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Sinaiko AD, Joynt KE, Rosenthal MB. Association Between Viewing Health Care Price Information and Choice of Health Care Facility. JAMA Intern Med. 2016;176(12):1868–1870. doi:10.1001/jamainternmed.2016.6622
Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.
In the United States, prices for health care services differ dramatically within a single geographic location, often without commensurate differences in quality.1 Transparency tools that provide price information to help patients identify lower-cost services are a strategy to reduce health care spending.2 Price information in combination with insurance benefit design that shares savings when patients choose low-cost health care facilities (eg, reference pricing) has led to lower spending3; however, the impact of price information on patient choices for patients in commercial insurance without such benefit design incentives is largely unknown. A prior study reported that payments for laboratory tests and advanced imaging services were 13% to 14% lower among transparency tool users than nonusers but could not disentangle whether this was due to inherent differences between users and nonusers,4 such as educational attainment, which can also be correlated with choice of health care facility.
This study was classified as exempt by the institutional review board at the Harvard T. H. Chan School of Public Health. We examined the impact of Aetna’s web-based, real-time, personalized episode-level price transparency tool on choice of health care facility for 8 services. The tool is offered to 94% of commercial enrollees; of these, 3.5% used the tool and constitute our sample.5
We linked administrative enrollment and medical claims data to detailed search data for enrollees aged 19 to 64 years who underwent carpal tunnel release, cataract removal, colonoscopy, echocardiogram, mammogram, several magnetic resonance imaging and computed tomographic imaging services, sleep studies, or upper endoscopy during the period 2010 through 2012 (N = 181 563).
To isolate the effect of viewing prices for a procedure one is planning to undergo, we compared whether patients who viewed price estimates for their specific procedure prior to undergoing it were more likely to choose lower-priced health care facilities than patients who queried the tool about other procedures, or who underwent their procedure in the baseline year before the tool was widely available. The price measure was the health care facility’s price estimate shown by the tool, which reflects the patient’s price information in advance of receiving care. We also examined whether total procedure spending differed between groups. Analyses estimated multivariate linear regression models controlling for patient age, sex, health status as measured by the Elixhauser index,6 month, year, and hospital referral region.
Patients who viewed price estimates prior to undergoing these procedures were more frequently male than other tool users; other differences, although significantly different primarily due to large sample sizes, were small (Table 1).
Patients who viewed price estimates prior to having their procedure chose health care facilities with lower relative price estimates than other patients for imaging services (facility price estimate in the 46th percentile in the market vs 54th percentile by the comparison groups; P < .001) and sleep studies (facility price estimate in the 42nd vs 47th percentile; P = .001) (Table 2). Searching for price information was also associated with lower adjusted total spending of $131.40 (12%) (P < .001) for imaging and of $103.50 (6%) (P = .06) for sleep studies.
Among early adopters, those searching for prices on imaging services and sleep studies chose health care facilities with lower prices and incurred lower spending for imaging studies.
This study focused on 1 carrier, and data include the first 2 years that the tool was available; future research is needed to determine whether these patterns hold if and when these tools are used more broadly. We also cannot observe whether patients used the tool not to comparison shop but to become educated on costs in advance of receiving medical care.
Low use of the tool and modest effects for other services suggest that barriers to use remain. Engaging patients with price information will be important to allow patients to better anticipate and plan their medical spending and to achieve a broad impact on health care spending.
Corresponding Author: Anna D. Sinaiko, PhD, MPP, Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, 677 Huntington Ave, Room 409, Boston, MA 02115 (firstname.lastname@example.org).
Published Online: October 24, 2016. doi:10.1001/jamainternmed.2016.6622
Author Contributions: Dr Sinaiko had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Sinaiko, Rosenthal.Acquisition, analysis, or interpretation of data: Sinaiko, Joynt.Drafting of the manuscript: Sinaiko.Critical revision of the manuscript for important intellectual content: Joynt, Rosenthal.Statistical analysis: Sinaiko, Rosenthal.Obtained funding: Sinaiko.
Conflict of Interest Disclosures: None reported.
Funding/Support: This work was supported by the Robert Wood Johnson Foundation (grant 71412).
Role of the Funder/Sponsor: The Robert Wood Johnson Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Previous Presentations: Some of the findings reported here were presented at the American Society of Health Economists Biannual meeting; June 14, 2016; Philadelphia, Pennsylvania; and at the AcademyHealth Annual Research Meeting; June 27, 2016; Boston, Massachusetts.
Additional Contributions: We thank Chris Riedl, BS; Jessica Harris, MBA, MPP; Charity Boutte, MA; and others at Aetna for help accessing study data, and Chapin White, PhD, for helpful comments on an earlier draft. They received no compensation for such contributions. Harlan Pittell, BA, provided excellent research assistance; a portion of his salary was supported by the grant from the Robert Wood Johnson Foundation that funded this work.
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