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Forgotten Patients: ACO Attribution Omits Those With Low Service Use and the Dying

The American Journal of Managed CareJuly 2018
Volume 24
Issue 7

This article compares clinical and utilization profiles of Medicare patients who are attributed to provider groups with those of patients unattributed to any provider group in accountable care organization models.


Objectives: Alternative payment models, such as accountable care organizations, hold provider groups accountable for an assigned patient population, but little is known about unassigned patients. We compared clinical and utilization profiles of patients attributable to a provider group with those of patients not attributable to any provider group.

Study Design: Cross-sectional study of 2012 Medicare fee-for-service beneficiaries 21 years and older.

Methods: We applied the Medicare Shared Savings Program attribution approach to assign beneficiaries to 2 mutually exclusive categories: attributable or unattributable. We compared attributable and unattributable beneficiaries according to demographics, dual eligibility for Medicaid, nursing home residency, clinical comorbidities, annual service utilization, annual spending, and 1- and 2-year mortality. We estimated multivariate regression models describing correlates of attribution status.

Results: Most beneficiaries (88%) were attributable to a provider group. The remaining 12% were unattributable. Beneficiaries unattributable to any provider group were more likely to be younger, male, and from a minority group; to have disability as the basis for enrollment; and to live in high-poverty areas. Unattributable beneficiaries included 3 distinct subgroups: nonusers of care, decedents, and those with healthcare service use but no qualifying evaluation and management visits. Many unattributable Medicare beneficiaries had minimal use of healthcare services, with the exception of a small subgroup of beneficiaries who died within the attribution year.

Conclusions: Attribution approaches that more fully capture unattributable patients with low service use and patients near the end of life should be considered to reward population health efforts and improve end-of-life care.

Am J Manag Care. 2018;24(7):e207-e215Takeaway Points

Given the reliance of alternative payment models on assignment of patients to provider groups, we compared clinical and utilization profiles of patients attributable to a provider group with those of patients unattributable to any provider group.

  • We estimated multivariate regression models describing correlates of attribution status.
  • Most beneficiaries (88%) were attributable to a provider group, whereas 12% remained unattributable.
  • Beneficiaries unattributable to any provider group included 3 distinct subgroups: nonusers of care, decedents, and those with healthcare service use but no qualifying evaluation and management visits.
  • Attribution approaches that more fully capture unattributable patients with low service use and patients near the end of life should be considered to reward population health efforts and to improve end-of-life care.

By holding networks of healthcare providers responsible for the total cost and quality of care for a designated population, the accountable care organization (ACO) model creates incentives to coordinate care across providers, reduce unnecessary spending, and improve the quality of care.1 The ACO model has grown steadily,2 and by January 2017, Medicare held ACO contracts with 525 organizations serving more than 10 million beneficiaries.3 Medicare ACO contract participants that meet quality benchmarks are eligible to share the savings that they generate.4,5 An ACO’s performance is evaluated based on Medicare methods of measurement and attribution. Researchers have evaluated outcomes of ACOs among attributable beneficiaries,6,7 but no study has described unattributable beneficiaries.

In Medicare ACO contracts, beneficiaries are attributed to organizations based on their use of primary care services from eligible providers,8,9 so attribution can influence an organization’s performance under an ACO contract. Among organizations serving patients with complex clinical and psychosocial needs, for example, quality metrics may be hard to achieve, giving organizations incentives to avoid accepting such patients. Conversely, ACO participants that are effectively managing population health may not achieve shared savings for patients who, appropriately, do not use the primary care services that would make them eligible for attribution. ACOs have little financial incentive to deliver preventive care that might decrease the chance that healthy patients are attributed to an ACO.10 As advanced payment models mature to include downside risk, and as models like Comprehensive Primary Care expand, it is important to understand which patients are left out so that policy makers can develop regulations that encourage participation and improved care in new payment models.

To date, we have no information on the composition of patients who are not attributable to any provider group under Medicare Shared Savings Program (MSSP) regulations. This paper examines beneficiary characteristics associated with attribution and compares hospitalization, mortality, and spending across attributable and unattributable beneficiaries. Results from our analyses can guide policy on whether additional actions are necessary to adequately give provider participants incentives to improve population health and to ensure that vulnerable beneficiaries—who may benefit the most from improved care coordination—are not excluded from new payment models.


Using Medicare claims and CMS attribution rules for the most widely adopted model, the MSSP,11 we categorized beneficiaries into 2 mutually exclusive groups: patients who were attributable to provider groups and patients who were not attributable to any provider group. We included only beneficiaries with full parts A and B coverage, limiting the sample to those for whom traditional fee-for-service (FFS) Medicare was the primary payer. We combined all beneficiaries attributable to provider organizations (ie, MSSP, Pioneer ACO, and non-ACO medical groups) into a single category because the purpose of our research was to investigate the characteristics of those falling through the cracks of the current attribution methodology. In 2012, Medicare ACO contracts included 32 Pioneer program participants and 114 MSSP participants (over the first performance period from April 2012 or July 2012 through December 2013) that were responsible for more than 2 million beneficiaries.12 Although Pioneer program participants, which were responsible for nearly 700,000 beneficiaries in 2012,13 faced downside risk for spending, virtually all MSSP participants (n = 110) were in Track 1 and eligible for upside savings only.12 We conducted cross-sectional analyses examining characteristics of beneficiaries according to their attribution status.

Beneficiary Attribution to Provider Organizations

Following MSSP’s 2-step attribution process, a beneficiary who has at least 1 face-to-face outpatient evaluation and management (E&M) visit is assigned to the provider group that has the highest allowed charges by primary care clinicians (ie, general practice, family practice, internal medicine, and geriatric medicine practitioners) for those visits.11,14-16 Patients not seeing primary care clinicians are attributed based on visits to qualifying non—primary care clinicians (ie, physicians in other specialties, nurse practitioners, physician assistants, and clinical nurse specialists).11 Beneficiaries who received care only from nonqualifying clinical providers (eg, interventional cardiologists or certified registered nurses) or in nonstandard settings (eg, emergency departments [EDs]) or who had no visits at all were not able to be assigned to any provider group using current MSSP attribution rules; they formed our “unattributable” group.11


For beneficiaries in parts A and B who were 21 years or older, data were drawn from a 40% random sample of 2012 Medicare FFS claims and 2012-2013 beneficiary summary files. Beneficiary summary files provided patient demographics, enrollment status, and date of death. Attribution status, diagnoses, utilization, and spending were obtained using claims. We chose 2012 because that was the most recent year of claims available without the suppression of claims indicating substance use disorder in research files17 and within the period of Medicare ACO implementation.

Long-term residence in nursing homes was determined from the 2012 Minimum Data Set (MDS). Patients’ zip codes were used to identify hospital referral regions (HRRs) of residence; HRRs represent regional healthcare markets in the United States defined by the Dartmouth Atlas. We further used beneficiaries’ zip codes to identify rural and urban areas18 and whether they lived in high-poverty Census tracts (≥20% of residents below the federal poverty line).19

Variable Descriptions

The main outcome of interest was whether a beneficiary was attributable to a provider group or not. We estimated correlations between attribution and beneficiary characteristics, including age, sex, race/ethnicity (ie, non-Hispanic white, non-Hispanic black, Hispanic, Asian or Pacific Islander, or other race), residence in a high-poverty Census tract or a rural area, dual eligibility for Medicaid, residence in long-term nursing home (≥100 days in a nursing home according to MDS), disability as a basis of original Medicare entitlement, and date of death.

We used International Classification of Diseases, Ninth Revision codes on claims to identify beneficiaries’ comorbid conditions in 2011 using Hierarchical Condition Categories developed by CMS20 (eAppendix Table 1 [eAppendix available at ajmc.com]). Chronic conditions included dichotomous variables for cancer, cerebrovascular disease and stroke, chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), connective tissue disease, coronary artery disease, dementia, diabetes, hematologic/thrombotic disease, liver disease, mental health conditions (severe mental illness, substance use disorder, and depression), paralysis (not stroke), Parkinson’s/Huntington disease, peripheral vascular disease, and renal disease. We identified these conditions in 2011 to account for beneficiary diagnosis history and for their chronic nature and association with mortality and costs. We categorized patients with 0 observed chronic conditions, with multiple observed chronic conditions, and with unknown diagnoses in 2011 due to lack of medical claims. We reported utilization (ambulatory care—sensitive condition admissions, admissions to acute care or critical access hospitals, E&M visits, ED visits not leading to admission) and 2012 spending (Medicare payments to all providers, total and by category: inpatient, physician services, home health, and hospice).

To distinguish those who were unattributable because attribution rules may exclude end-of-life care, we examined mortality in 2012 and 2013 (among those alive January 1, 2013). This approach disentangles the mechanical relationship between attribution and death from clinical complexity.

Data Analysis

We compared characteristics of attributable and unattributable Medicare enrollees—based on a 40% random sample to minimize computing time—using multivariate logistic models. We modeled attribution status as a function of demographics, dual eligibility for Medicaid, nursing home residency, disability, measures of chronic condition history, and HRR fixed effects.

We considered the attribution status of those with dual eligibility for Medicaid, who totaled more than 10 million people as of 2016, due to their low socioeconomic status, complex clinical needs, and elevated medical spending.21-26


This study included about 13,000,000 FFS Medicare beneficiaries in 2012. Most of these FFS Medicare beneficiaries (88%) were able to be attributed to a provider group, leaving 12% unattributable (Figure 1).

Compared with attributable beneficiaries, unattributable beneficiaries tended to be younger (65 vs 71 years), male (60% vs 43%), from a minority race or ethnic group (28% vs 18%), living in a high-poverty area (25% vs 22%; Table 1 [part A and part B]), and less likely to be dually eligible for Medicaid but no less likely to reside in a rural area (eAppendix Table 2). Unattributable beneficiaries had fewer observed chronic conditions and were less likely to have 2 or more chronic conditions in 2011 (5.3% vs 22.3%; Table 1) compared with attributable beneficiaries; however, people with no service use or very few visits have no or fewer opportunities for diagnoses to be observed. Mortality was higher in the unattributable group in 2012 (6.6% vs 4.3%; Table 1) but lower in 2013 (2.8% vs 4.9%). More beneficiaries younger than 65 years were unattributable than those older than 65 years (20% vs 10%; Figure 1). We do not report P values of differences in variables’ means and proportions across attribution groups, which were all statistically significant (P <.001) due to the large sample size.

In terms of geographic distribution, the proportion of unattributable beneficiaries ranged from 6% to 22% across HRRs (Figure 2); HRRs with the fewest unattributable beneficiaries (less than 10%) were located primarily in parts of the Midwest, South, and Northeast regions.27 Although results are not shown, we examined 2011 physician supply in the market—a proxy for access&mdash;and found no evidence for differences across attribution categories.

In the unattributable group, we identified a subpopulation with no healthcare service use or spending in 2012 (63% of the unattributable) and a group that died in 2012 (6%). The remaining beneficiaries (31%) used healthcare services, including primary care services, that did not qualify for attribution (Table 2 [part A and part B]). Among the unattributable nonusers of care in 2012, few (3.6%) had 1 or more reported diagnoses in 2011. However, spending averaged $9000 in 2011 in this group, primarily from ED visits and hospitalization (not shown).

Unattributable patients with healthcare service use who died (user-decedents) spent $19,000, on average (including $12,000 of inpatient spending and $5000 of hospice spending; Table 2). More than half (60%) of the unattributable decedents died within the first 3 months of 2012, compared with 20% of decedents attributable to provider organizations (eAppendix Table 3). Of the more than 80,000 unattributable user-decedents, 67% had 1 or more chronic condition diagnoses in claims in 2011; dementia, CHF, renal disease, diabetes, and COPD were common.

The third group of unattributable beneficiaries (with healthcare use but no qualifying primary care visits) averaged more than $3000 in annual spending, which was one-third of the spending of attributable patients. Very few of these beneficiaries had nonqualifying E&M visits (n = 57), whereas others had other healthcare use in nonattribution settings. Of the unattributable nondecedent healthcare users, 31% had reported clinical conditions in the prior year, with severe mental illness, diabetes, renal diseases, dementia, and COPD being most common.

In multivariate regression analyses, older and female beneficiaries were less likely to be unattributable (0.1 percentage-point decrease per year of age and 4.5 percentage points less likely among women; P <.001) (Table 3). Minorities and those living in high-poverty Census tracts were more likely to be unattributable. Nursing home residents and dual-eligible beneficiaries were less likely than others to be unattributable to provider groups (4.3 and 4.2 percentage points lower probability, respectively; P <.001). Disability as original reason for entitlement was associated with a 4.5 percentage-point increase in the likelihood of being unattributable. Beneficiaries with 0 observed chronic condition diagnoses and those with unknown chronic condition history (nonusers of care and those not in both parts A and B FFS Medicare in 2011) in 2011 were more likely to be unattributable in 2012 relative to those with observed chronic conditions (2.4 and 20.4-22.3 percentage points higher probability, respectively; P <.001).

Because Medicaid coverage complements Medicare benefits for certain low-income adults, we explored how attribution status differed in the 22% of our sample who were dually eligible for Medicaid benefits. Although they were less likely than other beneficiaries to be unattributable (Figure 1), results from multivariate analyses of dual-eligible enrollees were consistent with those found in the overall sample (eAppendix Table 4), with 2 exceptions: Asian/Pacific Islander ethnicity and disability status were both associated with a decrease in the likelihood of being unattributable.


Because existing evidence on ACOs focuses exclusively on attributable Medicare beneficiaries,8,15,28,29 we studied the excluded group of unattributable patients to understand how these omitted groups might influence performance in Medicare ACO programs.30 Using MSSP attribution methods, nearly 12% of beneficiaries were unattributable in 2012. This group was heterogeneous, composed of decedents, beneficiaries using no services, and those using services ineligible for attribution under common methods. The majority of unattributable beneficiaries had no encounter with the healthcare system at all, which may be due to financial and nonfinancial barriers that limit access to care.31,32 A small but costly group, 6% of unattributable beneficiaries, died in 2012; among them, inpatient and hospice service use was common. The remaining 31% of unattributable beneficiaries had minimal healthcare use, largely for urgent or hospital-based services.

The distribution of unattributable beneficiaries aligned with regional spending patterns in Medicare.33 Beneficiaries living in low-cost areas, such as Minnesota, had higher proportions of unattributable beneficiaries. This pattern indicates that alternative payment models in these regions, ironically, may not reward organizations for their activities targeting healthier groups who use few services. For example, ACOs in low-cost regions were evaluated against lower financial benchmarks than were ACOs in high-cost regions. Beginning in 2017, cost benchmark calculations were improved to integrate regional factors, making an ACO’s benchmark less reflective of its historical spending and more dependent on FFS expenditures in its region.34 Two changes to attribution might capture more low-utilization beneficiaries. First, Track 3 of the MSSP, with 16 participating organizations in 2016, and the Next Generation ACO model—another demonstration project in the CMS Innovation Center&mdash;are currently piloting attestation, allowing beneficiaries to voluntarily choose an ACO,35-38 which would allow for accountability for preventive services in healthier populations with little or no observed utilization. Second, “sticky” attribution, or allowing utilization in a prior year to count in the current year for those with no use in the current year, could capture more healthy patients; it could also capture more individuals who are very sick and use non—primary care settings due to death early in the year. Although attestation has the benefit of permitting patients to choose, sticky attribution may be more feasible in existing programs because it can be done using just claims.

Facilitating the attribution of healthy patients and those with minimal use through any method may improve the consistency and continuity of care for patients. Because ACOs are responsible for both cost and quality of care for assigned patients, attribution of healthy patients with no healthcare use, as well as of those with some healthcare use but nonqualifying visits for current attribution purposes, would reward providers for care delivered to these patients, without rewarding unnecessary care.

Attribution of nonusers may more appropriately contribute to an ACO’s shared savings, which are conditional on meeting predetermined quality measure benchmarks, and these benchmarks reflect beneficiaries classified as at-risk populations—for example, in benchmark year(s)&mdash;who currently do not use services.39,40 Moreover, financial benchmarks are calculated using attributed beneficiaries’ historical healthcare use. The current attribution methodology provides incentives for ACOs to bring in healthy patients for primary care visits, but attributing nonusers through alternative methods may be warranted to appropriately reward providers without inducing potentially unnecessary primary care visits.41 A practical solution that would address those nonusers of care who used healthcare in previous years would be to assign them based on sticky attribution.

Furthermore, healthcare providers and policy makers should give greater attention to the consequential subpopulation of decedents (ie, end-of-life patients) currently falling outside of the responsibility of organizations participating in ACOs. One-fourth of Medicare spending is devoted to care in the last year of life,42 so relative savings on end-of-life care contribute outsized savings to the Medicare program. There are also opportunities for improvements in care quality and preference-based care. Organizations that deliver thoughtful end-of-life care, by using providers or settings not recognized by current attribution methods, are not rewarded for this high-quality care. On the flip side, organizations that fail to coordinate end-of-life care or that deliver resource-intensive care not aligned with patient preferences face no penalty. Additionally, higher hospice spending registered in unattributable decedents signals that involvement of hospice and palliative care in new payment initiatives may improve quality of care and potentially reduce costs43-45 for patients at the end of life.

Alternative attribution methods have different strengths and weaknesses. Retrospective attribution, currently used for most MSSPs and done at the end of the performance year, reflects actual care delivery. It removes patients who no longer receive care from an ACO and captures new patients, such as those aging into Medicare during the year (eAppendix Table 5).6 However, with retrospective attribution, organizations cannot plan care for patients assigned to them only after care delivery. In contrast, prospective attribution removes this uncertainty, which might facilitate active engagement with patients and can improve management of patient population health. However, prospective attribution is a forecast based on past healthcare use, and it works best when care-seeking patterns are stable and therefore predictable over time. This attribution, although preferred by ACOs,46 may have important cost implications for ACOs if patient care patterns drastically change from one year to another because patients may see any Medicare provider. This type of attribution would also fail to capture patients who initiated care during the year (eAppendix Table 5).

Most of the gaps in attribution rules that we identified may be addressed with a hybrid attribution method. First, for most populations, the current assignment based on E&M visits is appropriate. Second, for those with limited healthcare use and for the healthy, assignment would be based on attestation over a longer time period, therefore “rewarding” providers for early prevention and early detection care patterns. For those with serious illnesses, assignment could be broadened to include hospice and palliative care to encourage better end-of-life care (eAppendix Table 5). Before patients with serious illness reach the end of life, however, a first priority is to engage them in care. Providers could tailor care management and coordination to the needs of patients with chronic conditions who have accessed very little healthcare47 in order to improve quality and health outcomes.

Gender disparities were noted between attribution categories, with disproportionately larger proportions of younger and male populations among the unattributable beneficiaries. This may signal gender differences in healthcare-seeking behavior.48 The slightly higher proportion of those with dual eligibility for Medicaid attributable to a provider group (90%) may reflect greater coverage for out-of-pocket spending in this group, lowering a barrier to access. New initiatives, such as the CMS Financial Alignment Initiative, which integrates care across primary, acute, behavioral health, and long-term care for dual-eligible beneficiaries, offer another avenue to engage these patients.


This cross-sectional exploratory study in the first year of the ACO payment model has limitations. First, methods other than the MSSP, which was used in this study, may result in slightly different distributions of beneficiaries across categories. However, MSSP is the largest Medicare ACO program. Second, a longitudinal study with additional years of data could examine trends in attribution of Medicare beneficiaries as provider groups gain knowledge and develop better strategies to manage attribution. Third, claims-based measures of patient illness cannot capture the illness burden for those without utilization. Thus, our results may reflect care-seeking behavior, as we cannot attribute or observe clinical comorbidities for those with no or little healthcare utilization and for new enrollees in the preceding year.


The promise of ACO contracts to lower spending and enhance quality of care, and, ultimately, to improve population health, can be enhanced by improving the attribution of individuals to providers. By rewarding organizations for keeping patients healthy and out of resource-intensive care settings and by appropriately capturing and rewarding compassionate care, such as hospice services, for patients approaching the end of life, Medicare’s continued development of advanced payment models has the potential to move us closer to the goal of improving population health.


The authors thank Kristen Bronner for her help in map designing.Author Affiliations: The Dartmouth Institute for Health Policy & Clinical Practice, Geisel School of Medicine at Dartmouth (MHO, EM, C-HC, SRR, JPWB, VAL, CHC), Lebanon, NH; The National Bureau of Economic Research (EM), Cambridge, MA; Department of Internal Medicine, University of Michigan Medical School (JPWB), Ann Arbor, MI; Institute for Health Policy and Innovation, University of Michigan (JPWB), Ann Arbor, MI.

Source of Funding: Supported by the National Institute on Aging (R03AG049360, R33AG044251, P01-AG019783, and 1K01AG049914), a grant from the Commonwealth Fund (20160616), and a grant from the Agency for Healthcare Research and Quality (5U19HS024075-02). The content of the article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging, the Commonwealth Fund, or the Agency for Healthcare Research and Quality.

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 (MHO, EM, C-HC, SRR, JPWB, VAL, CHC); acquisition of data (JPWB); analysis and interpretation of data (MHO, C-HC, SRR, JPWB, VAL); drafting of the manuscript (MHO, EM, C-HC, SRR, JPWB, CHC); critical revision of the manuscript for important intellectual content (MHO, EM, C-HC, JPWB, VAL, CHC); statistical analysis (MHO, VAL); administrative, technical, or logistic support (MHO, SRR); and supervision (MHO, EM, JPWB, CHC).

Address Correspondence to: Mariétou H. Ouayogodé, PhD, The Dartmouth Institute for Health Policy & Clinical Practice, Geisel School of Medicine at Dartmouth, Williamson Translational Research Building, Level 5, 1 Medical Center Dr, Lebanon, NH 03756. Email: Marietou.H.L.Ouayogode@Dartmouth.edu.REFERENCES

1. Bard M, Nugent M. Accountable Care Organizations: Your Guide to Strategy, Design, and Implementation. Chicago, IL: Health Administration Press; 2011.

2. Muhlestein D, Saunders R, McClellan M. Growth of ACOs and alternative payment models in 2017. Health Affairs Blog. June 28, 2017. healthaffairs.org/do/10.1377/hblog20170628.060719/full. Accessed August 16, 2017.

3. NAACOS joins CMS in welcoming 525 ACOs in 2017! [news release]. Washington, DC: NAACOS; January 18, 2017. naacos.memberclicks.net/naacos-joins-cms-in-welcoming-525-acos-in-2017. Accessed October 9, 2017.

4. 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.

5. Pyenson BS, Fitch KV, Iwasaki K, Berrios MM. The Two Medicare ACO Programs: Medicare Shared Savings and Pioneer—Risk/Actuarial Differences. New York, NY: Milliman Inc; 2011. milliman.com/uploadedFiles/insight/health-published/pdfs/14849_Milliman-Report-on-Pioneer-vs-MSSP070811.pdf. Accessed May 23, 2017.

6. Lewis VA, McClurg AB, Smith J, Fisher ES, Bynum JP. Attributing patients to accountable care organizations: performance year approach aligns stakeholders’ interests. Health Aff (Millwood). 2013;32(3):587-595. doi: 10.1377/hlthaff.2012.0489.

7. Hsu J, Price M, Spirt J, et al. Patient population loss at a large Pioneer accountable care organization and implications for refining the program. Health Aff (Millwood). 2016;35(3):422-430. doi: 10.1377/hlthaff.2015.0805.

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. Epstein AM, Jha AK, Orav EJ, et al. Analysis of early accountable care organizations defines patient, structural, cost, and quality-of-care characteristics. Health Aff (Millwood). 2014;33(1):95-102. doi: 10.1377/hlthaff.2013.1063.

10. Guide to quality measurement for accountable care organizations starting in 2012: agreement period, performance year, and reporting period. CMS website. cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/ACO-Guide-Quality-Measurement-2012.pdf. Published 2012. Accessed August 11, 2017.

11. Medicare Shared Savings Program: shared savings and losses and assignment methodology: specifications. CMS website. cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/Shared-Savings-Losses-Assignment-Spec.pdf. Published December 2014. Accessed May 20, 2016.

12. 2013 Shared Savings Program accountable care organizations (ACO) PUF. CMS website. data.cms.gov/Special-Programs-Initiatives-Medicare-Shared-Savin/2013-Shared-Savings-Program-Accountable-Care-Organ/faep-t7cf. Published November 14, 2016. Updated September 21, 2017. Accessed December 12, 2017.

13. Medicare Pioneer Accountable Care Organization model Performance Year 1 (2012) results. CMS website. innovation.cms.gov/Files/x/pioneeraco-fncl-py1.pdf. Accessed December 12, 2017.

14. CMS, HHS. Medicare program; Medicare Shared Savings Program: Accountable Care Organizations. final rule. Fed Regist. 2011;76(212):67802-67990.

15. McWilliams JM, Chernew ME, Zaslavsky AM, Landon BE. Post-acute care and ACOs—who will be accountable? Health Serv Res. 2013;48(4):1526-1538. doi: 10.1111/1475-6773.12032.

16. Pioneer ACO alignment and financial reconciliation methods. CMS website. innovation.cms.gov/Files/x/PioneerACOBmarkMethodology.pdf. Published March 26, 2014. Accessed December 9, 2016.

17. Redaction of substance abuse claims. Research Data Assistance Center website. resdac.org/resconnect/articles/203. Updated June 5, 2017. Accessed April 6, 2016.

18. Rural-urban commuting area codes. United States Department of Agriculture Economic Research Service website. ers.usda.gov/data-products/rural-urban-commuting-area-codes. Updated October 12, 2016. Accessed October 12, 2017.

19. How the Census Bureau measures poverty. United States Census Bureau website. census.gov/topics/income-poverty/poverty/about.html. Updated May 12, 2016. Accessed April 7, 2016.

20. Medicare Managed Care Manual: chapter 7 — risk adjustment. CMS website. cms.gov/Regulations-and-Guidance/Guidance/Manuals/downloads/mc86c07.pdf. Published September 19, 2014. Accessed August 21, 2016.

21. Chapter 9: issues affecting dual-eligible beneficiaries: CMS’s financial alignment demonstration and the Medicare Shared Savings Programs. Medicare Payment Advisory Commission website. medpac.gov/docs/default-source/reports/chapter-9-issues-affecting-dual-eligible-beneficiaries-cms-s-financial-alignment-demonstration-and-t.pdf. Published June 2016. Accessed August 20, 2016.

22. Miller ME. Dual eligible beneficiaries and care coordination. National Health Policy Forum website. nhpf.org/uploads/Handouts/Miller-slides_07-23-10.pdf. Published July 23, 2010. Accessed May 23, 2017.

23. Coughlin TA, Waidmann TA, Phadera L. Among dual eligibles, identifying the highest-cost individuals could help in crafting more targeted and effective responses. Health Aff (Millwood). 2012;31(5):1083-1091. doi: 10.1377/hlthaff.2011.0729.

24. Kaiser Commission on Medicaid and the Uninsured. The diversity of dual eligible beneficiaries: an examination of services and spending for people eligible for both Medicaid and Medicare. Kaiser Family Foundation website. kaiserfamilyfoundation.files.wordpress.com/2013/01/7895-02.pdf. Published April 2012. Accessed August 21, 2016.

25. Young K, Garfield R, Musumeci M, Clemans-Cope L, Lawton E. Medicaid’s role for dual eligible beneficiaries. Kaiser Family Foundation website. kaiserfamilyfoundation.files.wordpress.com/2013/08/7846-04-medicaids-role-for-dual-eligible-beneficiaries.pdf. Published August 2013. Accessed August 20, 2016.

26. Medicare-Medicaid Coordination Office. Data analysis brief: Medicare-Medicaid dual enrollment from 2006 through 2013. CMS website. cms.gov/Medicare-Medicaid-Coordination/Medicare-and-Medicaid-Coordination/Medicare-Medicaid-Coordination-Office/Downloads/DualEnrollment20062013.pdf. Published December 2014. Accessed August 19, 2016.

27. Regions. United States Census Bureau website. census.gov/geo/reference/webatlas/regions.html. Accessed October 31, 2017.

28. Colla CH, Lewis VA, Kao L-S, O’Malley AJ, Chang C-H, Fisher ES. Association between Medicare accountable care organization implementation and spending among clinically vulnerable beneficiaries [retraction and replacement in JAMA Intern Med. 2017;177(11):1702-1703. doi: 10.1001/jamainternmed.2017.4130]. JAMA Intern Med. 2016;176(8):1167-1175. doi: 10.1001/jamainternmed.2016.2827.

29. 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.

30. Luft HS. Becoming accountable—opportunities and obstacles for ACOs. N Engl J Med. 2010;363(15):1389-1391. doi: 10.1056/NEJMp1009380.

31. Fitzpatrick AL, Powe NR, Cooper LS, Ives DG, Robbins JA. Barriers to health care access among the elderly and who perceives them. Am J Public Health. 2004;94(10):1788-1794.

32. A Data Book: Health Care Spending and the Medicare Program. Washington, DC: Medicare Payment Advisory Commission; 2017. medpac.gov/docs/default-source/data-book/jun17_databookentirereport_sec.pdf. Accessed October 12, 2017.

33. Skinner JS, Gottlieb DJ, Carmichael D. A new series of Medicare expenditure measures by hospital referral region: 2003-2008. The Dartmouth Atlas Project website. www.dartmouthatlas.org/downloads/reports/PA_Spending_Report_0611.pdf. Published June 21, 2011. Accessed May 9, 2017.

34. CMS, HHS. Medicare Program; Medicare Shared Savings Program; Accountable Care Organizations—revised benchmark rebasing methodology, facilitating transition to performance-based risk, and administrative finality of financial calculations. Final rule. Fed Regist. 2016;81(112):37949-38017.

35. Kocot SL, White R, McClellan M. The revised Medicare ACO program: more options…and more work ahead. Health Affairs Blog. June 16, 2015. healthaffairs.org/do/10.1377/hblog20150616.048573/full. Accessed January 13, 2017.

36. Medicare Shared Savings Program final rule overview. CMS website. cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/2015-SSP-ACO-Final-Rule-Overview-6-29-15.pdf. Published June 2015. Accessed January 18, 2017.

37. 2016 Medicare Shared Savings Program organizations. CMS website. data.cms.gov/ACO/2016-Medicare-Shared-Savings-Program-Organizations/5kdu-cnmy. Published February 12, 2016. Updated June 19, 2017. Accessed January 18, 2017.

38. Next Generation ACO Model. CMS website. innovation.cms.gov/initiatives/Next-Generation-ACO-Model. Updated May 7, 2018. Accessed December 12, 2016.

39. Medicare Shared Savings Program quality measure benchmarks for the 2015 reporting year. CMS website. cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/MSSP-QM-Benchmarks-2015.pdf. Published February 2015. Accessed August 17, 2017.

40. Guide to quality performance scoring methods for accountable care organizations. CMS website. cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/2012-11-ACO-quality-scoring-supplement.pdf. Published 2012. Accessed May 20, 2017.

41. Morden NE, Colla CH, Sequist TD, Rosenthal MB. Choosing wisely—the politics and economics of labeling low-value services. N Engl J Med. 2014;370(7):589-592. doi: 10.1056/NEJMp1314965.

42. Riley GF, Lubitz JD. Long-term trends in Medicare payments in the last year of life. Health Serv Res. 2010;45(2):565-576. doi: 10.1111/j.1475-6773.2010.01082.x.

43. Smith S, Brick A, O’Hara S, Normand C. Evidence on the cost and cost-effectiveness of palliative care: a literature review. Palliat Med. 2014;28(2):130-150. doi: 10.1177/0269216313493466.

44. Smith G, Bernacki R, Block SD. The role of palliative care in population management and accountable care organizations. J Palliat Med. 2015;18(6):486-494. doi: 10.1089/jpm.2014.0231.

45. Kelley AS, Meier DE. The role of palliative care in accountable care organizations. Am J Manag Care. 2015;21(spec no 6):SP212-SP214.

46. MACRA and alternative payment models: developing options for value-based care. House Energy and Commerce website. energycommerce.house.gov/hearings/macra-alternative-payment-models-developing-options-value-based-care. Published November 8, 2017. Accessed November 20, 2017.

47. Blumenthal D, Anderson G, Burke S, Fulmer T, Jha AK, Long P. Tailoring complex-care management, coordination, and integration for high-need, high-cost patients. National Academy of Medicine website. nam.edu/wp-content/uploads/2016/09/Tailoring-Complex-Care-Management-Coordination-and-Integration-for-High-Need-High-Cost-Patients.pdf. Published September 19, 2016. Accessed December 2, 2016.

48. Dunlop DD, Manheim LM, Song J, Chang RW. Gender and ethnic/racial disparities in health care utilization among older adults. J Gerontol B Psychol Sci Soc Sci. 2002;57(4):S221-S233.

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