Physician practices intending to join Medicare accountable care organizations (ACOs) in 2012 had greater capabilities in health information technology, care management processes, and quality improvement methods than those not intending to join, but they still were far from using all recommended behaviors to manage risk.
Objectives: To assess whether the characteristics and capabilities of individual practices intending to join the early Medicare accountable care organization (ACO) programs differed from those of practices not intending to join.
Study Design: Data from a 2012-2013 national survey of 1398 physician practices were linked to 2012 Medicare beneficiary claims data to examine differences between practices intending to join a Medicare ACO and practices not intending to join a Medicare ACO.
Methods: Differences were examined with regard to patient sociodemographic characteristics and disease burden, practice characteristics and capabilities, and cost and quality measures. Logistic regression was used to examine the differences.
Results: Practices intending to join were more likely to have better care management capabilities (odds ratio [OR], 1.72; P <.003), health information technology functionality (OR, 1.87; P <.001), and use of quality improvement methods (OR, 1.52; P <.04). They were also more likely to have had prior pay-for-performance experience (OR, 1.59; P <.02) and less likely to be physician-owned (OR, 0.51; P <.001). However, the practices with the greater capabilities still used half or less of them.
Conclusions: Physician practices that intended to join the early ACO programs had greater capabilities and experience to manage risk than those practices that decided not to join. The early ACO programs thus attracted the more capable physician practices, but those practices still fell short of implementing key recommended behaviors. The findings have implications for future physician practice selection into ACOs.
Am J Manag Care. 2018;24(10):469-474Takeaway Points
A major response to the continued growth in healthcare costs and the highly varying quality of care in the United States has been the formation of accountable care organizations (ACOs). As of 2018, there are 1011 ACOs, with at least 1 located in every state, serving approximately 32 million people.1 Enrollment has been projected to grow to 68 million by 2020.2 Despite high hopes for savings, early research suggests only modest savings, albeit with no diminution of quality and, in fact, with increases on some quality measures.3-8 Substantial variation in the performance of ACOs has also been noted.9,10
There are a number of possible reasons that ACOs thus far appear to have achieved only modest savings. This paper’s contribution lies in using a unique national survey of physician practices to explore the possibility that there were differences in characteristics and capabilities between physician practices that intended to join the Medicare ACO programs in 2012 and those that did not. Presumably, those practices intending to join might have been larger, had greater access to resources, and believed they had greater ability to contain costs, achieve savings, and improve quality by doing a better job of managing patient care.
Taking a look back to the beginning of the ACO program is important for considering what may happen with the introduction of alternative payment models (APMs) and the Merit-based Incentive Payment System (MIPS) authorized to take effect in 2019 as part of the Medicare Access and CHIP Reauthorization Act. Practice leaders will need to assess their ability to take on financial risk and meet the challenge of performance metrics. Due to the importance of health information technology (HIT), care management processes (CMPs), and quality improvement (QI), we assess whether practices with strong capabilities in these areas were more or less likely to intend to join.11-14 Such capabilities are markers of a practice’s ability to redesign care to meet quality goals and achieve shared savings. If practices without strong capabilities were the most likely to join, then the ability of ACOs to implement the necessary changes to drive higher-value care may have been limited, with implications for those that have joined more recently and those intending to join in the future.
STUDY DATA AND METHODS
We draw on a unique national survey of individual physician practices in 2012-2013 to identify those choosing to join the Medicare ACO programs versus those not choosing to join. We collected data on practice ownership, size, care management capabilities, HIT use, QI processes, and related variables. We linked these data to the Medicare claims files to examine baseline differences across practices in Medicare patient characteristics and illness severity/disease burden, focusing on high-cost/high-need patients’ spending, ambulatory care—sensitive admissions (ACSAs), and 30-day unplanned hospital readmissions.15,16
Specifically, we used the IMS Healthcare Organizational Services Database to select a national sample of physician practices, with oversampling in 17 communities that participated in the Robert Wood Johnson Foundation Aligning Forces for Quality program. Veterans Health Administration practices and academic medical center practices were excluded. We focused on practices most likely to provide care for patients with chronic illnesses, including asthma, congestive heart failure, depression, and diabetes. These included general internal medicine, family medicine, general practice, cardiology, endocrinology, and pulmonology practices.
Between January 2012 and November 2013, the physician or administrative practice leader completed a 40-minute telephone survey administered by RTI International. The survey instrument (eAppendix A [eAppendices available at ajmc.com]) was based on prior National Survey of Physician Organizations (NSPO) surveys. A total of 1398 practices responded, yielding an adjusted response rate of 50%.17 We restricted our analyses to the practices that included at least 15% primary care physicians (PCPs), which totaled 1040 practices. Of these, 235 (22.6%) practices intended to join a CMS ACO program. These data were then linked to the 2012 Medicare beneficiary claims data using the CMS method for assigning beneficiaries to ACOs (eAppendix B). A total of 868,213 beneficiaries were linked to surveyed practices; 311,116 beneficiaries were attributed to the physician practices intending to join the CMS ACO program. Beneficiaries were eligible if they were 65 years or older as of January 1, 2011; were not in the end-stage renal disease program; and were alive and enrolled in Medicare parts A and B throughout 2011 and 2012. The research protocol was approved by the Institutional Review Board of the University of California, Berkeley.
The dichotomous dependent variable was the practice’s intention to join an ACO in 2012. Specifically, respondents were asked if their organization applied to become an ACO during 2012. For our analysis, we constructed a 0/1 variable indicating those that responded affirmatively to this question.
Our principal independent variables of interest were measures of CMPs, HIT functionality, and QI activities. Each practice was measured on a 20-item care management capability index, which included items involving nurse care management, reminders and education for patients, provision of quality data to physicians, and use of a registry to identify patients with chronic illnesses. Each practice was also measured on a 14-item HIT functionality index that included various uses of an electronic health record, such as clinical decision support, collection of quality data, and electronic connectivity with patients. QI activities were measured based on whether the practice used 1 or more of 5 specific QI processes, including Plan-Do-Study-Act cycles, lean, Six Sigma, and related approaches. The elements of these 3 indices along with their reliability are shown in eAppendix C. We recoded these variables and placed the practices into dichotomous categories based on whether or not they scored in the top quartile of each composite index.
In addition, we included a number of other measures of practice characteristics in our analysis. Ownership was measured by whether the practice was physician-owned, hospital-owned, or a federally qualified health center (FQHC) or community health center (CHC). Practice size was measured by the 6 categories shown in Table 1 and ranged from 1 or 2 physicians to more than 100. We also included each practice’s percentage of PCPs as a continuous variable and whether or not the practice had previous experience with pay-for-performance and/or public reporting programs.
Using the 2012 Master Beneficiary Summary File Cost and Use segment, we measured total spending per beneficiary, defined as the allowed amounts paid for services by Medicare, coinsurers, and the beneficiary. We also measured spending in 4 subcategories: hospital services, physician services, postacute care, and other services. Outliers were reduced to the amount spent at the 99th percentile for each subcategory. Each subcategory was then summed to measure total spending. We standardized spending to adjust for geographic differences in Medicare payments at the county level by multiplying spending in each category by the ratio of Medicare county-level total standardized spending to total actual spending in that county in 2012.18 We used this to construct practice-level measures of mean spending.
To assess severity of illness/disease burden, we used 2011 Medicare claims and modified a method developed by Jha et al by placing each beneficiary into 1 of 5 categories of need, ranging from those predicted to have the highest need (beneficiaries with 2 or more frail conditions) to the lowest need (beneficiaries with no chronic conditions), based on total spending on care in 2012.19 For purposes of this analysis, we collapsed the category with 3 or more chronic conditions and the category with 2 or more frailty conditions into 1 category labeled high-need, and those with 2 or fewer major conditions, only minor conditions, or no other conditions into the low-need category.
We used 2 utilization-based measures of quality. The first was the number of unplanned 30-day hospital readmissions for each beneficiary, measured using the hospitalwide (all-condition) 30-day risk-standardized readmission measure, which excludes planned readmissions.20 We computed whether or not the practice was above or below the median on this measure. Second, we measured the number of ACSAs for each beneficiary for conditions, such as congestive heart failure, chronic obstructive pulmonary disease, and short-term complications of diabetes, for which good outpatient care may reduce the rate of hospital admissions.21 We computed whether or not the practice was above or below the median on this measure.
From the Medicare Master Beneficiary Summary File, we included beneficiaries’ age, sex, racial/ethnic group, whether the beneficiary was dual-eligible (covered by both Medicare and Medicaid), and whether disability was the original reason for Medicare eligibility. We used these data to calculate the share of each practice’s Medicare beneficiaries in each of the above areas. We also calculated the percentage of the practice’s patients that were in the high-need/high-complexity category versus the low-need/low-complexity category. To account for possible heterogeneity within the need and complexity categories, we also included variables for each beneficiary’s number of major and minor conditions. We also took into account the percentage of the practice’s revenue from Medicaid, measured as a continuous variable. Finally, we included the mean annual spending per Medicare beneficiary for the hospital referral region in which the beneficiary resided, in order to account for the possible influence of local practice norms.
We used a logistic model to evaluate our binary dependent variable of whether or not the practice chose to join an ACO. All analyses were conducted accessing the Medicare Virtual Data Research Center using SAS Enterprise Guide 7.1 (SAS Institute; Cary, North Carolina).
Table 1 shows that the practices that chose to join a CMS ACO program had a greater number of Medicare beneficiaries, had twice as many practices with greater than 100 physicians, were more likely to be hospital-owned, had more experience with pay-for-performance and public reporting programs, and had greater capabilities in CMPs, HIT functionality, and QI activities than practices that did not join. It is important to note that, as Table 1 illustrates, even the practices scoring in the top quartile of the QI index and the CMP index were using less than half of the available recommended capabilities and, in regard to HIT functionality, were using only half. The 2 groups did not differ on any of the characteristics of patients seen, including the percentage of high-need/high-complexity patients. There were also no differences in total spending (costs), ACSA rates, or 30-day unplanned hospital readmissions.
The multivariable results shown in Table 2 indicate that the practices that joined ACOs had more prior experience with pay-for-performance programs (odds ratio [OR], 1.59) and greater capabilities in CMPs (OR, 1.72), HIT functionality (OR, 1.87), and QI (OR, 1.52). The results also indicate that the physician-owned practices and the FQHC/CHC sites were less likely to join than the hospital-owned practices (ORs, 0.51 and 0.31, respectively). There were no differences in practice size, types of patients treated, spending, ACSAs, or 30-day unplanned readmissions. The latter 2 variables were also assessed by whether or not a practice/group had any ACSAs or not or any 30-day unplanned readmissions or not, with virtually identical results.
We expected that practices intending to join ACOs would have greater CMP, HIT, and QI capabilities in order to better manage risk and achieve shared savings. The data support this expectation. But at the same time, even those scoring in the top quartile had only between 45% and 50% of the identified capabilities in CMPs, HIT, and QI. The practices joining ACOs also had greater prior experience with private-sector pay-for-performance programs containing some of the same performance metrics and incentives as those associated with the Medicare ACO programs. But although such practices may have more such capabilities and prior experience with value-based payment programs,22,23 there is mixed evidence to suggest that they are yet associated with better performance.3,4,12,24-26 It is important to recognize that most such practices have been operating in a largely risk-free fee-for-service world in which the capabilities and infrastructure to provide care more efficiently and effectively have not been needed. Given the results presented, it is likely that most practices lack the capabilities needed to succeed under new risk-based payment models. Those practices already doing well, or that at least believe that they have the QI, HIT, and CMP capabilities to do so, have the opportunity to get paid for continuing to do well. Those practices that have fewer resources to invest in such capabilities are likely to fall further behind, potentially exacerbating possible disparities in patient care. Thus, “catch-up” policies and practices may be needed, including technical assistance programs, such as the CMS Transforming Physician Practice Initiative; consolidation of practices or ownership by hospitals to create or gain access to greater resources; and partnerships with insurers and nonprovider organizations that provide management and infrastructure support services.27 Recent research suggests that physicians themselves participating in Medicare ACOs are only moderately convinced that ACOs are an effective model for delivering cost-effective care,28 despite some evidence that physician-owned practices are more financially successful.3,4 Those practices entering into ACO arrangements today and in the future may require an even stronger set of capabilities and face an even steeper learning curve to be successful under the newly evolving value-based payment arrangements.29
Although there were small differences between practices that responded to the NSPO survey versus those that did not,22 it is possible that unmeasured differences existed between the 2 groups that could result in biased estimates of the variables associated with those practices that intended to join an ACO versus those that did not. We also did not capture all practices in 2012 that intended to apply to become a Medicare ACO. Further, although we have key informant survey measures of each practice’s use of CMPs, HIT functionality, and QI processes, we do not have fine-grained measures of the quality or consistency of implementation of these processes, which are better captured through qualitative methods. Finally, we did not have access to the follow-up performance measures of the individual physician practices that intended to join an ACO in 2012, but rely on the literature cited above3-10 indicating that overall ACO accomplishments to date have been modest.
Physician practices intending to join an ACO in 2012 at the beginning of the program had greater care management capabilities, HIT functionality, and use of QI methods; were more likely to be hospital-owned; and had more experience with value-based payment programs than those practices that were not intending to join. There were no net differences in size, types of patients seen, or other characteristics. Thus, the early-joining physician practices did have greater capabilities to manage risk and succeed under the new value-based payment model. However, despite the involvement of such practices overall, ACO performance has been modest and, to some extent, disappointing. The finding that even the more capable practices used only 50% or less of recommended care management, HIT, and QI processes may be a reason for this relative lack of success. Forthcoming APMs and MIPS value-based payment models will likely present challenges to not only more advanced physician practices but also, in particular, to those currently lacking the needed capabilities to succeed.
The research upon which this paper is based was supported by the Commonwealth Fund under grant #20150261. The authors thank David Muhlestein, PhD, JD, of Leavitt Partners for supplying information helpful to the analysis.Author Affiliations: Center for Healthcare Organizational and Innovation Research (CHOIR), School of Public Health, University of California, Berkeley (SMS, PPR), Berkeley, CA; Department of Health Research and Policy, Stanford University School of Medicine (LCB), Stanford, CA; Department of Economics, Georgia State University (MFP), Atlanta, GA; Department of Healthcare Policy and Research, Weill Cornell Medicine (LPC), New York, NY.
Source of Funding: The Commonwealth Fund, grant 20150261.
Author Disclosures: Dr Shortell is a member of the Advisory Board of the Centene Corporation. Dr Casalino is a board member of the Hospital Research and Educational Trust of the American Hospital Association and is part of the American Medical Association Committee on Professional Satisfaction. 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 (SMS, PPR, LCB, MFP, LPC); acquisition of data (SMS, PPR, LPC); analysis and interpretation of data (SMS, PPR, LCB, MFP, LPC); drafting of the manuscript (SMS, PPR); critical revision of the manuscript for important intellectual content (SMS, LCB, MFP, LPC); statistical analysis (PPR, MFP); obtaining funding (SMS); administrative, technical, or logistic support (PPR); and supervision (SMS).
Address Correspondence to: Stephen M. Shortell, PhD, MPH, MBA, University of California, Berkeley School of Public Health, 2121 Berkeley Way, Room 5317, Berkeley, CA 94720. Email: firstname.lastname@example.org.REFERENCES
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