Out-Of-Network Primary Care Is Associated With Higher Per Beneficiary Spending In Medicare ACOs

In an effort to curb health care spending growth and improve quality, the Centers for Medicare and Medicaid Services (CMS) launched the Medicare Shared Savings Program (MSSP) in 2012. This program uses collective incentives to motivate providers participating in an accountable care organization (ACO) to collaborate with each other. Despite great fanfare, MSSP evaluations have demonstrated only modest spending reductions,¹ with significant variation across ACOs in performance.^2,3 One reason for such variation may be differences in the level of out-of-network care that ACO beneficiaries receive. Unlike beneficiaries in health maintenance organizations, which are allowed to implement explicit mechanisms to incentivize patients to stay in network (for example, requiring referrals and charging higher fees for out-of-network care), beneficiaries assigned to an ACO are free to seek care from whomever they want. In fact, before the Bipartisan Budget Act of 2018, MSSP beneficiaries were assigned to ACOs after they had already received care (based on where they had received the plurality of primary care services that year), even though participating organizations were responsible for all costs of care received. While preserving patient choice is laudable, it may hinder ACOs from lowering health care spending.

Specifically, ACOs have few sight lines into and even less control over care delivered outside their boundaries, which makes it difficult for ACOs with higher proportions of out-of-network care to reduce spending.^4–6 Recognizing this, ACO leaders are paying increasing attention to out-of-network specialty care, given specialists’ outsize contribution to spending.⁷ However, out-of-network primary care may also be important because of the critical role that primary care providers play in gatekeeping and care coordination, which could have salutary effects on spending. Previous studies have demonstrated that patients in managed care systems receive more coordinated care and have lower inpatient use than those whose care is delivered fee-for-service.^8,9 This may be attributable to managed care systems’ ability to keep primary care visits within network. Although variation in out-of-network specialty care across ACOs has been described, little is known about out-of-network primary care and its effects on spending.^4,10

Furthermore, little is known about how patient, organizational, and regional characteristics may influence beneficiary choice or ACOs’ ability to limit care sought outside their networks. It may be that ACOs with sicker patients or fewer participating providers or those in underserved communities may face additional challenges to keeping patient care inside the network, leading to higher spending. Indeed, there are compelling data to suggest that ACOs are more likely to reduce spending in areas with lower poverty rates, smaller populations of marginalized communities, and more patients with higher levels of education.^11–13

To address these knowledge gaps, we analyzed national Medicare data. For each MSSP ACO we calculated the amount of primary and specialty care delivered outside its network by year. We then assessed whether the level of out-of-network care was associated with ACO characteristics. Finally, we assessed whether the levels of out-of-network primary and specialty care were associated with per beneficiary spending, both total and by setting—inpatient, outpatient, skilled nursing facility (SNF), and emergency department (ED).

Study Data And Methods

Data Sources And Study Population

For our study we used data for 2012–15 for a random 20 percent sample of Medicare beneficiaries. For those enrolled in fee-for-service Medicare, we analyzed claims from the Medicare Provider Analysis and Review file, the Medicare Outpatient Fee-for-Service Claims Research Identifiable Files, and Carrier Research Identifiable Files. Through the MSSP Beneficiary-Level Research Identifiable File, we were able to determine which beneficiaries were attributed to an MSSP ACO during a given year. We included only beneficiaries who had continuous Parts A and B coverage in that year. We excluded people younger than age sixty-five and those with end-stage renal disease. To identify and characterize ACO-participating providers, we used the MSSP Provider-Level Research Identifiable File and the Leavitt Partners ACO Database.¹⁴ We also gathered county-level data from the Area Health Resources Files of the Health Resources and Services Administration.

Calculating Out-Of-Network Care

For each ACO-year observation we used a two-step process to calculate the proportion of outpatient primary care and specialty care visits delivered out of network.⁴ We first abstracted preventive visits, annual wellness visits, and other outpatient visits from beneficiaries’ claims using Current Procedural Terminology codes. We classified outpatient visits as primary or specialty care based on Medicare Provider Specialty codes obtained from the Carrier Files. Primary care providers were identified as providers with a specialty of general practice, family practice, internal medicine, or geriatric medicine. Specialist providers were identified as non–primary care providers as specified by Medicare for calculating MSSP shared savings and losses.¹⁵

For each ACO we then calculated the proportion of out-of-network primary care as the number of outpatient visits provided to the ACO’s beneficiaries by primary care providers not in its contracting network, divided by the total number of outpatient visits provided to its beneficiaries by any primary care provider. We calculated the proportion of out-of-network specialty care similarly, dividing the number of outpatient visits made by an ACO’s beneficiaries to specialists not in its contracting network by the total number of outpatient visits provided to its beneficiaries by any specialist.

Measuring Per Beneficiary Spending

Our primary outcome was per beneficiary total spending. We calculated this by summing all price-standardized and inflation-adjusted payments from Parts A and B claims at the quarter level for each beneficiary. CMS uses total spending to calculate the amount of “shared savings” that participating ACOs receive as an incentive to reduce their costs. Specifically, ACOs are given a portion of the difference between actual total spending and a benchmark (which is based on historical performance), provided that specific quality metrics are met. Therefore, our primary outcome parallels the processes used by CMS to assess individual ACOs’ performance.

We focused on per beneficiary spending instead of other performance measures such as shared savings or clinical quality for multiple reasons. First, we wanted our results to be directly comparable to those of existing studies. The bulk of the literature on ACO performance and many CMS evaluations have used per beneficiary spending as a main outcome.^1,16 Second, the goal of this study was to understand whether ACO spending varies with out-of-network care among ACOs. To do this, we needed a measure of health care spending that could be compared across all ACOs. The way that shared savings are calculated was problematic for our purposes because it is based on ACO- and population-specific factors that could systematically bias our results.¹⁷ Third, as described above, the dollar amount of the MSSP incentive award is directly related to health care spending for a given ACO’s beneficiaries. Thus, once specific quality metrics are met, MSSP ACOs may be more likely to focus on cost reduction efforts than on further quality improvement. Fourth, data suggest that efforts to improve clinical quality in ACOs are still maturing. In particular, MSSP ACOs have made only small quality gains, especially in the early years of ACO implementation from which our study drew its data.¹⁸ Finally, while studies have demonstrated significant spending reductions in ACOs over time,¹⁸ the exact mechanism through which these reductions occur is unknown. By focusing on spending, our study attempted to address this knowledge gap.

To understand how patterns of spending change with level of out-of-network care, we broke down total spending by place of service (that is, inpatient, outpatient, SNF, and ED settings). We chose to examine these specific categories based on prior literature on ACOs and managed care,^9,19,20 which has suggested that ACOs may be reducing costs by shifting utilization from one care setting to another. For example, if out-of-network care is associated with increased outpatient and decreased inpatient spending, this would imply that ACO providers may be shifting expensive inpatient procedures to less costly ambulatory venues. We identified place of service using Medicare place-of-service codes.

Statistical Analyses

For our initial analytic step, we examined the median and interquartile range of the percentage of out-of-network primary and specialty care, as well as yearly percentage and percentage-point changes in out-of-network primary and specialty care. We also evaluated the extent to which an organization’s level of out-of-network primary care was correlated with its level of out-of-network specialty care.

We then sought to understand how ACOs with high versus low levels of out-of-network care differ. We conducted bivariate analyses, examining characteristics at the patient, organizational, and regional levels that we hypothesized were related to beneficiary choice and ability to seek care outside their ACO’s network. In particular, we compared ACOs in the highest quartile of out-of-network primary and specialty care in 2015 with ACOs in the three lower quartiles. We examined differences in their assigned beneficiaries with respect to age, sex, race/ethnicity, comorbidity (assessed by Hierarchical Condition Categories),^21,22 and dual eligibility (for Medicare and Medicaid) distributions. We also examined ACOs’ organizational differences: the percentage of primary care providers; leadership (physician, hospital, or joint); risk-bearing status; size (the numbers of beneficiaries and providers); and whether the ACO included a critical access hospital, federally qualified health center, or acute care hospital. We examined regional differences: population, rural location, median per capita income, percentages of families living in poverty and of people ages twenty-five and older with less than a high school education, supply of medical doctors and primary care providers, and numbers of SNFs and hospitals.

Finally, we assessed the association between the percentage of out-of-network care and per beneficiary spending—adjusted for patient, ACO, and regional characteristics—using two-part multivariable regression models on our beneficiary-quarter level data set (from the first quarter of 2012 through the fourth quarter of 2015).²³ Compared to linear regression, two-part models were more appropriate for our analysis given the high proportion of patients with no spending per quarter. The first part of our two-part model used a logistic regression to predict whether a patient had any Medicare spending. The second part used a generalized linear model to predict the amount of spending, conditional on any spending. We present our results from these models as marginal effects across both parts to make inferences about the entire study population.

Our models included ACO fixed effects to account for non-time-varying ACO-specific effects, quarter-year fixed effects, the beneficiary characteristics described above (to control for beneficiary-level factors, such as comorbidity, that may simultaneously influence out-of-network care and costs), the organizational characteristics described above (for example, the number of providers, risk-bearing status, and size), and selected regional characteristics. In addition to controlling for seasonal effects, quarter-year fixed effects allowed us to account for differences in the way beneficiaries were attributed to ACOs in 2012 compared to the other years in our study period (2012 attribution was based on 2013 primary care use, which resulted in higher levels of out-of-network primary care for 2012). To make our results more interpretable, we used our models to predict the annual change in spending across all ACOs if every ACO decreased out-of-network primary care by a tenth of a percentage point (which represents 1 percent of the median 2015 level). Eleven ACOs in the 2014 MSSP Provider-Level Research Identifiable File and Shared Savings Program Accountable Care Organizations Public-Use File had no affiliated provider National Provider Identifiers at the time of this analysis. Therefore, beneficiaries assigned to these ACOs were dropped (they accounted for 0.5 percent of beneficiary-quarter observations, or 56,024 beneficiary-quarter observations).

Sensitivity Analyses

We conducted several sensitivity analyses. Because more complex patients are also more likely to seek care outside the network and have higher spending, ACOs with more complex patients may have higher out-of-network care and spending. Although our main analysis controlled for comorbidity in beneficiaries, endogeneity could still introduce bias. To examine the size of this potential issue, we ran the models without controls. We also tested the sensitivity of our main results to this issue by calculating out-of-network care using a random 50 percent “holdout” sample of beneficiaries in each ACO-year and predicting spending for the remaining 50 percent of beneficiaries. Our next sensitivity analysis included a measure of whether or not a health information organization was present in the hospital referral region in which the majority of an ACO’s beneficiaries resided, as health information organizations may facilitate lower health care costs and greater care continuity. Our next three sensitivity analyses tested the robustness of our results to outliers in out-of-network primary care. Outliers may exist both because of differences in how patients were attributed to ACOs in 2012 and because some patients are attributed to ACOs despite having a very low number of primary care visits. Finally, our last sensitivity analysis used lagged levels of out-of-network care to assess the robustness of our main analysis to learning effects. Full details of our methodology are in online appendix exhibit A1.²⁴

Limitations

Our study had several limitations. First, because we used a retrospective observational design, the potential for omitted variable bias exists. We addressed this using the sensitivity analyses described above.

Second, the findings of our study might not apply to ACOs outside the study sample, such as commercial ACOs, Medicare Next Generation ACOs, and End-Stage Renal Disease Seamless Care Organizations. Nonetheless, given that the vast majority of Medicare ACOs participate in the MSSP and Medicare is the main driver of the ACO model, our findings are still critically important to understanding how ACO design can influence spending.

Finally, it is important to note that we did not examine the association between out-of-network care and other measures of ACO performance (for example, processes and outcomes). Therefore, we cannot provide insights into the relationship between out-of-network care and quality. Future research will need to examine this relationship, especially in light of our finding that ACOs that serve disadvantaged populations have higher levels of out-of-network care. If out-of-network care is also associated with poorer quality, these ACOs may also struggle to meet the required benchmarks, further exacerbating existing disparities.

Study Results

Our sample included 1,604,809 unique beneficiaries. The number of MSSP ACOs from which these beneficiaries received care grew from 114 in 2012 to 392 in 2015. Overall, the median level of out-of-network primary care was 8 percent (interquartile range: 6–10 percent), and the median level of out-of-network specialty care was 82 percent (IQR: 66–95 percent). While overall levels remained constant across the study years (exhibit 1), we observed variation in yearly change in out-of-network care at the ACO level. Across all study years, the median yearly change in out-of-network care was −0.27 percentage points (IQR: −1.77–0.59) for primary care and 0.00 percentage points (IQR: −1.84–1.31) for specialty care, which represented −3.54 percent (IQR: −19.72–8.43 percent) and 0.00 percent (IQR: −2.40–1.75 percent) changes, respectively (data not shown). Levels of out-of-network primary and specialty care were not highly correlated in any year (average correlation: 0.23). Quarterly spending per beneficiary ranged from $0 to $391,937 for total spending, $0 to $379,498 for inpatient spending, $0 to $330,645 for outpatient spending, $0 to $117,135 for SNF spending, and $0 to $350,531 for ED spending. The distribution of spending was left-skewed for all spending categories, with many beneficiaries having no spending.

SOURCE Authors’ analysis of Medicare claims for 2012–15. NOTE The error bars indicate interquartile ranges.

Relationship Between Accountable Care Organization Characteristics And Out-Of-Network Care

ACOs in the highest quartile of out-of-network primary care and those in the highest quartile of out-of-network specialty care shared certain characteristics. Compared to ACOs in the lower three quartiles, these ACOs had significantly different patient, organizational, and regional characteristics. These ACOs cared for beneficiaries who had more comorbidities, were older, and were more likely to be dually eligible and to be black or Hispanic (exhibit 2). They also had fewer beneficiaries and providers, were more likely to be physician led, were less likely to have an acute care hospital, and had a larger proportion of primary care providers (appendix exhibit A2).²⁴ They were also more likely to be located in rural areas and in areas with higher poverty levels and less educated populations.

Relationship Between Out-Of-Network Care And Spending

The median level of total per beneficiary spending for ACO beneficiaries was $401 per quarter (IQR: $140–$1,131) (data not shown). When we held control characteristics constant, we found that increasing levels of out-of-network primary care were associated with higher per beneficiary spending. Each percentage-point increase in out-of-network primary care was associated with a $10.79 increase in total spending for each beneficiary per quarter (exhibit 3). When we examined the relationship between out-of-network primary care and spending by setting, we found that each percentage-point increase in out-of-network primary care was associated with increases in outpatient ($2.80), SNF ($3.97), and ED setting ($2.59) spending. However, changes in out-of-network specialty care were not significantly associated with total spending or spending in any specific setting. To put these numbers in context at the national level, our estimated quarterly savings of $10.79 per percentage-point increase in out-of-network primary care suggests that if all MSSP ACOs decreased their level of out-of-network primary care by one-tenth of a percentage point for all 10.5 million of their beneficiaries (based on 2018 enrollment), national ACO spending would decrease by an estimated $11.3 million per quarter, or approximately $45 million per year.

SOURCE Authors’ analysis of Medicare claims for 2012–15. NOTES One model was run for each spending category. Models included accountable care organization (ACO) and quarter-year fixed effects and the following controls: sex; race; age; dual eligibility for Medicare and Medicaid; Hierarchical Condition Categories; whether the beneficiary was a new Medicare enrollee; percent of ACO providers who were primary care providers; number of beneficiaries in the study sample; number of providers; ACO leadership (hospital, physician, or joint); whether the ACO included a critical access hospital, federally qualified health center, or acute care hospital; ACO risk-bearing status (one- or two-sided; see exhibit 2 notes); numbers of primary care providers, medical doctors, and skilled nursing facilities in the county; county population; and county median per capita income. ED is emergency department. *$p<0.10$ **$p<0.05$ ***$p<0.01$ ****$p<0.001$

Sensitivity Analyses

When we ran the analyses without time-varying controls to assess the potential size of selection bias, we found that out-of-network primary care was still significantly associated with higher total, outpatient, and SNF spending (appendix exhibit A3).²⁴ However, effect sizes for ED spending could not be estimated because convergence for the statistical model could not be attained. Additionally, results from our second sensitivity analysis, which used the level of out-of-network care for a random 50 percent subsample to predict spending on the remaining 50 percent of beneficiaries, were also consistent with our main finding that out-of-network primary care was associated with total spending (appendix exhibit A4).²⁴ The results from our third sensitivity analysis, in which we controlled for the presence of health information organizations, were also consistent with our main findings (appendix exhibit A5).²⁴ In addition, the results from our sensitivity analyses in which we removed outliers were consistent with our main results (appendix exhibits A6–A8).²⁴ Our final sensitivity analysis, which used one-year lags, suggested that the level of out-of-network care did not significantly predict the next year’s level of spending (appendix exhibit A9).²⁴

Discussion

In a national study of Medicare ACOs, we found that more out-of-network primary care was associated with higher total spending. When we broke this relationship down into spending by place of service, out-of-network primary care was associated with higher spending in outpatient, SNF, and ED settings. We observed that ACOs that served poorer and sicker patients and people from underserved communities were more likely to have high levels of out-of-network primary care. Collectively, our findings suggest that reducing out-of-network primary care could yield substantial savings for MSSP ACOs.

Our study suggests that ACO administrators may have focused too much attention on reducing specialty care usage.

The existing literature on out-of-network care in Medicare ACOs has focused primarily on specialty care.^4,10 However, we found that out-of-network specialty care was not associated with an ACO’s performance. The lack of an association may explain why early MSSP evaluations revealed only modest ACO effects on spending, with most gains occurring in regions with high pre-ACO spending.^1,25 Thus, our study suggests that ACO administrators may have focused too much attention on reducing specialty care usage, while overlooking the importance of out-of-network primary care.²⁶

Our study also suggests that MSSP ACOs with high levels of out-of-network primary care may have difficulty achieving shared savings. However, even small changes in out-of-network primary care could have large effects on performance. Indeed, reducing out-of-network primary care across all MSSP ACOs by one-tenth of a percentage point could save Medicare an estimated $45 million annually. This finding is most relevant for ACOs with high shares of beneficiaries from underserved backgrounds, who are more likely to receive out-of-network primary care.

One possible explanation for the association between out-of-network primary care and health care spending relates to churn. Specifically, higher-spending ACOs may also have substantial beneficiary and provider turnover year-to-year, leading to both higher levels of out-of-network care and poorer organizational performance.²⁷ However, if this were the case, we would not expect to find the differential effects of out-of-network primary care by place of service that we observed.

There are at least two plausible reasons why out-of-network primary care was associated with health care spending in outpatient, SNF, and ED settings. First, primary care providers who participate in an ACO may be acting as “gatekeepers,” reducing the provision of unnecessary or lower-value services—which are often delivered in these settings. For example, the providers may limit access to specialists who deliver lower-value care, or they may refer patients to home health agencies instead of SNFs. Previous studies that showed that cost savings in ACOs are driven by decreases in use of unnecessary and lower-value services in SNFs^16,28 provide support for this possibility. Second, primary care providers who participate in an ACO may be acting as “care coordinators,” ensuring that patients are not receiving duplicative care in outpatient and ED settings. Prior research that revealed that care coordination is associated with less redundant imaging in EDs and fewer ED visits^20,29–31 supports this possibility.

Our finding that out-of-network primary care is not associated with differences in inpatient spending is not surprising, given that the bulk of ACO savings results from reductions in overused skilled nursing services, outpatient departments, and home health care.^1,28,32 This is likely due to the fact that primary care providers who participate in an ACO have limited control over the use of inpatient services, or to the fact that interventions at the primary care level need more time to mature before bringing about meaningful effects on inpatient spending.

Policy Implications

In light of the recent MSSP overhaul (detailed in CMS’s “Pathways to Success” rule in 2018) that will require ACOs to assume downside risk more quickly,³³ our findings suggest that controlling the level of out-of-network primary care may be one mechanism to lower spending. Our findings also suggest that Medicare might realize more savings if all of its ACO initiatives created explicit incentives for beneficiaries to seek primary care within network. For instance, copayments could be lowered for in-network primary care services. The ACO Beneficiary Incentive Program, which allows certain ACOs to pay beneficiaries a monetary incentive to receive primary care in network, could also be expanded.³⁴ Of course, incentives such as these must be balanced with the preservation of patient choice—a defining characteristic of MSSP ACOs.

ACOs with high levels of out-of-network primary care serve poorer and sicker patients and those from underserved communities.

We also found that ACOs with high levels of out-of-network primary care serve poorer and sicker patients and those from underserved communities. Perhaps these patients lack the mobility necessary to maintain continuity of care.³⁵ Such an explanation is supported by prior work that showed that ACOs with higher proportions of disadvantaged populations are less likely to achieve shared savings than those with more affluent populations.¹² Without additional support, it is less likely that these ACOs will be able to thrive under current Medicare policies, leading to the further bifurcation of an already two-tiered health care system.³⁶ To avoid exacerbating existing disparities, policy makers should consider providing data on out-of-network care to these ACOs to help identify reasons why their patients are seeking primary care services elsewhere. Policy makers could also provide training and resources to encourage these ACOs to offer telehealth services in line with those covered under the Bipartisan Budget Act of 2018.

Conclusion

In this study we found that higher levels of out-of-network primary care are associated with higher health care spending for Medicare beneficiaries in ACOs, especially in outpatient, SNF, and ED settings. These findings highlight the critical role of primary care physicians and suggest that ACOs that are not able to keep primary care services in network are less likely to succeed under current ACO policies. Relatedly, we also found that ACOs with higher levels of out-of-network primary care were also more likely to serve disadvantaged patients, which suggests that current Medicare ACO design may inadvertently exacerbate existing health care disparities. Consequently, policy makers may want to consider explicit incentives for beneficiaries to seek primary care within their ACOs.

ACKNOWLEDGMENTS

Sunny Lin is supported by a grant from the Agency for Healthcare Research and Quality (AHRQ) (Grant No. R36HS025875-01A1). Andrew Ryan is supported by a grant from the National Institute on Aging (Grant No. R01AG047932). Julia Adler-Milstein is supported by a grant from AHRQ (Grant No. 1R01HS025165-01A1). John Hollingsworth is supported by grants from AHRQ (Grant Nos. R01HS024728 and R01HS024525). The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ or the National Institute on Aging.

NOTES

• 1 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):2357–66. Google Scholar
• 2 McClellan MB, Kocot SL, White R. Early evidence on Medicare ACOs and next steps for the Medicare ACO program (updated). Health Affairs Blog [blog on the Internet]. 2015 Jan 22 [cited 2019 Nov 21]. Available from: https://www.healthaffairs.org/do/10.1377/hblog20150122.043904/full/ Google Scholar
• 3 Muhlestein D, Hall C. ACO quality results: good but not great. Health Affairs Blog [blog on the Internet]. 2014 Dec 18 [cited 2019 Nov 21]. Available from: https://www.healthaffairs.org/do/10.1377/hblog20141218.043429/full/ Google Scholar
• 4 McWilliams JM, Chernew ME, Dalton JB, Landon BE. Outpatient care patterns and organizational accountability in Medicare. JAMA Intern Med. 2014;174(6):938–45. Crossref, Medline, Google Scholar
• 5 Han MA, Clarke R, Ettner SL, Steers WN, Leng M, Mangione CM. Predictors of out-of-ACO care in the Medicare Shared Savings Program. Med Care. 2016;54(7):679–88. Crossref, Medline, Google Scholar
• 6 McWilliams JM. Cost containment and the tale of care coordination. N Engl J Med. 2016;375(23):2218–20. Crossref, Medline, Google Scholar
• 7 Medicare Payment Advisory Commission. Health care spending and the Medicare program: a data book [Internet]. Washington (DC): MedPAC; 2016 Jun [cited 2019 Nov 21]. Available from: http://www.medpac.gov/docs/default-source/data-book/june-2016-data-book-health-care-spending-and-the-medicare-program.pdf Google Scholar
• 8 Safran DG, Tarlov AR, Rogers WH. Primary care performance in fee-for-service and prepaid health care systems. Results from the Medical Outcomes Study. JAMA. 1994;271(20):1579–86. Crossref, Medline, Google Scholar
• 9 Miller RH, Luft HS. Managed care plan performance since 1980. A literature analysis. JAMA. 1994;271(19):1512–9. Google Scholar
• 10 Barnett ML, McWilliams JM. Changes in specialty care use and leakage in Medicare accountable care organizations. Am J Manag Care. 2018;24(5):e141–9. Medline, Google Scholar
• 11 Lewis VA, Colla CH, Carluzzo KL, Kler SE, Fisher ES. Accountable care organizations in the United States: market and demographic factors associated with formation. Health Serv Res. 2013;48(6 Pt 1, 6pt1):1840–58. Crossref, Medline, Google Scholar
• 12 Lewis VA, Fraze T, Fisher ES, Shortell SM, Colla CH. ACOs serving high proportions of racial and ethnic minorities lag in quality performance. Health Aff (Millwood). 2017;36(1):57–66. Go to the article, Google Scholar
• 13 Yasaitis LC, Pajerowski W, Polsky D, Werner RM. Physicians’ participation in ACOs is lower in places with vulnerable populations than in more affluent communities. Health Aff (Millwood). 2016;35(8):1382–90. Go to the article, Google Scholar
• 14 Colla CH, Lewis VA, Tierney E, Muhlestein DB. Hospitals participating in ACOs tend to be large and urban, allowing access to capital and data. Health Aff (Millwood). 2016;35(3):431–9. Go to the article, Google Scholar
• 15 For a full list of specialty codes, see Centers for Medicare and Medicaid Services. Medicare Shared Savings Program: shared savings and losses and assignment methodology: specifications: version 3 [Internet]. Baltimore (MD): CMS; 2014 Dec. Table 3, CMS specialty codes included in the definition of a physician (MD/DO only); [cited 2019 Dec 12]. Available from: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/Shared-Savings-Losses-Assignment-Spec.pdf Google Scholar
• 16 Nyweide DJ, Lee W, Cuerdon TT, Pham HH, Cox M, Rajkumar Ret al. Association of Pioneer Accountable Care Organizations vs traditional Medicare fee for service with spending, utilization, and patient experience. JAMA. 2015;313(21):2152–61. Crossref, Medline, Google Scholar
• 17 Chernew ME, Barbey C, McWilliams JM. Savings reported by CMS do not measure true ACO savings. Health Affairs Blog [blog on the Internet]. 2017 Jun 19 [cited 2019 Nov 21]. Available from: https://www.healthaffairs.org/do/10.1377/hblog20170619.060649/full/ Google Scholar
• 18 Kaufman BG, Spivack BS, Stearns SC, Song PH, O’Brien EC. Impact of accountable care organizations on utilization, care, and outcomes: a systematic review. Med Care Res Rev. 2019;76(3):255–90. Crossref, Medline, Google Scholar
• 19 Hsu J, Price M, Vogeli C, Brand R, Chernew ME, Chaguturu SKet al. Bending the spending curve by altering care delivery patterns: the role of care management within a Pioneer ACO. Health Aff (Millwood). 2017;36(5):876–84. Go to the article, Google Scholar
• 20 Larson BK, Van Citters AD, Kreindler SA, Carluzzo KL, Gbemudu JN, Wu FMet al. Insights from transformations under way at four Brookings-Dartmouth accountable care organization pilot sites. Health Aff (Millwood). 2012;31(11):2395–406. Go to the article, Google Scholar
• 21 Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27. Crossref, Medline, Google Scholar
• 22 Southern DA, Quan H, Ghali WA. Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care. 2004;42(4):355–60. Crossref, Medline, Google Scholar
• 23 Deb P, Norton EC. Modeling health care expenditures and use. Annu Rev Public Health. 2018;39:489–505. Crossref, Medline, Google Scholar
• 24 To access the appendix, click on the Details tab of the article online.
• 25 Epstein AM, Jha AK, Orav EJ, Liebman DL, Audet AM, Zezza MAet al. Analysis of early accountable care organizations defines patient, structural, cost, and quality-of-care characteristics. Health Aff (Millwood). 2014;33(1):95–102. Go to the article, Google Scholar
• 26 Rittenhouse DR, Shortell SM, Fisher ES. Primary care and accountable care—two essential elements of delivery-system reform. N Engl J Med. 2009;361(24):2301–3. Crossref, Medline, Google Scholar
• 27 Hsu J, Vogeli C, Price M, Brand R, Chernew ME, Mohta Net al. Substantial physician turnover and beneficiary “churn” in a large Medicare Pioneer ACO. Health Aff (Millwood). 2017;36(4):640–8. Go to the article, Google Scholar
• 28 McWilliams JM, Gilstrap LG, Stevenson DG, Chernew ME, Huskamp HA, Grabowski DC. Changes in postacute care in the Medicare Shared Savings Program. JAMA Intern Med. 2017;177(4):518–26. Crossref, Medline, Google Scholar
• 29 Cabana MD, Jee SH. Does continuity of care improve patient outcomes? J Fam Pract. 2004;53(12):974–80. Medline, Google Scholar
• 30 van Walraven C, Oake N, Jennings A, Forster AJ. The association between continuity of care and outcomes: a systematic and critical review. J Eval Clin Pract. 2010;16(5):947–56. Crossref, Medline, Google Scholar
• 31 Lammers EJ, Adler-Milstein J, Kocher KE. Does health information exchange reduce redundant imaging? Evidence from emergency departments. Med Care. 2014;52(3):227–34. Crossref, Medline, Google Scholar
• 32 McWilliams JM, Chernew ME, Landon BE. Medicare ACO program savings not tied to preventable hospitalizations or concentrated among high-risk patients. Health Aff (Millwood). 2017;36(12):2085–93. Go to the article, Google Scholar
• 33 Centers for Medicare and Medicaid Services. Medicare program; Medicare Shared Savings Program; accountable care organizations—pathways to success and extreme and uncontrollable circumstances policies for performance year 2017. Final rule. Fed Regist. 2018;83(249):67816–8082. Medline, Google Scholar
• 34 Schulz J, DeCamp M, Berkowitz SA. Medicare Shared Savings Program: public reporting and shared savings distributions. Am J Manag Care. 2015;21(8):546–53. Medline, Google Scholar
• 35 Nutting PA, Goodwin MA, Flocke SA, Zyzanski SJ, Stange KC. Continuity of primary care: to whom does it matter and when? Ann Fam Med. 2003;1(3):149–55. Crossref, Medline, Google Scholar
• 36 Lewis VA, Larson BK, McClurg AB, Boswell RG, Fisher ES. The promise and peril of accountable care for vulnerable populations: a framework for overcoming obstacles. Health Aff (Millwood). 2012;31(8):1777–85. Go to the article, Google Scholar