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The American Journal of Managed Care December 2019
Clinical Characteristics and Treatment Patterns Among US Patients With HIV
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Evan K. Perrault, PhD; Katie J. Schmitz, BA; Grace M. Hildenbrand, MA; and Seth P. McCullock, MA
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Wendy Yi Xu, PhD; Bryan E. Dowd, PhD; Macarius M. Donneyong, PhD; Yiting Li, PhD; and Sheldon M. Retchin, MD, MSPH
Benzodiazepine and Unhealthy Alcohol Use Among Adult Outpatients
Matthew E. Hirschtritt, MD, MPH; Vanessa A. Palzes, MPH; Andrea H. Kline-Simon, MS; Kurt Kroenke, MD; Cynthia I. Campbell, PhD, MPH; and Stacy A. Sterling, DrPH, MSW
Catheter Management After Benign Transurethral Prostate Surgery: RAND/UCLA Appropriateness Criteria
Ted A. Skolarus, MD, MPH; Casey A. Dauw, MD; Karen E. Fowler, MPH; Jason D. Mann, MSA; Steven J. Bernstein, MD, MPH; and Jennifer Meddings, MD, MS
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Heidi C. Waters, PhD, MBA; Charles Ruetsch, PhD; and Joseph Tkacz, MS
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Delivery System Performance as Financial Risk Varies
Joseph P. Newhouse, PhD; Mary Price, MA; John Hsu, MD, MBA; Bruce Landon, MD, MBA; and J. Michael McWilliams, MD, PhD
Outcome Measures for Oncology Alternative Payment Models: Practical Considerations and Recommendations
Jakub P. Hlávka, PhD; Pei-Jung Lin, PhD; and Peter J. Neumann, ScD

Delivery System Performance as Financial Risk Varies

Joseph P. Newhouse, PhD; Mary Price, MA; John Hsu, MD, MBA; Bruce Landon, MD, MBA; and J. Michael McWilliams, MD, PhD
One delivery system’s healthcare utilization in its Medicare Advantage product was notably less than in its Pioneer accountable care organization or in a traditional Medicare comparison group.
Commercial. Actual attribution was prospective and similar to Medicare, but we used retrospective attribution to analyze the data for the same reasons as with the Medicare sample. We risk adjusted commercial spending using HHS-HCCs, V0314.127.L1,10 and estimated equations for the ACO and comparison groups similar to the Medicare equation shown previously. The HHS-HCC model uses concurrent diagnoses with a separate model for each metal level in the exchange. We used the model for the Gold plan because its actuarial value is close to that of the actual plan and, as in the Medicare case, centered the predicted values at the mean risk score. We did not have firm identifiers, so we could not include firm fixed effects. Thus, there may be some modest bias to the degree that the penetration of Banner differs by firm.

We disaggregated total spending and use into inpatient, E&M, ED, and other outpatient spending. Like the Medicare analysis, we do not have data on drug spending other than drugs covered by the medical benefit. Because our data set included a flag from the plan for attribution, which was based on the past year’s use, we compared stability of attribution in using prospective and retrospective attribution.

RESULTS

After inverse probability weighting, the age–sex groups were well balanced (eAppendix Table 1).

Medicare

Figure 1 [A-D] shows risk-adjusted utilization rates of various medical services in the MA, ACO, and TM comparison group among those with positive use. Although the percentage of users in MA was greater than in the 2 TM groups, as noted previously, MA hospitalization rates were below those of the ACO and the TM comparison groups in all years (Figure 1 [A]). The differences between the hospitalization rate in the MA group versus the ACO and TM groups steadily narrowed over time, but the MA rate remained about 10% below the rates of the other 2 groups in 2014, the final year of observation.

In the 2-year pre-ACO period, the hospitalization rate in the ACO and TM groups had parallel trends, but after the establishment of the ACO, the rate in the ACO group fell at a more rapid rate (Figure 1 [A]). In 2010, the rate of skilled nursing facility (SNF) days in both the ACO and TM groups was about twice that of the MA plan rate, but the MA rate rose steadily, whereas rates in the other 2 groups fell (Figure 1 [B]). The ACO–TM comparison is difficult to interpret because pre-ACO period trends differ. Neither E&M office visit nor ED visit rates exhibited any notable trend (Figure 1 [C and D]).

Consistent with its lower use of acute and postacute services, the MA group had the lowest total risk-adjusted spending in all years (Figure 2). Nevertheless, its spending rose consistently through the 5-year period, whereas spending in the TM and ACO groups did not vary nearly as much. By 2014, spending in the MA group had converged toward that of the other 2 groups; however, it remained 10% below that of the 2 groups, and the difference was larger in the first 2 years of the ACO.

Spending in the Medicare ACO cohort was slightly higher in the pre-ACO period than in the TM comparison group, and in 2012—the first year of the ACO—it ticked marginally up. It then fell to the same level as the comparison group in 2013 and 2014. eAppendix Figures 1 through 4 show corresponding spending data on specific services for the ACO and TM comparison groups.

DID results that compare averages for the 2 pre-ACO years with the 3 post-ACO years are shown in eAppendix Tables 2 and 3. These results add no new insights to the results just described. Unadjusted rates are shown in eAppendix Figures 5 through 9.

Commercial

The risk-adjusted data show that total cost in the ACO group rose at the same rate as in the comparison group in the pre-ACO period. However, in 2012—the first year of the ACO—costs rose in the commercial ACO relative to the comparison group but thereafter fell at a faster rate than in the comparison group, such that by 2014—the third year of the ACO—costs were approximately equal (Figure 3 [A]). This result is mainly driven by the experience with inpatient costs and, to a much lesser degree, by outpatient non-E&M costs (Figure 3 [B-E]). Differences in other types of costs are small. Unadjusted commercial rates of utilization and spending on these services are shown in eAppendix Figures 10 through 14. DID results for the commercial group are shown in eAppendix Table 4. Like the Medicare DID results, these shed no new light.

We also assessed the proportion of commercially insured individuals assigned to the ACO using retrospective attribution who would also have been assigned using prospective attribution. For Banner, these values were a little more than 40% in 2013 and 2014; for non-Banner physicians, the values were a little more than 60%. The non-Banner values are higher in part because individuals attributed to a given non-Banner physician in 2013 and a physician in another non-Banner group in 2014 both count as being attributed to a non-Banner physician, whereas an individual had to remain within Banner in both years to be attributed to Banner. Both these values are well below the 80% value for Medicare beneficiaries because of the churn among employers in commercial insurance that does not occur among the Medicare population.8


 
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