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Continue Logout“What makes an ACO successful—especially MSSP E track?” That’s the question a Director of Population Health recently asked me.
Previous ACO research suggests success factors include being physician-led, duration of beneficiary attribution, site of care shifts from IP to OP and simply taking risk. Other research concludes the size of the total medical expense budget and beneficiary demographic attributes, particularly regarding disability, play an important role in ACO success. A recent study on ACO financial performance, Promise v. Practice: The Actual Financial Performance of Accountable Care Organizations, concludes that on balance the Medicare ACO program at best was financially neutral. Based on the aggregate data it would be unwise to disagree. At the same time some ACOs appear to perform very well financially in terms of not only generating savings for the ACO but also in lower beneficiary expenses.
With the existing research in mind, we conducted our own analysis to answer the question. Based on MSSP ACO performance data, what made an ACO more likely to be successful?
We took a cautious approach when answering the question using the MSSP ACO public use files. Within the MSSP program, the “glide path” for various levels of risk in the Basic ACO tracks still has two tracks (A and B) with no downside risk. Three tracks (C, D and E) have progressively increasing levels of downside risk. There is also an Enhanced track with both a greater reward potential and more downside risk. Given this delineation in risk-levels, we ran a cohort analysis based on the amount of risk the ACO took. The two cohorts consisted of the non-risk bearing ACOs in tracks A and B (n=190) and risk bearing ACOs in tracks C, D, and E plus Enhanced (n=164).
The performance data for all MSSP ACOS in CY2019 has two Public Use Files (PUF), one from July and the updated PUF from January 2020. The July PUF contains data not published in the January 2020 PUF, particularly reporting on some quality measures (see greater detail in the results section). We used the January 2020 PUF for the majority of the analysis. We used ANOVA to detect mean differences between the cohorts.
While our analysis is specific to Medicare ACOs in a single performance year (2019), the three indicators of ACO success we identified are consistent with the findings of the previous research cited above. This lends some validity to the idea of consistency over time. The findings discussed below strongly suggest these same factors play an even more important role in full risk programs, like global budgets.
We tested the two ACO cohorts to ensure their comparability and found they exhibited only minor differences in their beneficiary populations. In terms of demographic differences, the non-risk taking ACOs had fewer Hispanic patients than risk taking ACOs (p=.028) but none of the other demographic differences concerning age, gender and race were significant. Some differences also existed in HCC risk scores by beneficiary status.
| Variable | Non-Risk Taking | Risk Taking | p-Value |
|---|---|---|---|
| HCC risk BY3 ESRD | 1.006 | 1.018 | .027 |
| HCC risk BY3 AGDU | .998 | 1.021 | .021 |
| HCC risk BY3 ADND | 1.013 | 1.049 | .001 |
| HCC risk PY ESRD | .996 | 1.012 | .002 |
| HCC risk PY AGDU | .997 | 1.025 | .011 |
| HCC risk PY AGND | 1.013 | 1.062 | .001 |
On average, risk taking ACOs generated savings of roughly $5M more than non-risk taking ACOs ($11.72M v. $6.65M). This translated into earned savings of $7.33M v. $2.82M for risk taking and non-risk taking ACOs, respectively, despite the smaller number of risk taking ACOs.
The number of beneficiaries and total expense were not statistically different between the two ACO types. If you consider the MedPac estimate of 2% of beneficiary expense in added overhead to operate an ACO, the $5M difference in earned savings becomes quite significant.
It's clear. Whatever happens in the ACO as a result of the act of taking risk--whether there are structural or process changes--risk taking ACOs save more money than non-risk taking ACOs.
Reading between the lines, however, strongly suggests that entities working in capitated or global budget arrangements might have even more opportunities for financial returns because the issue of demand destruction in full-risk v. largely FFS with some downside risk environments is different.
If an ACO is going to generate savings it has to spend less than the budget. The math is simple. Since ACOs use existing Medicare payment methodologies under Parts A and B, and these are largely prospective payment systems, generating savings may well mean the providers in the ACO are capturing less volume-driven revenue. The difference in that demand destruction between risk taking and non-risk taking ACOs was $124 per capita. In other words, risk taking ACOs “destroyed” $124 more in revenue per beneficiary than non-risk taking ACOs.
Consider an “average” ACO with 20,000 attributed beneficiaries and a total medical expense of $20M. If the risk taking ACOs generated on average $7.33M in shared savings but spent $4M to operate the ACO and then reduced per beneficiary expenses by $124 more than non-risk ACOs, the total of $6.2M between those factors essentially leaves the ACO with a $1M surplus.
Our findings mean the number one driver of ACO success is committing to risk—taking meaningful, downside risk is more likely to drive financial success than spending more time on the “glide path” while dabbling in upside risk.
Our univariate analysis revealed some significant differences between the two ACO types in terms of types of expenses and visits. When it comes to broad expense categories, the risk taking ACOs spent less on IP and OP (defined as HOPD) and more on physician services:
| Expense Category (per capita) | Non-Risk Taking | Risk Taking | p-Value |
|---|---|---|---|
Total Inpatient expenditures | 3,008 | 2,862 | .014 |
Short term acute hospital expenditures | 2,626 | 2,476 | .005 |
Outpatient expenditures | 2,544 | 2,182 | .001 |
Physician/supplier expenditures | 3,341 | 3,503 | .038 |
Some of the variation in these expense categories may be attributable to univariate differences in broad encounter types. The risk taking ACOs had fewer “expensive” visit types but more PCP E&M encounters:
| Visit Category (per 1,000 beneficiaries) | Non-Risk Taking | Risk Taking | p-Value |
|---|---|---|---|
COPD admissions | 4.64 | 4.25 | .031 |
Outpatient ED | 594 | 555 | .001 |
CT Events | 656 | 636 | .039 |
PCP E&M | 3,772 | 4,210 | .004 |
FQHC/RHC | 609 | 342 | .011 |
In the performance data published in July 2019, risk taking ACOs had a statistically lower number of all-cause unplanned admissions. Medicare did not publish this metric in the January 2020 PUF, updated August 2021. However, the difference on this measure between the ACO types is consistent with the differences in expense and visit categories in the January 2020 PUF.
This finding is consistent with previous research: successful ACOs manage patient care in lower cost settings.
The two ACO types were not statistically different in terms of PCPs and specialists. The overall quality scores were not statistically different either. However, the risk taking ACOs statistically outperformed the non-risk taking positively on the following quality measures:
If the better performance of the risk taking ACOs on these quality measures does not scream primary care, then it is difficult to know what else would. The statistical difference in the mid-year file regarding all-cause unplanned admissions highlights the impact of primary care. Our findings suggest it’s not that successful ACOs have more PCPs. Rather, successful ACOs measure, document, and manage care better in primary care than their counterparts.
Commitment. Our three big findings answer my colleague’s question about what makes an MSSP track E ACO successful – at least in the 2019 performance year. In our analysis, committing to downside risk and committing to provide care at lower cost settings associated to greater savings. It would be reasonable to ask, since MSSP ACOs typically do not start by taking risk, whether it is taking risk that contributes to savings or whether it is savings in other models that contributes to deciding to take risk. These results can’t answer the question of which came first. At the same time the act of committing to risk-based payment is the cornerstone of the other success factors we found.
Some caution is necessary. Looking at these same success indicators through a different analysis with a multivariate lens only explains half of the variation between risk taking and non-risk taking ACOs when it comes to shared savings. This suggests other factors are at play, whether they are ownership models, number of hospitals, HCC risk score differences, or other measurements. And those as yet unknown chapters are in no small part why the VBP book is still in development.
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