update on aco evaluations - amazon s3 · residence) in relation to the primary service area of the...

Update on ACO Evaluations

Using Mixed Methods to Better Understand ACOs

Teresa Litton, NAACOSAlex Hartzman, Dobson & DaVanzoDavid Muhlestein, Leavitt Partners

April 6, 2017 1

Agenda

Update on ACO Evaluations: Using Mixed Methods to Better Understand ACOs through:

• Claims data• NAACOS Claims Database • Presented by Alex Hartzman, Dobson & DaVanzo

• Survey data• NAACOS/Leavitt Partners Annual ACO Survey• Presented by Teresa Litton, NAACOS, and David

Muhlestein, Leavitt Partners

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NAACOS Claims Database

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The NAACOS Claims database represents ~24 million Medicare beneficiaries and covers three years (2012-2014) with 2015 to be added soon.

• The database includes a 100% of ACO attributed beneficiaries, ~4-6 million, and 83% of unattributed but eligible beneficiaries (non-ACO sample), ~18-20 million, for comparative analysis.


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Supporting NAACOS in this large endeavor is the health care consulting firm, Dobson DaVanzo & Associates, LLC.

Alex Hartzman, Senior Manager, is the lead consultant working on the NAACOS research projects.

Dobson DaVanzo & Associates, LLC Vienna, VA 703.260.1760 www.dobsondavanzo.com

ACO Analytics Projects and FindingsPREPARED FOR:NAACOS Spring Conference, 2017

PREPARED BY:Al Dobson, Ph.D., Alex Hartzman, M.P.A, M.P.H., Sung Kim, Kimberly Rhodes, M.A., Sarmistha Pal, Ph.D., Joan DaVanzo, Ph.D., M.S.W.

PRESENTED BY:Alex Hartzman

April 6, 2017

• NAACOS-Dobson DaVanzo collaboration• Research questions addressed to date• Lessons learned to date• Future directions

Presentation overview

© 2017 Dobson DaVanzo & Associates, LLC. All Rights Reserved.

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• Dual purpose: • Develop data-driven analyses to inform NAACOS policy efforts• Provide data-based tools to support individual NAACOS members

• Approach: • Questions and study concepts from NAACOS staff, Analytics and Quality

Advisory Panels, Board, individual ACOs, research community• Use all available data sources

• Public data such as the ACO Public Use Files • Survey data, such as the 2016 NAACOS Member Survey• Data directly from ACOs• NAACOS-Dobson DaVanzo custom claims database (including Research

Identifiable (RIF) Files)

NAACOS-Dobson DaVanzo collaboration


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• 2012 - 2014, with 2015 arriving soon, allowing us to construct patient episodes over time

• Two ACO population cohorts of interest:1. Medicare beneficiaries who are ACO-

attributed2. Medicare beneficiaries who are ACO-

eligible, but unattributed• Our unique claims database uses ACO-

eligible, unattributed beneficiaries as a comparison instead of total Medicare beneficiaries to ensure that the comparison group is similar to the study group of ACO-attributed beneficiaries

• Our database also contains post-acute care assessment data, allowing us to measure beneficiaries’ outcomes and utilization across the care continuum

NAACOS-Dobson DaVanzo custom claims database: ACO-attributed & unattributed beneficiaries


ACO-Attributed

Beneficiaries

ACO-Eligible, Unattributed Beneficiaries

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• Addressing quick turnaround policy questions• ACO exposure maps• B-CAPA comparative analyses• Analyses used in NAACOS reports, publications and

policy positions

Activities to date


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• How has the Medicare Shared Savings Program grown?• Do ACO actions affect their likelihood of earning shared

savings or is it circumstance?• Are ACOs changing post-acute care utilization patterns

and what patterns are associated with success and shared savings?

• How are ACOs investing and how is this related to performance?

Research questions addressed to date


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How has the Medicare Shared Savings Program grown?


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Approximately 7% of US counties had >20% of their ACO-eligible beneficiaries attributed to an ACO in 2012*

Notes: *2012 VRDC RIF data did not contain ACO-attribution information. We used a proxy which resulted in 1.9m ACO-attributed beneficiaries, although we know from CMS that there were 1.2m beneficiaries that year. - Percent calculation: # of ACO-attributed beneficiaries from VRDC dataset / # of ACO-eligible beneficiaries, factored up from VRDC dataset sample**. **Because this data comes from our VRDC sample of ACO-eligible beneficiaries, we factored it up to account for all ACO-eligible beneficiaries. To do this, we divided the VRDC total eligible beneficiaries in each county by a factor of .86397765. This factor was derived by dividing our VRDC sample total of ACO-eligible beneficiaries (24.2m) by the NPRM total number of ACO-eligible beneficiaries (28.0m). The NPRM total number of ACO-eligible beneficiaries was derived from 27.2m beneficiary-years, which we factored up to 28.0m beneficiaries. - In 2012, there were 3,220 counties in the US (3,142 in US + 78 in PR). This chart and table contain data for 3,208 counties - 12 counties are missing from our dataset.

Source: Dobson|DaVanzo analysis of 2012 VRDC RIF data, DUA #28643.

0%

10%

35%42%

6% 3% 2% 1% 0% 0% 0% 0% 0%0%5%

10%15%20%25%30%35%40%45%50%

Distribution of Counties by Percent: ACO-Attributed Beneficiaries divided by all

ACO-Eligible Beneficiaries, 2012

County Exposure to ACOs, 2012

• Darker shading (green) represents a greater portion of ACO-eligible beneficiaries attributed to an ACO

• White and gray shading represent counties with a very small number or no ACO-attributed beneficiaries

• The shaded county is the area of residence on file for the beneficiary


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Approximately 15% of US counties had >20% of their ACO-eligible beneficiaries attributed to an ACO in 2013


0% 2%

20%

49%

13%7% 4% 2% 1% 0% 0% 0% 0%

0%5%

10%15%20%25%30%35%40%45%50%







Notes: - Percent calculation: # of ACO-attributed beneficiaries from VRDC dataset / # of ACO-eligible beneficiaries, factored up from VRDC dataset sample*. *Because this data comes from our VRDC sample of ACO-eligible beneficiaries, we factored it up to account for all ACO-eligible beneficiaries. To do this, we divided the VRDC total eligible beneficiaries in each county by a factor of .86397765. This factor was derived by dividing our VRDC sample total of ACO-eligible beneficiaries (24.2m) by the NPRM total number of ACO-eligible beneficiaries (28.0m). The NPRM total number of ACO-eligible beneficiaries was derived from 27.2m beneficiary-years, which we factored up to 28.0m beneficiaries. - In 2013, there were 3,220 counties in the US (3,142 in US + 78 in PR). This chart and table contain data for 3,208 counties - 12 counties are missing from our dataset.


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Approximately 25% of US counties had >20% of their ACO-eligible beneficiaries attributed to an ACO in 2014


0%1%

12%

47%

15%11%6%4%2%1%0%0%0%

0%5%

10%15%20%25%30%35%40%45%50%







Notes: - Percent calculation: # of ACO-attributed beneficiaries from VRDC dataset / # of ACO-eligible beneficiaries, factored up from VRDC dataset sample*. *Because this data comes from our VRDC sample of ACO-eligible beneficiaries, we factored it up to account for all ACO-eligible beneficiaries. To do this, we divided the VRDC total eligible beneficiaries in each county by a factor of .86397765. This factor was derived by dividing our VRDC sample total of ACO-eligible beneficiaries (24.2m) by the NPRM total number of ACO-eligible beneficiaries (28.0m). The NPRM total number of ACO-eligible beneficiaries was derived from 27.2m beneficiary-years, which we factored up to 28.0m beneficiaries. - In 2014, there were 3,220 counties in the US (3,142 in US + 78 in PR). This chart and table contain data for 3,208 counties - 12 counties are missing from our dataset.


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• As of 2014, ACOs have attributed beneficiaries in almost every county in the US

• We also studied where attributed beneficiaries live (primary residence) in relation to the primary service area of the ACO• ACOs tend to have about 2.5-5% of their attributed

beneficiaries with residences outside the primary geographic reach of the ACO

• Some counties have beneficiaries attributed to many ACOs (e.g. Palm Beach County, FL has beneficiaries attributed to 250 ACOs from across the country)

• This gives population health a “new” meaning. How are these patients managed?

More counties are getting more exposure to ACOs each year

Source: Dobson|DaVanzo analysis of 2012-2014 VRDC RIF data, DUA #28643.


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Do ACO actions affect their likelihood of earning shared

savings or is it circumstance?


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• Using the ACO Public Use Files, we ran a multivariate logistic regression model with pooled panel data

• We found that patient population characteristics, ACO characteristics and benchmark levels had a statistically significant effect on probability of earning shared savings: • % of disabled beneficiaries• % of beneficiaries 85+• % of beneficiaries 75-84• % of beneficiaries 65-74• PMPY benchmark expenditures• Hybrid Owned ACOs• ACOs with 2012 or 2013 start dates

ACO success – is it circumstance?

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• Who you are as an ACO – your patient population, organizational characteristics, and benchmark – has some bearing on whether or not you earn shared savings

• HOWEVER, these traits do not fully explain or predict why an ACO might save, leaving:• ACO care coordination and management• Changes in ACO service areas which benefit the ACO• Other factors not yet accounted for

ACO success – is it circumstance? (continued)

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Are ACOs changing post-acute care utilization patterns and what patterns are associated

with success and shared savings?


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• Post-Acute Care (PAC) is the care a patient receives upon being discharged from a hospital stay – common practice and clinically useful for restorative care and other types of long-term care

• 4 settings:• Home Health Agencies (HHAs)• Skilled Nursing Facilities (SNFs)• Inpatient Rehabilitation Facilities (IRFs)• Long-Term Care Hospitals (LTCHs)

• PAC is an expensive phase of care accounting for ~15-30% of an ACO’s expenditures• Some settings provide hospital-level care or have physicians on call 24/7• Each PAC setting also has its own unique payment system with different levers

• Other Alternative Payment Models (APMs) – such as Bundled Payments for Care Improvement (BPCI) Initiative – have found a pathway to programmatic savings by decreasing PAC spending and volume

ACO post-acute care trends


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ACOs which consistently

overspent their benchmark

ACOs which consistently

earned shared savings

Averaged Combined PAC (LTCH, IRF, SNF, and HHA) Expenditures per Beneficiary Year

4% -3%

Average HHA Expenditures per Beneficiary Year 11% 1%

Average SNF Expenditures per Beneficiary Year 0% -7%

Average IRF Expenditures per Beneficiary Year 11% 11%

Average LTCH Expenditures per Beneficiary Year 0% -19%

• ACOs which are consistently earning shared savings have decreased PAC spending by ~3% over 3 years, with the most substantial reductions in SNF and LTCH spending

• But is it aggregate volume or a change in PAC services provided?• Not shown: hospital admissions rose 2013-2014 and fell 2014-2015

2013-2015 Percent Change in spending by PAC Setting

Publicly available data: ACO post-acute care trends


Source: Dobson|DaVanzo analysis of 2013-2015 ACO Public Use Files

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• Distribution of discharge destination did not differ substantially between ACOs and their markets for the other three PAC settings over this period

NAACOS-Dobson DaVanzo custom claims database: Reductions in discharges to SNFs



1 0 0 1 5

32

93

63

19

00 0 0 0 0 2

45

150

125

0

20

40

60

80

100

120

140

160

-20 to -17 >-17 to -14 >-14 to -11 >-11 to -9 >-9 to -6 >-6 to -3 >-3 to 0 >0 to 3 >3 to 6 >6 to 9

Num

ber o

f ACO

s

Distribution of ACOs by Percentage Point Change in the Share of Beneficiary Discharges to SNFs by Beneficiary ACO Attribution Status, 2013 to 2014

Percentage Point Change in the Share of Attributed Beneficiary Discharges to SNFs

Percentage Point Change in the Share of Unattributed Beneficiary Discharges to SNFs

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There is a negative relationship between higher proportions of ACO beneficiary discharges to SNFs and ACO savings rate, share of discharges to HHAs, IRFs, LTCHs, and Community



Note: ACOs from performance years 2013 and 2014 were included; each circle represents an ACO

More SNF ~ Less Savings

More SNF ~ Less HHA

More SNF ~ Less IRF

Less SNF ~ More Community

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Decreases in the share of ACO beneficiaries discharged to SNFs from 2013 to 2014 were associated with increases in ACO savings rate and the share of ACO beneficiaries discharged to HHAs and the community



Reduced SNF ~ Increased HHA

Reduced SNF ~ Increased IRF

Reduced SNF ~ Increased Community

Reduced SNF ~ Increased Savings Rate

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• ACOs appeared to decrease the share of beneficiaries discharged to SNFs from 2013 to 2014• HHAs and community discharge (with or without outpatient therapy) appear

to have been the primary substitutes for SNF care, though some may have also gone to IRFs

• LTCH volume trends were harder to observe in data• LTCH care is expensive but rarely used, so decreases in cost within episodes or

relatively subtle changes in distribution can have a substantial impact on overall PMPY LTCH spending while having a minor impact on PMPY total PAC spending

• There are no clinically appropriate alternatives for some LTCH-provided services such as ventilator weening, so substitution of service settings is less common than we see in SNFs

• PAC setting payment system uniqueness means that different settings will have different ways to reduce costs – e.g. length of stay is a cost-driver for SNFs, but not IRFs

• ACOs should monitor rebalancing of PAC activities for adverse patient outcomes

PAC conclusions

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How are ACOs investing and how is this related to

performance?


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• To address this question, we combined the 2016 NAACOS Member Survey with the PY3 ACO Public Use File and created 2 comparison groups:

1. ACO Success - Survey responses were segmented by ACO performance relative to benchmarks

2. ACO Quality Performance - Survey responses were also segmented by ACO quality score

• Sample Size = 102 ACOs

How are ACOs investing and how is this related to performance?

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The ACO Cost & MACRA Implementation Survey was sent to 433 MSSP ACOs

144 Unique ACOs responded (17 did not provide their ACO's name)

127 ACOs gave their ACO name along with their responses (25 could not be matched up with an ACO in the PY3 ACO Public Use File)

102 Responding ACOs were matched with an ACO in the PY3 (2015) results

ACOs that spent more than their

benchmark(N=47)

ACOs that beat their

benchmark, but did not earn

shared savings(N=26)

ACOs that earned shared

savings(N=29)

Clinical & care management costs $33.60 $29.64 $52.93

Healthcare IT, population analytics & reporting

$25.02 $32.80 $29.25

ACO management, administration, financial, legal & compliance

$22.77 $17.66 $26.48

Other operating costs $6.40 $8.08 $6.36

Total $87.78 $88.19 $115.02

Survey results by savings

• ACOs that achieved shared savings invested more heavily overall -$115 PMPY compared to $87-88 PMPY for the other groups

• ACOs that achieved shared savings spent most heavily in clinical and care management

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Reported Total Spending Converted to PMPY Spending


Source: Dobson|DaVanzo analysis of NAACOS 2016 ACO Cost and MACRA Implementation Survey and ACO MSSP 2015 (PY3) Results

ACOs with below average

(<91.44%) quality scores

(N=23)

ACOs with above average

(>91.44%) quality scores

(N=64)

ACOs without quality scores

(pay for reporting)

(N=15)

Clinical & care management costs $24.86 $41.70 $38.77

Healthcare IT, population analytics & reporting

$27.54 $23.99 $47.11

ACO management, administration, financial, legal & compliance

$13.52 $23.01 $34.73

Other operating costs $6.62 $6.63 $7.57

Total $72.54 $95.33 $128.18

Survey results by quality score• New ACOs (pay for reporting)

spend most heavily on data infrastructure and spend more on ACO management than other groups

• ACOs with below average quality scores invested the least overall at $73 PMPY compared with $95 and $128 PMPY for high quality scores and P4R, respectively

• ACOs with below average quality scores invested least in ACO management

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Reported Total Spending Converted to PMPY Spending



ACOs that spent more than their benchmark

(N=47)

ACOs that beat their benchmark, but did not

earn shared savings(N=26)

ACOs that earned shared savings

(N=29)

1-3 years 36% 62% 61%

4-6 years 48% 23% 32%

7+ years 9% 0% 4%

Never 7% 15% 4%

• 62% of ACOs that beat their benchmark but did not earn shared savings would be willing to share losses in 1-3 years

• 61% of ACOs who earned shared savings would be willing to share losses in 1-3 years• 64% of ACOs that spent more than their benchmark responded that they would need

at least 4 years before they’d be willing to share losses

ACOs that are saving tend to be willing to share risk sooner; other ACOs need more time


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How many years before you're willing to share losses?


ACOs with below average

(<91.44%) quality scores

(N=23)

ACOs with above average

(>91.44%) quality scores (N=64)

ACOs without quality scores

(pay for reporting) (N=15)

Definitely or likely participate 24% 33% 27%

Equally likely and unlikely to participate

14% 27% 13%

Likely not or definitely not participate

62% 38% 60%

• 33% of ACOs with above average quality scores reported that they would either definitely participate or likely participate in the MSSP if they were required to share losses compared to 24% of ACOs with below average scores

• However, an even higher percentage (38%) of the high quality score group reported that they would likely not or definitely not participate

ACOs with higher quality scores were more likely to report that they would participate in the MSSP if two-sided risk were required


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How likely are you to participate in the MSSP if required to share losses?

Note: Numbers do not add to 100% due to rounding.


• 66% of ACOs with above average quality scores indicated they had at least 1 new shared risk agreement, whereas a smaller percent (39%) of ACOs with below average quality scores had at least 1 new shared risk agreement in 2015

ACOs that performed better on quality also tended to have more new shared risk agreements compared to ACOs with lesser scores

ACOs with below average (<91.44%) quality scores

(N=23)

ACOs with above average (>91.44%) quality scores

(N=64)

ACOs without quality scores (pay for reporting) (N=15)

0 61% 34% 40%1-3 30% 53% 40%4-6 9% 13% 20%


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# of new shared risk/savings agreements


• Of the sample, ACOs that earn shared savings invest differently than other ACOs

• New ACOs spend more on compliance costs and HIT than other ACOs

• ACOs earning shared savings also tend to hold different attitudes about willingness to take on two-sided risk

• ACOs that have high quality scores tend to have contracts with multiple payers

Survey conclusions

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• It is unclear what “population health” means in the context of retrospective patient attribution

• MSSP ACO shared savings potential appears to be somewhat dependent on benchmark, patient population, and organizational characteristics

• Post-acute care service mixes are changing under ACOs (as of 2014), primarily to the detriment of SNFs

• ACOs are investing heavily and the ROI looks good• Some ACOs, especially those that are not performing well on cost or

quality, may not be willing to transition to two-sided risk

Lessons learned to date


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• Comparing your ACO’s performance to other providers in your service area and to other ACOs on utilization metrics, such as:

• Number of acute hospital readmissions • Readmission rates at 30, 60, and 90 days• CHF admissions per 1000 beneficiaries • Bacteria pneumonia admissions per 1000 beneficiaries • CT events per 1000 beneficiaries • Primary care service with a PCP per 1000 beneficiaries • Cost per admission, by eligibility category• Mortality rate

• Comparing your ACO’s performance to other providers in your service area and to other ACOs on claims-based quality metrics, such as:

• Provision of preventive care• Reduction of overused services

• Gleaning insights through analyses of the 2017 NAACOS survey combined with public use data and the NAACOS-Dobson DaVanzo custom claims database

Future directions


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• Our database is unique in that we have a control group of beneficiaries who are eligible, but not attributed. With this, we can answer: • How is your ACO doing compared to the rest of your service area?

(overall PMPY savings, readmissions, admissions, ED use, PCP use, post-acute care use, etc.)

• Our database spans 3 years (with the 4th year, 2015, on the way). With this, we can answer time trend questions such as: • What impact have your initiatives had on the health care use of your

attributed population and how has this changed over time? • What “success factors” are related to ACOs earning shared savings and

how might these be applicable to other ACOs?• We can determine if some types of ACO activities are more likely to

produce success than other types of ACO activities

Future directions (continued)

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• Our database contains geographic information at the beneficiary level. With this, we can answer: • Does your proportion of far-flung attributed beneficiaries affect your

ability to control costs and quality?• Our database contains post-acute care assessments for SNF, HHA,

and IRF. With this we can convert existing PAC functional assessment scales into a single, cross-PAC scale to compare functional status of beneficiaries to answer: • How is the acuity of PAC care by PAC setting for my attributed

beneficiaries different from the acuity of other PAC patients in my service area?


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• Our database gives us the capability to follow patients and the (claims-based) quality and cost of the care they receive in an episode. With this, we can answer: • Who are the best performing post-acute care and hospital partners in my

ACO’s service area? (overall costs, readmissions, length of stay, etc.)• Our database includes information at the patient level. With this, we

can:• Build population-based estimates at the ACO, service area, and county

level to show ACO performance compared to one another as well as the non-ACO providers

• Our database contains physician identifiers. With this we can track physician performance


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• ACO-based improvements in care and chronic condition management:• What is the next wave of ACO-sponsored clinical endeavors that is

expected to improve patient care? How can we measure these activities with a claims database?

• How are you applying incentives to your participating clinicians and how do you expect this is changing care?

• If you tell us what you’re doing, we’ll tell you if it’s working compared to other ACOs with different care strategies and to other providers in your service area

Brief discussion / facilitation

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Dobson DaVanzo & Associates, LLC (Dobson|DaVanzo) is a health economics and policy consulting firm based in the

Washington, D.C. metropolitan area

Contact information:(703) 260-1760

[email protected]

Dobson|DaVanzo

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