policy Changes and Payment Rates. Price Transparency Requirements for Hospitals To Make Standard Charges Public [Internet]. Fed Regist. 2019 [cited 2022 Oct 1]. Available from: https://www.federalregister.gov/documents/2019/11/27/2019-24931/medicare-and-medicaid-programs-cy-2020-hospital-outpatient-pps-policy-changes-and-payment-rates-and. Accessed 1 Oct 2022.
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- 6.Turquoise Health. The state of hospital price transparency, with pictures! February 2021. https://blog.turquoise.health/state-of-hospital-price-transparency-with-pictures/. Accessed 5 Dec 2022.