David J. Nyweide, PhD

 Understanding variation in spending across organizations, rather than across geographic areas, is important because care is delivered by organizations and interventions increasingly focus on organizations. Accountable care organizations (ACOs) are particularly important to study given their incentives to reduce spending. Analyzing spending differences across ACOs may help identify cost savings opportunities.

 Cross-sectional analysis of Medicare claims.

 We stratified ACOs into quartiles based on the deviation between each ACO’s risk-adjusted spending and average risk-adjusted fee-for-service spending in the same market (hospital referral region). We compared spending between top- and bottom-quartile ACOs on each of 7 major service categories and 10 clinical condition groups to identify areas of potential savings. We simulated spending reductions if ACOs with high adjusted spending reduced spending to the levels of lower-spending ACOs.

 In 2016, geographically adjusted and risk-adjusted total per-beneficiary spending for the highest-spending quartile of ACOs was 14% higher than for ACOs in the lowest quartile. Variation between high- and low-spending ACOs was greatest, at 27%, in the use of skilled nursing facilities—a service category in which ACOs have reduced spending by the greatest percentage. Inpatient care was the largest driver of absolute dollar differences in spending, however, accounting for 37% of the total spread. If spending in ACOs above median adjusted spending were brought down to the median, savings would be 3% to 4%.

 By extending the variations literature to focus on ACOs, we illustrated that meaningful further savings opportunities exist both within and across markets.

There was a 14% difference in risk-adjusted total spending between accountable care organizations (ACOs) in the highest and lowest quartiles of spending relative to local fee-for-service Medicare spending, suggesting meaningful opportunities for savings even after 4 years of the ACO program.

If spending in ACOs with above-median adjusted spending were brought down to the median, savings would be 3% to 4%.

Spending variation by service category suggests savings opportunities in inpatient, postacute, and home health care. Variation in spending for patients by select condition categories indicates substantial savings opportunities in aggregate for prevalent chronic conditions, but the magnitude of variation in dollars per patient is small, suggesting that interventions must be low-cost on a per-beneficiary basis to achieve net savings.

Variation in healthcare spending and utilization exists at every level of the healthcare system, ranging from states to individual physicians within a given practice.

 Yet most of the research characterizing variation in healthcare spending has been focused at the geographic level, as exemplified by the Dartmouth Atlas. Results of geographic analyses argue convincingly that a great deal of spending variation across areas is not connected to patient morbidity or outcomes.

 However, considerable differences in spending exist even within geographic areas,

 and geographic areas are not a natural unit for actionable interventions to reduce spending variation.

 The National Academy of Medicine’s 2013 report on spending variation emphasized the importance of extending analyses of practice pattern variation to “the unit of decision-making,” such as individual providers or provider systems.

Evidence characterizing organizational variation remains relatively limited. Studies of organizational variation in Medicare spending typically focus on a specific clinical context, like outpatient cardiology or oncology practices, or examinations of specific services, like low-value care.

 Evidence from the commercial sector focuses on variation in price, rather than total spending, and generally looks at variation across hospitals rather than organizations such as accountable care organizations (ACOs) that are responsible for population-level spending.

 As in the geographic variation literature, research examining practice pattern variation in provider organizations reveals large and unexplained differences, but none of that work examines variation in total spending at the population level.

ACOs are provider groups that agree to take accountability for spending and quality of care for patients in the ACO contract. ACOs represent an important unit of observation because they are CMS’ most important alternative payment model and the foundation of many current efforts to reduce wasteful medical spending. Little research exists on spending variation in ACOs: One early study reported considerable variation in ACO spending, even after controlling for regional variation and case mix, but did not delve into sources of variation.

 Breaking down sources of spending variation among ACOs could inform where organizations might intervene to reduce avoidable spending.

We examined the deviation between each 2016 ACO’s risk-adjusted spending and average risk-adjusted spending in its market, then stratified ACOs into quartiles based on these deviations. We defined top-quartile ACOs—those with the largest positive deviations, in dollars, from adjusted local market fee-for-service (FFS) spending&mdash;as high spending (quartile 4 [Q4]). We defined bottom-quartile ACOs&mdash;those with the largest negative deviations&mdash;as low spending (quartile 1 [Q1]). We compared spending between Q1 and Q4 ACOs on each of 7 major service categories and 10 clinical condition groups to identify areas of potential savings. Finally, we simulated spending reductions if ACOs with high adjusted spending (within their market or nationally) reduced spending to the levels of lower-spending ACOs.

We analyzed a 100% sample of FFS Medicare beneficiaries, refined to include only beneficiaries continuously enrolled in parts A and B for the full year (or until death) and continuously enrolled in the prior year. We further limited the sample to beneficiaries potentially eligible for enrollment in an ACO: individuals with at least 1 qualifying ambulatory evaluation and management visit in the year of interest.

We analyzed ACOs participating in the Medicare Shared Savings Program (MSSP), the largest of Medicare’s ACO initiatives. We assigned beneficiaries to ACOs using the provider identifiers (taxpayer identification numbers [TINs] and CMS Certification Numbers [CCNs]) defining each ACO in the ACO SSP Provider-Level Research Identifiable File for that year. We assigned beneficiaries to an ACO if it accounted for a larger share of qualifying office visits with a primary care physician than any other ACO or non-ACO TIN/CCN during the year (see 

]). Patients assigned to non-ACO TINs served as FFS controls. As in the MSSP, we used retrospective assignment, assigning patients to ACOs based on utilization in the year of interest.

Our sample included 432 ACOs in 2016. (There were 220 in 2013, 333 in 2014, and 387 in 2015, and 145 ACOs were present all 4 years.) For ACOs present in multiple years, the composition of TINs or CCNs included in the ACO may have changed over time, which we addressed in sensitivity analyses.

Our outcomes of interest were total annual per-beneficiary Part A and Part B Medicare spending and annual per-beneficiary Medicare spending by service subcategory.

In all analyses, we controlled for beneficiary age, sex, race/ethnicity, Medicaid status, disability as the reason for enrollment, and end-stage renal disease. We controlled for comorbidities using prospective hierarchical condition category (HCC) diagnoses and risk scores using CMS 2014 version 12 software for all 4 years, using CMS’ 

International Classification of Diseases, Tenth Revision, Clinical Modification

International Classification of Diseases, Ninth Revision, Clinical Modification

. We identified whether beneficiaries had ever had 1 of 27 chronic conditions identified in the Chronic Conditions Warehouse (CCW). We assigned beneficiaries to a hospital referral region (HRR) based on their residential zip code.

Ranking ACOs by Adjusted Spending Deviations From Their Markets

To rank ACOs by total adjusted spending, we estimated a linear mixed-effects regression model predicting total per-beneficiary spending (eAppendix) as a function of the covariates described previously: HRR fixed effects and ACO random effects. Estimated random effects represent an adjusted mean spending deviation from local market FFS spending for each ACO. In contrast to fixed-effects models, the random-effects model accounts for within-ACO variation in spending in the estimation of the between-ACO variance; without this correction (eg, in a fixed-effects model), the ACO-level variance would be overestimated. We defined an ACO’s market as the HRRs that it serves. ACOs can serve multiple HRRs, so an ACO beneficiary’s spending relative to other FFS beneficiaries in its market is implicitly estimated as a weighted average of the beneficiaries in each HRR served by the ACO. We were thus able to measure each ACO’s spending deviation relative to other ACOs and non-ACO FFS beneficiaries in the ACO’s market.

We grouped ACOs into quartiles based on their risk-adjusted spending deviation from average FFS spending in their market. We used ACOs’ total spending quartile rank in all analyses, including condition or service subcategory analyses.

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We ran a linear regression model with Q1 and Q4 quartile dummies and covariates for beneficiary demographic characteristics, case mix (HCC), and market (HRR). We subtracted the Q4 coefficient (spending attributed to high-spending ACOs) from the Q1 coefficient (spending attributed to low-spending ACOs) to calculate the spread in total spending. We focused on this “spending gap” between the Q1 and Q4 means as a useful summary of the range of the spending deviations.

We repeated the same total spending per beneficiary model described previously for each service category, changing only the dependent variable to subcategory spending rather than total spending. Additionally, we calculated the percentage share of the total spending gap between quartiles attributable to each service category.

We estimated deviations for patient populations with specific conditions, using the 27 CCW conditions to create 10 CCW condition groups (eAppendix). We restricted some conditions, such as hip fracture, to diagnoses within the last year to better isolate spending associated with that diagnosis. We included chronic conditions if the patient had ever received a diagnosis. Using linear regression models with quartile dummies, condition dummies, and interactions between quartiles and conditions, we computed spread in total spending and service category spending between Q1 and Q4 ACOs. We determined the prevalence of conditions in Q4 ACOs to estimate potential savings in aggregate associated with each condition; prevalence was approximately evenly distributed across ACO quartiles and other FFS beneficiaries.

Simulation of Potential Spending Reductions

We simulated cumulative spending reductions in 3 scenarios to crudely measure ACO savings potential. We estimated change in total spending if all ACOs with above-median adjusted spending were able to reduce spending to the level of the median (50th percentile) ACO. We repeated this analysis for thresholds at the 25th and 10th percentiles.

Our primary analyses focused on 2016. We repeated the analyses for 2013 to 2015, as well as for ACOs present in all 4 years. We replicated the service and clinical area analyses (both quartile rankings and spending category variation models) for each year, with and without controlling for geography (HRR). We also simulated savings without HRR controls.

Our sample included approximately 26.6 million eligible beneficiaries in 2016, with 8.3 million beneficiaries attributed to ACOs. Mean ACO membership was 19,179. Adjusted mean total per-beneficiary spending in 2016 was $10,212 for low-spending ACOs and $10,850 for high-spending ACOs. Results for 2013 to 2015 are reported in the eAppendix.

In 2016, mean total risk-adjusted spending was 14% higher for ACOs in the highest quartile relative to those in the lowest (

). Although the relative percentage difference between high and low spending quartiles for inpatient care was only 15%, inpatient spending was the largest service category in dollars; consequently, it accounted for 37% of the absolute spread in total spending (ie, in dollars per beneficiary). Outpatient spending in the upper quartile was 22% greater than that in the lowest quartile, and, because of its large spending share, it was the second largest driver of the overall spending gap, accounting for 32% of the spread in total spending.

In all years, variation on a percentage basis between Q1 and Q4 ACOs was greatest in skilled nursing facility (SNF) and home health. However, because SNF and home health spending accounted for a smaller share of spending than inpatient and outpatient spending, they account for a smaller share of the overall gap in spending.

 summarizes spending and prevalence by condition group for high- and low-spending ACOs. Taking patients with chronic kidney disease (CKD) as an example, the mean risk-adjusted difference in spending for Q4 is $1372 higher than spending for Q1 ACOs. Prevalence of CKD in Q4 ACOs was 22%. If Q4 ACOs reduced incremental spending for patients with CKD to that of Q1 ACOs, Q4 ACOs could achieve cost savings of $302 per beneficiary across the entire ACO population (ie, including people with and without CKD).

We observed substantial spending variation across all condition groups. In general, acute or newly diagnosed CCW condition groups tended to have a high dollar difference, driven by higher mean spending, in per-beneficiary spending between Q1 and Q4 ACOs, but low prevalence. Chronic condition groups tended to have smaller per-beneficiary dollar differences in spending for patients with those diagnoses but high prevalence, and thus they were responsible for greater absolute spending differences between high- and low-performing ACOs when considered over the full ACO population.

If all beneficiaries in ACOs with geographically adjusted and risk-adjusted spending higher than the median ACO were brought down to that threshold, savings would be 3.6% (

Without adjusting for HRR, the between-quartile spread in total risk-adjusted spending deviations for the top and bottom quartiles of ACOs nationally was 10 percentage points larger than the models adjusting for HRR (eAppendix). Our primary analysis is thus conservative in that it assumes that ACOs are more readily able to reduce spending relative to their markets than relative to national benchmarks. On a national basis, if beneficiaries in ACOs above median national ACO spending (adjusted for case mix but not geography) were brought down to that threshold, savings would be 9.2%.

Our results were largely insensitive to the year of analysis (eAppendix): The gap between Q1 and Q4 ACOs in 2016 was $1409 (14% of mean per-beneficiary spending). The analogous figures for 2013, 2014, and 2015 were $1328 (14%), $1364 (14%), and $1475 (15%), respectively. The services contributing to this variation were largely unchanged. ACO quartile rankings were largely stable over time; only 6 ACOs had a year in the highest quartile and a year in the lowest.

This analysis suggests meaningful variation in ACO spending within markets and overall. Despite widespread recognition that high spending in the United States, particularly in the commercial sector, reflects high prices, our savings estimates largely measure variation in utilization because Medicare prices are set administratively and vary much less than commercial prices.

 Thus, our analysis can broadly identify areas of savings opportunity related to utilization. Adjusted within-market spread between Q1 and Q4 ACOs was 14% in 2016, suggesting meaningful opportunities for savings even after 4 years of ACO adoption.

Our analysis suggests that key savings opportunities exist in the inpatient, SNF, and home health care settings, consistent with ACO evaluations reporting spending reductions in these areas.

 ACOs may find savings by changing the site of care (eg, shifting from hospital outpatient department to office settings), which is consistent with observed evidence on ACO savings.

Research involving ACO executives suggests that they have adopted strategies including strengthened primary care, greater care management, and reductions in avoidable ED use—all with an emphasis on high-risk patients.

 However, care management strategies do not appear to be how ACOs, on average, have found savings. Prior research indicates that ACOs have not differentially reduced admissions for ambulatory care—sensitive conditions, nor have reductions in hospitalizations and spending been disproportionately concentrated among high-risk patients.

 ACOs increasingly report focusing on standardizing and systematizing clinical processes.

 And, as ACOs have discovered the savings opportunities in postacute care, they have initiated strategies such as preferred SNF networks.

Spending variation in clinical groups suggests considerable aggregate savings potential among patients with high-prevalence chronic conditions, but the magnitude of spending variation per beneficiary in dollars is small, meaning that interventions aiming for net savings would need to be highly efficient on a per-patient basis. Incremental spending differences between Q1 and Q4 ACOs for people with newly diagnosed and acute conditions are sizeable in dollars per beneficiary, but these conditions are low in prevalence, meaning that aggregate savings would be modest in condition-specific efforts to control costs.

Targeted interventions to improve chronic conditions may not provide a return on investment for ACOs in the short term because per-beneficiary cost savings are modest and the costs of many interventions, such as case management, are high. Further, returns to chronic disease management may be realized in future years, and beneficiary attribution to an ACO is not guaranteed from year to year. Many popular clinical interventions may be cost-effective but not cost-saving.

 The most proximate savings may lie in interventions to reduce utilization of low-value services; this requires developing effective interventions, as existing tools to measure low-value care capture only a small fraction of it.

 Because low-value care is not necessarily confined to the high-risk group, interventions almost certainly need to be broad.

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Analysis of spending differences among accountable care organizations (ACOs) may help identify cost savings opportunities. We examined the magnitude and sources of spending variation among ACOs over 4 years.

David J. Nyweide, PhD

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