Physician-Mediated Interventions to Lower Medical Expenditures Under Risk-Based Contracts: A Systematic Review

ABSTRACT

Objectives: Provider organizations are increasingly entering risk-based payment contracts with incentives to minimize medical expenditures. Little is known about physicians’ role in controlling costs. This systematic review aims to identify and characterize physician-mediated cost-management interventions in risk-bearing organizations, assess their effectiveness, and evaluate the quality of the literature.
Study Design: Systematic literature review.
Methods: We searched PubMed and EconLit for studies published between 2000 and 2021 reporting physician-mediated interventions intended to reduce medical expenditures in risk-bearing provider organizations. We included quantitative studies evaluating single interventions, quantitative survey-based studies, and qualitative case studies. The quality of the quantitative studies was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework.
Results: Twenty-seven studies were identified, including 12 quantitative evaluations, across diverse provider settings and payment models. We found 5 types of interventions: decision support and performance review, individual financial incentives, physician-led care management, expanded access, and provider-facing price transparency. All but 1 evaluation study found a statistically significant reduction in spending or utilization, but no intervention achieved substantial savings relative to total medical expenditures. The quality of this literature is low, with only 3 studies using a randomized controlled design. Generalizability of results to different provider contexts and payment models remains unclear.
Conclusions: There is a striking scarcity of high-quality studies on physician-mediated interventions to manage total medical expenditures. The limited evidence to date suggests that no single intervention has a substantial impact on total medical expenditures. Risk-bearing providers have limited guidance in the literature on the most effective practices clinicians can adopt to improve cost-related performance in risk-based contracts.

Am J Manag Care. 2026;32(Spec. No. 3):SP164-SP175. https://doi.org/10.37765/ajmc.2026.89907

As US health care spending grows, so does the consensus among health system stakeholders that value-based payment models are critical to controlling the rate of increase.^1-3 These payment models transfer the risk of high medical spending from payers to provider organizations to encourage delivery of high-quality care while also controlling health care expenditures. In turn, physicians within risk-bearing organizations must adopt new approaches to care to achieve these goals. As of 2022, 40% of traditional Medicare (TM) and Medicaid payments flowed through risk-based payment models, and policy makers hope to increase the share in TM to 100% by 2030.^3,4 Commercial payers are increasingly interested in risk sharing as well, with 35% of commercial payments and 57% of Medicare Advantage (MA) payments passing through risk-based models in 2022 (vs 28% and 49%, respectively, in 2017).⁵ In total, 41% of all payments flowed through risk-based contracts in 2022 vs 34% in 2017.^4,5

However, our knowledge of strategies adopted by provider organizations to encourage physicians to modify their practice to manage spending has not kept pace with this growth. Much of the literature on risk-based payment models tries to estimate the causal effect of payment models on spending or quality outcomes.^6-9 These studies treat provider organizations as black boxes: Payment incentives enter, and spending outcomes emerge. Less is known about how specific provider organizations seek to influence physician behavior to achieve these spending outcomes and which specific strategies and interventions have proven successful. Thus, although data on the overall effectiveness of such payment strategies are critical for payers tasked with model design, the literature is less helpful for provider organizations seeking guidance on the best strategies for succeeding under these payment models.

This review focuses on strategies that provider organizations might adopt to influence physician behavior within their organizations (hereafter referred to as physician-mediated interventions). These are interventions implemented by provider organizations that seek to manage medical expenditures by directly influencing physician behavior, whether through direct payment incentives or other approaches such as the adoption of algorithms to identify low-value care. A typical example of a non–physician-mediated intervention is care management. Although care management has been more widely studied, it is typically delivered by nonphysicians or even nonclinical administrative staff and thus occurs largely outside physicians’ scope of responsibility and attention. Given the minimal effectiveness of care management in reducing spending to date, however, it may be necessary to enlist physicians to achieve meaningful reductions in health spending.^10,11 Details are lacking about the specific form and efficacy of physician-mediated interventions in risk-bearing organizations, despite calls for reliable studies.¹²

To date, no systematic reviews have summarized what is known about physician-mediated interventions to curb spending and preserve quality within risk contracts. Although there is substantial content in the gray literature on interventions to reduce spending, this evidence is largely experiential and anecdotal, making it difficult for provider organizations to assess its quality and applicability.^13,14 In this article, we therefore sought to assess the scale and quality of academic research on these interventions, evaluate their efficacy, and describe the enablers and challenges that provider organizations encounter in the course of implementation.

METHODS

Study Selection

We conducted a systematic review to identify physician-mediated interventions to reduce health care spending. We used the Populations, Interventions, Comparison, and Outcome (PICO) framework to refine our inclusion criteria (eAppendix 1 [eAppendices available at ajmc.com]) and followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. We searched PubMed and EconLit to identify studies meeting our search criteria, requiring eligible studies to match with a search term from each PICO criterion (Figure). Our first search was hypothesis-driven, based on keywords we expected to appear in relevant articles. Our second search was more exploratory, using Medical Subject Headings terms to identify interventions not captured using the initial strategy. Exact search terms are in eAppendix 2. Searches were conducted in November 2021.

We excluded articles focused on pediatric populations, published outside the US, or published before 2000. Identified studies were exported into Covidence, an online software for literature reviews.¹⁵ In the first round of screening, pairs of reviewers independently evaluated article titles and abstracts to identify candidates for full text review; conflicts were resolved by the full group of 3 authors. We also examined prior related systematic reviews for potentially relevant references and added them to the abstract review queue, but did not advance them to the next stage of screening. Pairs of reviewers next performed a full text review; conflicts were again resolved by the full group of authors. Finally, all articles that received at least 1 “include” vote were examined to identify additional candidate articles from their references.

Data Extraction and Quality Assessment

Because our included studies represented diverse methodological approaches and provider contexts, we designed a flexible abstraction tool to capture the relevant contributions from each study (eAppendix 3). For all studies, we extracted demographic information, the methodological approach, information about the provider context, a description of the relevant intervention, and observations about its implementation process. For studies that included quantitative evaluations of initiatives, we also recorded each paper’s main results and an evaluation of the quality of evidence using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework.¹⁶ Studies received a score from 0, indicating the best quality, to 2, indicating the lowest. Qualitative case studies and survey-based studies that did not include an evaluation of an initiative received the worst possible score (2).

RESULTS

Description of Included Studies

Our searches yielded 2076 unique abstracts (eAppendix 4). Of these, 196 (9%) advanced to the full text review stage, and of those, 27 (14%) were included in the final review. Twelve studies were quantitative evaluations of an initiative, 10 were qualitative case studies, and 5 were survey-based studies that did not include evaluations of specific initiatives (Table 1^17-43 [part A and part B]). Among the quantitative evaluations, 3 were randomized controlled trials (RCTs), 4 were cohort studies with a contemporaneous comparison group, and 5 were cohort studies without a comparison group (ie, pre–post design). The median year of publication was 2016.

Clinical and Financial Settings of Providers in Included Studies

Our studies were set in a range of health care organizations, including independent physician practices, large integrated health systems, and physician-owned health maintenance organizations. Eight studies described interventions specifically targeting only primary care team members.^17-24 For example, Tanio and Chen²² is a case study of the early experience of Chen Med, the now-large primary care–based organization providing care for a capitated MA population.⁴⁴ Six studies described interventions targeting a single specialty other than primary care,^25-30 such as Shah et al,²⁵ which studied a virtual health initiative in a single health system’s neurology department. The remaining studies considered cost savings at the level of one or many multispecialty provider organizations, patient populations, or buckets of spending that cut across specialties. For example, Agnew et al³¹ studied efforts made by physician groups to reduce spending across specialties on injectable drugs.

The studies considered providers with a range of risk contracts, and often more than 1 at a given institution. For example, some studies^22,27 described organizations with a global budget or varying exposure to shared savings and losses, whereas others described organizations in episode-based payment models.^29,30 Medicare-based alternative payment models were well represented, including the Medicare Shared Savings Program,^18,32,33 Pioneer Accountable Care Organizations (ACOs),³⁴ the Bundled Payments for Care Improvement initiative,²⁹ and the Oncology Care Model.²⁸

Description of Interventions

Seventeen studies described interventions involving clinical decision support tools and/or the management of clinician performance to achieve better adherence to clinical guidelines related to utilization (Table 1^17-43). These interventions included the use of real-time or nearly real-time feedback such as automated decision support workflows integrated with electronic health records, asynchronous alerts sent to physicians based on manual review, individual or small-group coaching to increase adherence to clinical guidelines,^18,32,34-37 and the dissemination of peer-benchmarked performance reports enabling physicians to assess their relative performances.³⁶ For example, one intervention involved integrating a utilization management checklist into an electronic ordering system for imaging studies.³⁸

Eight studies were of interventions establishing clinician financial incentives, all using qualitative or survey-based designs. Some risk-bearing provider organizations based physician incentive payments on individual performance; one hospital in a bundled payments model passed through a portion of the shared savings from each surgical episode to the individual surgeon responsible.²⁹ Other providers incentivized physicians based on group performance by distributing the pool of shared savings equally among physicians²⁸ or in proportion to their individual performance on quality metrics.²⁶ Incentives variously reflected performance on cost of care, care quality and guideline adherence, patient experience, and citizenship. Notably, some organizations based their performance payments partly on the volume of evaluation and management visits, an interesting resolution to the tension between reducing utilization and improving access.²⁸

Seven studies described interventions that expanded access to care. Six were survey-based or case studies, and the interventions generally entailed expanded access to primary care using walk-in hours or extended hours on nights and weekends.^{20,22,28,33,34,39} For example, the case study of an early ChenMed clinic highlighted its efforts to ensure that physicians had small panels, offered walk-in hours, directly dispensed medications to patients, and frequently consulted each other on their approach to patient care.²² The sole quantitative study was of a telehealth intervention within a neurology practice: Patients were permitted to substitute telehealth for in-person visits following an initial in-person visit to reduce the total volume of in-person visits.²⁵

There were 2 studies of physician-led care management interventions.^17,27 These resembled traditional care management programs, but with physicians playing roles often reserved for other disciplines—for example, surgeons in an orthopedics practice introduced decision aids to patients to help avoid unnecessary surgeries.²⁷ There were 3 studies of price transparency interventions, all quantitative. Each intervention involved showing physicians price information at the point of order—for example, showing primary care physicians in an ACO the median paid price of diagnostic orders.¹⁹

Findings From Studies That Included Quantitative Evaluations

Twelve studies presented quantitative evaluations. Study samples were described in terms of the number of participating physicians and the number of patients (Table 2^{17,18,21,23,25,27,30,35,38,40-42} [part A, part B, and part C]). In the first group, the physician samples size ranged from 37 to 1205, and the patient samples size ranged from 268 to approximately 10,000. Some samples were drawn from members of a single health plan, patients with a specific diagnosis, or all patients visiting a specific practice.

Typical outcome measures were the volume of specific tests or procedures, such as the proportion of patients with osteoarthritis receiving joint replacement or hospitalization rates, or, more rarely, a measure of aggregate utilization such as total medical expenditures. One study considered utilization growth rates.³⁸ All studies but one⁴⁰ reported statistically significant results for at least 1 key outcome linked to medical expenditures. The magnitude of changes in the outcome metrics, however, varied widely, as did the potential impact on total medical spending. For example, one study found a 38% reduction in the proportion of patients with osteoarthritis receiving a total knee replacement,²⁷ and another observed an 87% reduction in unnecessary cardiology referrals during simulated primary care visits.¹⁸ By contrast, another study found a 6% reduction in paid claims surrounding arthroplasty episodes.³⁰ Therefore, although the impacts on specific aspects of utilization were sometimes large, these were relatively constrained areas of utilization within a health system or a representative patient population.

Quality Appraisal of Included Studies

Overall, the quality of the literature we found was low. We identified just 12 studies that attempted a quantitative evaluation of an initiative, and of these, only 3 employed a randomized design. On a scale from 0 (best) to 2 (worst), with qualitative and survey-based studies receiving a score of 2, the mean (SD) quality score was 1.6 (0.6). The mean quality among the quantitative studies (1.0) was somewhat higher. For the pre–post studies, a major barrier to credibility was the inability to control for secular trends. For example, Sistrom et al³⁸ found that a real-time utilization management workflow integrated into an electronic order entry system reduced the rate of growth of outpatient procedures in a health system. However, the authors noted that there was a national trend during the same period of slowing growth (or even shrinking) of the procedure rates. A study by Ip et al³⁵ of a similar intervention had a similar finding; however, this study appeared to not control for possible shifts in patient characteristics between the pre- and post periods. Hawes et al¹⁷ showed that a care management intervention targeting high utilizers was associated with a decline in those patients’ utilization. However, because the sample was deliberately composed of outlier utilizers from the preperiod, mean reversion alone could explain lower observed utilization for this group in the post period.

A common challenge for the studies with nonrandom, simultaneous control groups was endogeneity bias. In several of these studies, adoption of the intervention was optional and nonadopters were treated as a control group. For example, Burgon et al¹⁸ found that primary care physicians who completed an optional digital training improved more on efficiency metrics than a control group of physicians not offered the training. However, the treatment group excluded physicians who were offered the training but opted not to complete it, thus making the treatment and control groups incomparable; for example, the treatment group may have included only those physicians who believed they needed additional training to deliver more efficient care.

Although the 3 RCTs were free of these defects, each was potentially vulnerable to bias resulting from spillover effects from the treatment group to the control group. For example, Chien et al⁴⁰ found that when randomly selected physicians were shown data on the cost of radiology services, they ordered fewer imaging studies than physicians not shown prices. However, it is plausible that the control group was nevertheless influenced by this intervention through informal information sharing with the treatment group. Although this possibility does not undermine the authors’ conclusions that the intervention reduced order rates (in fact, it suggests that the authors potentially understated the effect), it does illustrate the difficulty of carefully identifying treatment effects, even in randomized trials.

DISCUSSION

In this systematic review of physician-mediated cost management interventions attempted by risk-bearing provider organizations, we identified just 27 relevant studies, with fewer than half including a quantitative evaluation of the intervention. Despite growing adoption of risk-based payment models, there are very few high-quality studies to guide clinicians and their organizations seeking specific tactics that they can use to succeed in these models. The result is that the literature affords little guidance to clinicians for meeting spending benchmarks when entering risk-based contracts. Although providers often perform (occasionally rigorous) analyses of their own data to identify promising opportunities for cost savings, often relying on consultants or health tech vendors, there are few examples in the literature of specific interventions rigorously shown to achieve those savings. The danger is that champions of cost management in provider organizations will be inadequately armed to persuade executive decision makers to take action; investing in cost management requires a leap of faith.

There are several plausible explanations for the paucity of literature. First, provider organizations are typically executing many unrelated operational initiatives in parallel, making it difficult to identify the impact of any one intervention. For example, a study of a value-based payment program in California enumerated broad categories of interventions that physician organizations used to control cost and increase quality, some of which were physician-mediated. However, this study did not offer details of implementation or provide data on the interventions’ effectiveness, potentially limiting its usefulness as guidance to managers of risk-bearing provider organizations.⁴⁵ This is especially relevant to studies of broad outcomes such as total medical expenditures, which are sensitive to a large variety of unrelated factors, and may explain why most studies focused on narrower categories of spending. Second, high-quality randomized trials can pose operational and ethical challenges; moreover, there is rarely a business case for such an approach, which, if the provider believes the initiative will work, leaves savings on the table. And third, providers have no incentive to share management strategies with competitors, even in the rare event that one has been rigorously shown to achieve savings. Although these are barriers to publishing rigorous, peer-reviewed research, a substantial gray literature has emerged on physician-mediated interventions. In general, such reports lack rigorous study designs that would allow for causal inference, making it difficult to judge the true effectiveness of such interventions.^13,14 Further research should identify promising interventions from the gray literature and subject them to more rigorous implementation trials to improve their usefulness to organizations considering adopting such interventions.

The literature we did find provided no home runs with respect to cost management. When substantial decreases in spending did occur, they affected just a small proportion of total medical spending. It is likely necessary to deploy a suite of tactics to meaningfully reduce total medical expenditures. On the other hand, most providers lack the organizational capacity, strategic commitment, and physician buy-in required to execute a complex, multipronged operational overhaul. For an integrated health system, such an effort would require a centralized office with the ability and authority to coordinate between multiple sites and specialties, among physicians with various employment relationships, and across payer contracts with potentially conflicting incentives.

Finally, an overarching limitation to these sorts of studies is that context matters: Providers’ technology, the organizational structure of providers included in a payment contract, and organizational culture all affect the solution space for cost management and the quality of execution for cost management interventions. For example, many initiatives relied on electronic medical records to deliver real-time feedback to physicians. The provider studied by Javitt et al had no such tool, and so the intervention in that study used asynchronous feedback,²¹ making the study potentially irrelevant to current providers.

Organizational structure may also play a role. For example, a stand-alone primary care practice in a global budget model has the unadulterated incentive to reduce hospital admissions, whereas an integrated delivery system faces a countervailing incentive to maintain hospital admissions, even for patients covered by the contract. These trade-offs also exist for individual physicians—for example, an independent surgeon practicing at a hospital. On the other hand, integrated delivery systems with financial resources and technical know-how may be the most capable of implementing practice redesign initiatives to facilitate delivery of high-value care; those that employ or purchase physician practices may have the best opportunity to reconcile countervailing incentives at the physician level.⁴⁶ Strategic commitment to cost management at the executive level is critical, especially for loosely federated health systems lacking formal management hierarchies; explicit executive enthusiasm is a clear signal to decision makers throughout an organization to rally around an initiative.

Limitations

Our study has several limitations. First, although we conducted a broad search of the published literature, including the references of papers we reviewed, we may have missed some relevant studies, especially those published in the gray literature or used in proprietary commercial products. As noted earlier, many more interventions have been attempted than are included here, but most were not studied, and some that were studied were not published. Finally, given the small number of studies with evaluations and the high heterogeneity of interventions and provider contexts, it was not feasible to rigorously compare the efficacy of the interventions we identified.

CONCLUSIONS

In this systematic review of the academic literature on approaches that clinicians and their organizations can adopt to control total medical expenditures, we found scant evidence that could be used to inform organizations seeking successful strategies to control total medical spending. Although it is clear from our results that providers use diverse tactics to achieve savings, they rarely publish quantitative evaluations of these interventions in peer-reviewed journals and even more rarely employ high-quality randomized designs. To build a rigorous evidence base on cost-saving interventions, much more research is needed.

Author Affiliations: Doctoral Programs, Harvard Business School (GGW), Boston, MA; Department of Health Care Policy (GGW, BEL), Harvard Medical School (JPS), Boston, MA; Division of Pulmonary, Critical Care, and Sleep Medicine (JPS) and Division of Primary Care and General Internal Medicine (BEL), Department of Medicine, and Center for Healthcare Delivery Science (JPS, BEL), Beth Israel Deaconess Medical Center, Boston, MA.

Source of Funding: None.

Author Disclosures: The 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 (GGW, JPS, BEL); acquisition of data (GGW, BEL); analysis and interpretation of data (GGW, JPS, BEL); drafting of the manuscript (GGW); critical revision of the manuscript for important intellectual content (JPS, BEL); provision of study materials or patients (JPS); administrative, technical, or logistic support (JPS); and supervision (JPS, BEL).

Send Correspondence to: Gabe G. Weinreb, BA, Doctoral Programs, Harvard Business School, Wyss House, Soldiers Field, Boston, MA 02163. Email: weinreb@hcp.med.harvard.edu.

REFERENCES

1. Lewis C, Horstman C, Blumenthal D, Abrams MK. Value-based care: what it is, and why it’s needed. The Commonwealth Fund. February 7, 2023. Accessed December 15, 2023. https://www.commonwealthfund.org/publications/explainer/2023/feb/value-based-care-what-it-is-why-its-needed
2. Abou-Atme Z, Alterman R, Khanna G, Levine E. Investing in the new era of value-based care. McKinsey. December 16, 2022. Accessed December 15, 2023. https://www.mckinsey.com/industries/healthcare/our-insights/investing-in-the-new-era-of-value-based-care
3. Innovation Center Strategy Refresh. CMS; 2021. Accessed December 15, 2023. https://www.cms.gov/priorities/innovation/strategic-direction-whitepaper
4. APM Measurement: 2023 Methodology and Results Report. Health Care Payment Learning & Action Network; 2023. Accessed December 15, 2023. https://web.archive.org/web/20231213223034/https://hcp-lan.org/workproducts/apm-methodology-2023.pdf
5. APM Measurement: 2018 Methodology and Results Report. HCP-LAN; 2018. Accessed January 4, 2024. https://web.archive.org/web/20241103215523/https://hcp-lan.org/workproducts/apm-methodology-2018.pdf
6. McWilliams JM, Landon BE, Chernew ME. Changes in health care spending and quality for Medicare beneficiaries associated with a commercial ACO contract. JAMA. 2013;310(8):829-836. doi:10.1001/jama.2013.276302
7. Joynt Maddox KE, Orav EJ, Zheng J, Epstein AM. Evaluation of Medicare’s bundled payments initiative for medical conditions. N Engl J Med. 2018;379(3):260-269. doi:10.1056/NEJMsa1801569
8. McWilliams JM, Hatfield LA, Chernew ME, Landon BE, Schwartz AL. Early performance of accountable care organizations in Medicare. 2016;374(24):2357-2366. doi:10.1056/NEJMsa1600142
9. Milad MA, Murray RC, Navathe AS, Ryan AM. Value-based payment models in the commercial insurance sector: a systematic review. Health Aff (Millwood). 2022;41(4):540-548. doi:10.1377/hlthaff.2021.01020
10. Peikes D, Chen A, Schore J, Brown R. Effects of care coordination on hospitalization, quality of care, and health care expenditures among Medicare beneficiaries: 15 randomized trials. JAMA. 2009;301(6):603-618. doi:10.1001/jama.2009.126
11. McWilliams JM. Cost containment and the tale of care coordination. N Engl J Med. 2016;375(23):2218-2220. doi:10.1056/NEJMp1610821
12. Scanlon DP, Harvey JB, Wolf LJ, et al. Are health systems redesigning how health care is delivered? Health Serv Res. 2020;55(suppl 3):1129-1143. doi:10.1111/1475-6773.13585
13. Bundled Payments for Care Improvement (BPCI) Initiative: public toolkit. CMS. Updated February 2019. Accessed June 6, 2025. https://www.cms.gov/priorities/innovation/files/x/bpci-toolkit.pdf
14. Transforming Clinical Practice Initiative. CMS. Accessed June 17, 2025. https://www.cms.gov/priorities/innovation/innovation-models/transforming-clinical-practices
15. Covidence. Accessed December 15, 2023. https://www.covidence.org/
16. What is GRADE? BMJ Best Practice. Accessed December 15, 2023. https://dev-bestpractice.bmjgroup.com/info/toolkit/learn-ebm/what-is-grade/
17. Hawes EM, Smith JN, Pinelli NR, et al. Accountable Care in Transitions (ACTion): a team-based approach to reducing hospital utilization in a patient-centered medical home. J Pharm Pract. 2018;31(2):175-182. doi:10.1177/0897190017707118
18. Burgon TB, Cox-Chapman J, Czarnecki C, et al. Engaging primary care providers to reduce unwanted clinical variation and support ACO cost and quality goals: a unique provider-payer collaboration. Popul Health Manag. 2019;22(4):321-329. doi:10.1089/pop.2018.0111
19. Schiavoni KH, Lehmann LS, Guan W, Rosenthal M, Sequist TD, Chien AT. How primary care physicians integrate price information into clinical decision-making. J Gen Intern Med. 2017;32(1):81-87. doi:10.1007/s11606-016-3805-0
20. Simon M, Choudhry NK, Frankfort J, et al. Exploring attributes of high-value primary care. Ann Fam Med. 2017;15(6):529-534. doi:10.1370/afm.2153
21. Javitt JC, Rebitzer JB, Reisman L. Information technology and medical missteps: evidence from a randomized trial. J Health Econ. 2008;27(3):585-602. doi:10.1016/j.jhealeco.2007.10.008
22. Tanio C, Chen C. Innovations at Miami practice show promise for treating high-risk Medicare patients. Health Aff (Millwood). 2013;32(6):1078-1082. doi:10.1377/hlthaff.2012.0201
23. Horn DM, Koplan KE, Senese MD, Orav EJ, Sequist TD. The impact of cost displays on primary care physician laboratory test ordering. J Gen Intern Med. 2014;29(5):708-714. doi:10.1007/s11606-013-2672-1
24. Rosenthal MB, Frank RG, Buchanan JL, Epstein AM. Transmission of financial incentives to physicians by intermediary organizations in California. Health Aff (Millwood). 2002;21(4):197-205. doi:10.1377/hlthaff.21.4.197
25. Shah SJ, Schwamm LH, Cohen AB, et al. Virtual visits partially replaced in-person visits in an ACO-based medical specialty practice. Health Aff (Millwood). 2018;37(12):2045-2051. doi:10.1377/hlthaff.2018.05105
26. Kazberouk A, McGuire K, Landon BE. A survey of innovative reimbursement models in spine care. Spine (Phila Pa 1976). 2016;41(4):344-352. doi:10.1097/BRS.0000000000001212
27. Arterburn D, Wellman R, Westbrook E, et al. Introducing decision aids at Group Health was linked to sharply lower hip and knee surgery rates and costs. Health Aff (Millwood). 2012;31(9):2094-2104. doi:10.1377/hlthaff.2011.0686
28. Kline RM, Blau S, Buescher NR, et al. Secret sauce—how diverse practices succeed in Centers for Medicare & Medicaid Services Oncology Care Model. JCO Oncol Pract. 2021;17(12):734-743. doi:10.1200/OP.21.00165
29. Iorio R, Bosco J, Slover J, Sayeed Y, Zuckerman JD. Single institution early experience with the Bundled Payments for Care Improvement Initiative. J Bone Joint Surg Am. 2017;99(1):e2. doi:10.2106/JBJS.16.00066
30. Froemke CC, Wang L, DeHart ML, Williamson RK, Ko LM, Duwelius PJ. Standardizing care and improving quality under a bundled payment initiative for total joint arthroplasty. J Arthroplasty. 2015;30(10):1676-1682. doi:10.1016/j.arth.2015.04.028
31. Agnew JD, Stebbins MR, Hickman DE, Lipton HL. Financial risk relationships and adoption of management strategies in physician groups for self-administered injectable drugs. J Manag Care Pharm. 2003;9(6):523-533. doi:10.18553/jmcp.2003.9.6.523
32. D’Aunno T, Broffman L, Sparer M, Kumar SR. Factors that distinguish high-performing accountable care organizations in the Medicare Shared Savings Program. Health Serv Res. 2018;53(1):120-137. doi:10.1111/1475-6773.12642
33. Harris JM, Elizondo I, Brown AM. Orchestrating ACO success: how top performers achieve shared savings. Healthc Financ Manage. 2016;70(3):42-50.
34. Phipps-Taylor M, Shortell SM. More than money: motivating physician behavior change in accountable care organizations. Milbank Q. 2016;94(4):832-861. doi:10.1111/1468-0009.12230
35. Ip IK, Schneider L, Seltzer S, et al. Impact of provider-led, technology-enabled radiology management program on imaging. Am J Med. 2013;126(8):687-692. doi:10.1016/j.amjmed.2012.11.034
36. Hilligoss B, Song PH, McAlearney AS. Aligning for accountable care: strategic practices for change in accountable care organizations. Health Care Manage Rev. 2017;42(3):192-202. doi:10.1097/HMR.0000000000000110
37. Rosenthal M, Shortell S, Shah ND, et al. Physician practices in accountable care organizations are more likely to collect and use physician performance information, yet base only a small proportion of compensation on performance data. Health Serv Res. 2019;54(6):1214-1222. doi:10.1111/1475-6773.13238
38. Sistrom CL, Dang PA, Weilburg JB, Dreyer KJ, Rosenthal DI, Thrall JH. Effect of computerized order entry with integrated decision support on the growth of outpatient procedure volumes: seven-year time series analysis. Radiology. 2009;251(1):147-155. doi:10.1148/radiol.2511081174
39. Van Citters AD, Larson BK, Carluzzo KL, et al. Four health care organizations’ efforts to improve patient care and reduce costs. The Commonwealth Fund. January 13, 2012. Accessed December 14, 2023. https://www.commonwealthfund.org/publications/case-study/2012/jan/four-health-care-organizations-efforts-improve-patient-care-and
40. Chien AT, Lehmann LS, Hatfield LA, et al. A randomized trial of displaying paid price information on imaging study and procedure ordering rates. J Gen Intern Med. 2017;32(4):434-448. doi:10.1007/s11606-016-3917-6
41. Javitt JC, Steinberg G, Locke T, et al. Using a claims data-based sentinel system to improve compliance with clinical guidelines: results of a randomized prospective study. Am J Manag Care. 2005;11(2):93-102.
42. Tanabe P, Blewer AL, Bonnabeau E, et al. Dissemination of evidence-based recommendations for sickle cell disease to primary care and emergency department providers in North Carolina: a cost benefit analysis. J Health Econ Outcomes Res. 2021;8(1):18-28. doi:10.36469/jheor.2021.21535
43. Broffman L, Brown K, Bayley BK, Savitz L, Rissi J. Funding accountable care in Oregon: financial models in two coordinated care organizations. J Healthc Manag. 2016;61(4):291-302.
44. Landon BE, Weinreb GG, Bitton A. Making sense of new approaches to primary care delivery: a typology of innovations in primary care. NEJM Catalyst. May 9, 2022. Accessed January 3, 2024. https://catalyst.nejm.org/doi/full/10.1056/CAT.22.0032
45. Damberg CL, Silverman M, Burgette L, Vaiana ME, Ridgely MS. Are value-based incentives driving behavior change to improve value? Am J Manag Care. 2019;25(2):e26-e32.
46. Harvey JB, Vanderbrink J, Mahmud Y, et al. Understanding how health systems facilitate primary care redesign. Health Serv Res. 2020;55(suppl 3):1144-1154. doi:10.1111/1475-6773.13576