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Delivery System Performance as Financial Risk Varies
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Delivery System Performance as Financial Risk Varies

Joseph P. Newhouse, PhD; Mary Price, MA; John Hsu, MD, MBA; Bruce Landon, MD, MBA; and J. Michael McWilliams, MD, PhD
One delivery system’s healthcare utilization in its Medicare Advantage product was notably less than in its Pioneer accountable care organization or in a traditional Medicare comparison group.

Within the Medicare program, we expected the MA group to exhibit the lowest spending over the years we observed because Banner faced financial risk throughout the period, whereas the ACO did not begin until 2012. We also expected the ACO group to exhibit slower growth in use and cost than the TM comparison group after the ACO was established. In fact, the MA group did have the lowest spending of the 3 groups, driven by the lowest use of hospital and postacute services.

Also as expected, hospitalization rates in the Pioneer ACO group declined more rapidly than in the TM comparison group (with similar pre-ACO period trends for the 2 groups). Comparison of SNF rates was difficult because pre-ACO period trends differed. Counter to expectation, ACO spending rose relative to the comparison group in the first year of the ACO but then fell faster in the next 2 years. The subsequent entry of Medicare Shared Savings Program plans in the local market may have biased our comparison against the ACO.

Zero-users could not be attributed in the ACO and TM comparison groups, which complicates comparison with the MA results because there were 5 to 7 percentage points fewer zero-users in the MA plan (10% zero-users in the TM groups vs 3%-5% in the MA group, depending on year). If we were to arbitrarily distribute the TM zero-use group between the ACO and the TM comparison groups in the same ratio as the positive-user group to derive per-person rather than per–positive-user rates, the differences between the MA group and the other 2 groups in utilization and spending would be about 5 to 7 percentage points smaller than shown previously. Nonetheless, MA spending rates would remain below those of the other 2 groups, especially in the pre-ACO period.

MA plans as a group are known to code diagnoses more intensively than coding in TM claims,11,12 raising the possibility that the MA plan was observed to spend less because, conditional on age, sex, and diagnosis, the average individual was coded as healthier in MA. In unadjusted data, however, the pre-ACO period difference between MA spending and that of the other 2 groups was even larger than in the adjusted data (eAppendix Figure 5), so the large pre-ACO period difference in the risk-adjusted data is not an artifact of risk adjustment or of more intensive coding in MA. As a sensitivity test, we examined whether the MA results were sensitive to using the list of 2014 MA rather than 2012 MA providers and also providers in each calendar year, and the results were not sensitive.

Overall comparisons between the commercial ACO and TM comparison group showed little effect of the ACO. This may well be due to the greater degree of churn among the commercial ACO patients than the Medicare patients. Whether this degree of churn is found in other commercial ACO contracts is unknown.


This study is limited to outcomes at a single hospital-based delivery system, and one must therefore be cautious about generalizing its findings to other settings, especially to non–hospital-based ACOs. Nonetheless, its finding of better performance at the Pioneer ACO than at the TM comparison group, with respect to hospitalization, is consistent with the literature cited in the introduction.2,5


Relative to the literature, what is novel in these results is that adjusted hospitalization, SNF, and spending rates in Banner’s MA plan were notably below those of its Medicare ACO, although there was partial convergence over the 3-year period of observation. Although the commercial results were more ambiguous, possibly because of greater churn, our results overall support CMS’ efforts to transition Medicare reimbursement away from traditional fee-for-service. 


The authors are grateful to The Commonwealth Fund for supporting this research under grant #20150905 and to David Blumenthal, MD, MPP, and Melinda Abrams of the Fund for comments on a preliminary draft. They thank Chuck Lehn of Banner Health, Robert Groves, MD, of Banner Aetna, Dave Firdaus of Blue Cross Blue Shield of Arizona, and Brigitte Nettesheim of Aetna for assistance.

Author Affiliations: Department of Health Care Policy, Harvard Medical School (JPN, BL, JMM), Boston, MA; Department of Health Policy and Management, Harvard T.H. Chan School of Public Health (JPN), Boston, MA; Harvard Kennedy School (JPN), Cambridge, MA; National Bureau of Economic Research (JPN), Cambridge, MA; Mongan Institute for Health Policy, Massachusetts General Hospital (MP, JH), Boston, MA.

Source of Funding: The Commonwealth Fund grant #20150905.

Author Disclosures: Dr Newhouse was a board member of Aetna through May 2018 and held Aetna stock through November 2018. Dr Hsu consults for Cambridge Health Alliance, University of Southern California, Community Servings, and Delta Health Alliance; works at Massachusetts General Hospital, which provides patient care; and receives grants from the National Institutes of Health and the Agency for Healthcare Research and Quality. Dr McWilliams reports serving as a consultant to Abt Associates Inc on an evaluation of the ACO Investment Model. 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 (JPN, JH, BL, JMM); acquisition of data (JPN); analysis and interpretation of data (JPN, MP, JH, BL, JMM); drafting of the manuscript (JPN, MP); critical revision of the manuscript for important intellectual content (JPN, JH, BL, JMM); statistical analysis (JPN, JMM); obtaining funding (JPN); administrative, technical, or logistic support (JPN, MP); and supervision (JPN).

Address Correspondence to: Joseph P. Newhouse, PhD, Harvard University, 180 Longwood Ave, Boston, MA 02115. Email:

1. Nyweide DJ, Lee W, Cuerdon TT, et al. Association of Pioneer accountable care organizations vs traditional Medicare fee for service with spending, utilization, and patient experience. JAMA. 2015;313(21):2152-2161. doi: 10.1001/jama.2015.4930.

2. McWilliams JM, Chernew ME, Landon BE, Schwartz AL. Performance differences in year 1 of Pioneer accountable care organizations. N Engl J Med. 2015;372(20):1927-1936. doi: 10.1056/NEJMsa1414929.

3. McWilliams JM, Hatfield LA, Chernew ME, Landon BE, Schwartz AL. Early performance of accountable care organizations in Medicare. N Engl J Med. 2016;374(24):2357-2366. doi: 10.1056/NEJMsa1600142.

4. McWilliams JM, Hatfield LA, Landon BE, Hamed P, Chernew ME. Medicare spending after 3 years of the Medicare Shared Savings Program. N Engl J Med. 2018;379(12):1139-1149. doi: 10.1056/NEJMsa1803388.

5. Song Z, Rose S, Safran DG, Landon BE, Day MP, Chernew ME. Changes in health care spending and quality 4 years into global payment. N Engl J Med. 2014;371(18):1704-1714. doi: 10.1056/NEJMsa1404026.

6. L&M Policy Research LLC. Evaluation of CMMI accountable care organization initiatives: Pioneer ACO evaluation findings from performance years one and two. CMS website. Published March 10, 2015. Accessed January 29, 2019.

7. Chen JL, Hicks AL, Chernew ME. Prices for physician services in Medicare Advantage versus traditional Medicare. Am J Manag Care. 2018;24(7):341-344.

8. McWilliams JM, Chernew ME, Dalton JB, Landon BE. Outpatient care patterns and organizational accountability in Medicare. JAMA Intern Med. 2014;174(6):938-945. doi: 10.1001/jamainternmed.2014.1073.

9. Daw JR, Hatfield LA. Matching and regression to the mean in difference-in-differences analysis. Health Serv Res. 2018;53(6):4138-4156. doi: 10.1111/1475-6773.12993.

10. Kautter J, Pope GC, Ingber M, et al. The HHS-HCC risk adjustment model for individual and small group markets under the Affordable Care Act. Medicare Medicaid Res Rev. 2014;4(3). doi: 10.5600/mmrr2014-004-03-a03.

11. Kronick R. Projected coding intensity in Medicare Advantage could increase Medicare spending by $200 billion over ten years. Health Aff (Millwood). 2017;36(2):320-327. doi: 10.1377/hlthaff.2016.0768.

12. Kronick R, Welch WP. Measuring coding intensity in the Medicare Advantage program. Medicare Medicaid Res Rev. 2014;4(2). doi: 10.5600/mmrr2014-004-02-a06.
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