Table 2 Study characteristics
Author/Pub Year/Citation | Study Country | Study Setting and Model | Study Sample | Interventions | Outcomes | Overall Conclusion | Risk of Bias Score |
|---|---|---|---|---|---|---|---|
Panel A The effects of hospital price transparency | |||||||
  Wu et al. (2014) [44] | US | Quasi-experimental (pooled cross-sectional data), DID | 105,637 patients | The implementation of a private insurer-initiated price transparency program | The change in average cost per imaging test | Negative | 5 |
  C. Whaley et al. (2014) [45] | US | Association analysis (pooled cross-sectional data), GLM | 502,949 patients | Usage of the price transparency platform | Total payment amount (i.e., the sum of the patient and employer payments) at the procedure level (lab tests, imaging services, and clinician office visits) | Negative | 7 |
  Desai et al. (2016) [46] | US | Quasi-experimental (pooled cross-sectional data), Matching and DID | 354,187 outpatients | Availability of the price transparency tool. | Annual outpatient spending, outpatient out-of-pocket spending, use rates of the tool. | Positive | 7 |
  Desai et al. (2017) [47] | US (California) | Quasi-experimental (pooled cross-sectional data), Matching and DID | 843,533 beneficiaries | The implementation and usage of the price transparency tool | 1) individual-level spending 2) average service-level price for lab tests, office visits, and imaging services. | No effect | 6 |
  Lieber (2017) [48] | US | Quasi-experimental (pooled cross-sectional data), DID | 6208 employees (the unit of analysis rests on 387,774 procedures) | The sesearch behavior for price information through a given price transparency tool | The transacted price for procedures | Negative | 5 |
  C. Whaley et al. (2019) [49] | US | Quasi-experimental (pooled cross-sectional data), DID | 1) 214,746 patients for laboratory tests (the unit of analysis rests on 2,443,211 claims records) 2) 32,363 patients for imaging tests (the unit of analysis rests on 37,750 claims records) | The implementation of an online price transparency (PT) tool in 2010, and a reference pricing program (RP) in 2011 | The price of laboratory and imaging test | 1) No effect, for PT only. 2) Negative, for PT and RP. | 6 |
Brown (2019) [50] | US (New Hampshire) | Quasi-experimental (pooled cross-sectional data), DID | 811,553 enrollees in New Hampshire | The implementation of an out-of-pocket price transparency website | Total visit price, patients’ out-of-pocket price, and insurers’ reimbursement price | Negative | 6 |
Kobayashi et al. (2019) [51] | Japan (Tokyo) | Randomised controlled trial (pooled cross-sectional data), GLM | 1053 outpatients | A randomly presented price list about outpatient healthcare services | Total payment amount | Positive | 5 |
C. M. Whaley (2019) [52] | US | Quasi-experimental (longitudinal data), DID | 93,974 office visit providers and 16,502 lab test providers | The staggered and nationwide diffusion of an online price transparency platform | The price for laboratory tests and office visit services | 1) Negative for laboratory tests. 2) No effects for office visit services. | 8 |
Carey & Dor (2020) [53] | US (New York and Florida) | Association analysis (longitudinal data), DID | 8,616,184 inpatients in NY, and 9,802,568 inpatients in FL | The release of the CMS hospital charge report | The charges of hospital for inpatient services | Negative | 4 |
Christensen et al. (2020) [54] | US | Quasi-experimental (pooled cross-sectional data), DID and DDD | 1) 244,962 inpatients, and the unit of analysis rests on the charges and payments 2) 244,962 total payment records 3) 2,145,926 charge records | The disclosure date of price transparency website in each state | Charges and payments for 5 procedures | 1) Negative for charge 2) No effects for payment | 8 |
Panel B The effects of hospital quality transparency | |||||||
Outcome 1 The price of healthcare services and the payment of consumers | |||||||
  Dor et al. (2015) [55] | US | Quasi-experimental (pooled cross-sectional data), DID | 18,532 CABG inpatients and 54,301 PCI inpatients | The implementation of Hospital Compare mortality rankings | The transaction prices for CABG and PCI | 1) Negative in the growth rates 2) BUT Positive in the price level | 8 |
  Huang & Hirth (2016) [56] | US (California, Florida, New York, Ohio, Texas) | Quasi-experimental (longitudinal data), DID | Around 7000 nursing facility | The differential ratings of nursing homes | The private-prices in nursing homes | 1) Positive in the price level 2) Positive in the price and revenue differentials among higher- and lower-rated nursing homes | 6 |
Liu et al. (2016) [57] | China (Qian Jiang City) | Randomised controlled trial (longitudinal data), DID | 748,632 outpatient prescriptions | The public reporting (PR) about physicians’ prescribing information | Outpatients’ average expenditure | Negative | 5 |
Dor et al. (2020) [58] | US | Quasi-experimental (pooled cross-sectional data), DID and DDD | 20,773 CABG inpatients and 39,002 PCI inpatients | The implement of Hospital Compare, and hospitals’ differential rankings | The transaction prices for CABG and PCI | 1) Negative in the price level 2) BUT Positive for higher-rated hospitals | 8 |
Outcome 2 The premium of health insurance plans bonding with hospital networks | |||||||
  McCarthy & Darden (2017) [59] | US | Quasi-experimental (pooled cross-sectional data), RDD | 247,978 health plans | The introduction of the CMS quality star rating system for Medicare Advantage (MA) contracts | The premium of contracts | Positive for higher-rating contracts | 9 |
  McCarthy (2018) [60] | US | Quasi-experimental (pooled cross-sectional data), DID and FE model | 311,571 health plans | The disclosure of CMS Medicare Advantage (MA) star rating program in period t + 1 or t + 2 | The anticipated bids and premiums of health plans | 1) Positive for lower-quality plans 2) Negative for higher-quality plans | 9 |
  Polsky & Wu (2021) [61] | US | Association analysis (cross-sectional data), LM | 7706 health plans | A self-constructed hospital network quality factor | The premium of insurance plans | No effects | 3 |