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What Are the Potential Savings From Steering Patients to Lower-Priced Providers? A Static Analysis
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What Are the Potential Savings From Steering Patients to Lower-Priced Providers? A Static Analysis

Sunita M. Desai, PhD; Laura A. Hatfield, PhD; Andrew L. Hicks, MS; Michael E. Chernew, PhD; Ateev Mehrotra, MD, MPH; and Anna D. Sinaiko, PhD, MPP
Steering patients who visit providers with above-median prices to their market’s median-priced provider would save 42%, 45%, and 15% of laboratory, imaging, and durable medical equipment spending, respectively.
Policies that promote competition in the healthcare delivery system, such as stricter antitrust enforcement, may limit the ability of increasingly large health systems to negotiate higher prices, which puts downward pressure on prices for healthcare services.24 Relatedly, curbing alleged anticompetitive practices by large systems, such as the inclusion of antisteering clauses in payer contracts, may also be effective.25 The presence of these contracting arrangements inhibits ability to reduce healthcare spending through consumer-directed strategies. Our results suggest that a majority of the potential savings would come from steering patients away from the hospital-based providers. For example, 32% of the 45% in overall estimated savings in imaging would come from simply shifting patients visiting an above-median hospital-based provider to the median-priced provider. In laboratory tests and imaging, focusing on patients visiting above-median hospital-based providers would account for 71% and 40% of overall estimated savings, respectively. However, due to the competitive power wielded by hospital systems, as discussed, steering patients away from these providers may be most difficult.

Moreover, specific market conditions could affect both the potential for savings from successfully steering patients to lower-cost providers and the ability of a payer to actually steer patients. Although the differences were not large, potential savings are higher in Northern compared with Southern California. This may reflect the fact that Northern Californian markets are less competitive, and, on average, less competitive markets have higher prices. Although less competitive markets may have greater opportunities for savings, influencing patient behavior may be more difficult in these markets. The dominant providers in these markets may have the means to more effectively retain their patient base through measures such as advertising. Moreover, longer-term effects of steering patients away from dominant providers in less competitive markets could also differ. For example, although our analyses hold price constant, providers in less competitive markets may wield their market power by threatening to leave the payer’s network or demanding higher prices in response to payer efforts to shift patients away from them.


This analysis has several limitations. First, it is limited to enrollees of a single insurer in a single type of plan, and savings are conditional on the network and range of provider prices in this sample. Wide variation in negotiated prices for individual insurers has been observed in other US regions,26,27 but changes in market structure since 2014 may have altered the extent of price variation and distribution of market share. By looking at a large insurer, we might expect less variation in prices than if we examined results from a smaller insurer with less market power. In contrast, studying an insurer with a broad network will yield savings greater than if the insurer had a small network (although narrowing the network may be one way to capture the savings).

Second, we assume that 3-digit zip codes are markets. Although sensitivity tests that defined smaller markets using 5-digit zip codes also demonstrated potential savings, in reality, markets may be bigger or smaller and are likely to differ based on the service and the burden on patients from travel. Future work should evaluate other geographies and commercial insurers.

Third, providers may be misclassified as freestanding or hospital owned if our method, which is based on the place of service codes in the claims, does not produce accurate designations. However, if the misclassification is not systematically related to the provider’s relative price in the market, noise due to such misclassification should not bias the results.

Fourth, our analysis does not consider that price variation and greater market share of higher-priced providers could be justified by variation in provider quality or in patients’ perceptions of quality. If providers who charge higher prices provide higher quality of care, then savings from shifting patients to lower-cost providers could be offset by reduced patient satisfaction or worse health outcomes. However, past work suggests that higher-priced providers do not necessarily generate better health outcomes compared with lower-priced providers.5 Also, variation in quality may be less of a concern in laboratory tests, imaging, and DME than in physician services.

Finally, our simulation does not consider potential long-term, general equilibrium effects of shifting a large volume of services to lower-priced providers or of higher-priced providers substantially reducing their prices. If lower-priced providers increase their share of patients and increase their prices in response, our simulated savings would be overestimates. If, instead, price competition due to enrollee shifting leads to further price decreases, our simulated savings would underestimate savings. Longer-term effects of changes in market share or other downward pressure on healthcare prices are important topics for further research.


For 3 sets of clinical services (laboratory testing, imaging, and DME), we observe both significant price variation and substantial market share among high-priced providers. If patients were to switch away from the highest-price providers, our analyses suggest savings of roughly 11% of total outpatient spending. These findings suggest that efforts to steer patients to providers where they pay lower prices or to negotiate lower provider prices could substantially reduce healthcare spending.

Author Affiliations: NYU School of Medicine (SMD), New York, NY; Harvard Medical School (SMD, LAH, ALH, MEC, AM), Boston, MA; Beth Israel Deaconess Medical Center (AM), Boston, MA; Harvard T.H. Chan School of Public Health (ADS), Boston, MA.

Source of Funding: This work was supported by a grant from California Public Employee and Retirement System to Harvard. Dr Desai was also supported by the Marshall J. Seidman Center for Studies in Health Economics and Health Care Policy at Harvard Medical School.

Author Disclosures: Dr Mehrotra is employed by Harvard, which, as a large employer, is working to address high prices; he has received grants from the Arnold Foundation and the Donaghue Foundation and has attended meetings of AcademyHealth and the Society of General Internal Medicine. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (SMD, LAH, MEC, AM, ADS); acquisition of data (SMD, AM); analysis and interpretation of data (SMD, LAH, ALH, MEC, ADS); drafting of the manuscript (SMD); critical revision of the manuscript for important intellectual content (SMD, LAH, ALH, MEC, AM, ADS); statistical analysis (SMD, ALH); provision of patients or study materials (SMD); and supervision (SMD).

Address Correspondence to: Sunita M. Desai, PhD, Department of Population Health, NYU School of Medicine, 227 E 30th St, New York, NY 10016. Email:

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