Comparison of Approaches for Aggregating Quality Measures in Population-based Payment Models.
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ABSTRACT:
SUBMITTER: McDowell A
PROVIDER: S-EPMC6232509 | biostudies-literature | 2018 Dec
REPOSITORIES: biostudies-literature
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Comparison of Approaches for Aggregating Quality Measures in Population-based Payment Models.
McDowell Alex A Nguyen Christina A CA Chernew Michael E ME Tran Kevin N KN McWilliams J Michael JM Landon Bruce E BE Landrum Mary Beth MB
Health services research 20180822 6
<h4>Objective</h4>To assess the impact of alternative methods of aggregating individual quality measures on Accountable Care Organization (ACO) overall scores.<h4>Data source</h4>2014 quality scores for Medicare ACOs.<h4>Study design</h4>We compare ACO overall scores derived using CMS' aggregation approach to those derived using alternative approaches to grouping and weighting measures.<h4>Principal findings</h4>Alternative grouping and weighting methods based on statistical criteria produced ov ...[more]
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Project description:ImportanceHawaii Medical Service Association (HMSA), the Blue Cross Blue Shield of Hawaii, introduced Population-based Payments for Primary Care (3PC), a new capitation-based primary care payment system, in 2016. The effect of this system on quality measures has not been evaluated.ObjectiveTo evaluate whether the 3PC system was associated with changes in quality, utilization, or spending in its first year.Design, setting, and participantsObservational study using HMSA claims and clinical registry data from January 1, 2012, to December 31, 2016, and a propensity-weighted difference-in-differences method to compare 77 225 HMSA members in Hawaii attributed to 107 primary care physicians (PCPs) and 4 physician organizations participating in the first wave of the 3PC and 222 233 members attributed to 312 PCPs and 14 physician organizations that continued in a fee-for-service model in 2016 but had 3PC start dates thereafter.ExposuresParticipation in the 3PC system.Main outcomes and measuresThe primary outcome was the change in a composite measure score reflecting the probability that a member achieved an eligible measure out of 13 pooled Healthcare Effectiveness Data and Information Set quality measures. Primary care visits and total cost of care were among 15 secondary outcomes.ResultsIn total, the study included 299 458 HMSA members (mean age, 42.1 years; 51.5% women) and 419 primary care physicians (mean age, 54.9 years; 34.8% women). The risk-standardized composite measure scores for 2012 to 2016 changed from 75.1% to 86.6% (+11.5 percentage points) in the 3PC group and 74.3% to 83.5% (+9.2 percentage points) in the non-3PC group (differential change, 2.3 percentage points [95% CI, 2.1 to 2.6 percentage points]; P < .001). Of 15 prespecified secondary end points for utilization and spending, 11 showed no significant difference. Compared with the non-3PC group, the 3PC system was associated with a significant reduction in the mean number of primary care visits (3.3 to 3.0 visits vs 3.3 to 3.1 visits; adjusted differential change, -3.9 percentage points [95% CI, -4.6 to -3.2 percentage points]; P < .001), but there was no significant difference in mean total cost of care ($3344 to $4087 vs $2977 to $3564; adjusted differential change, 1.0% [95% CI, -1.3% to 3.4%]; P = .39).Conclusions and relevanceIn its first year, the 3PC population-based primary care payment system in Hawaii was associated with small improvements in quality and a reduction in PCP visits but no significant difference in the total cost of care. Additional research is needed to assess longer-term outcomes as the program is more fully implemented and to determine whether results are generalizable to other health care markets.
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Project description:ObjectivesThis paper aims to synthesize existing scholarship on quality measures in oncology, with a specific focus on outcome-based quality measures, which are often underutilized. We also present a set of "core outcome measures" that may be considered in future oncology alternative payment models (APMs).Study designOur research consists of a focused literature review, content analysis, and quality measure synthesis and categorization.MethodsWe conducted a focused literature review to generate key evidence on quality measures in oncology. We studied 7 oncology quality assessment frameworks, encompassing 142 quality metrics, and synthesized recommendations using the Center for Medicare and Medicaid Innovation APM toolkit, focusing on outcome measures.ResultsWe present 34 outcome-based oncology quality measures for consideration, which are classified into 5 domains: clinical care (eg, hospital and emergency department visits, treatment effectiveness, mortality), safety (eg, infections, hospital adverse events), care coordination (for hospital and hospice care), patient and caregiver experience, and population health and prevention. Both general and indication-specific outcome measures should be considered in oncology APMs, as appropriate. Utilizing outcome-based measures will require addressing multiple challenges, ranging from risk adjustment to data quality assurance.ConclusionsOncology care will benefit from a more rigorous approach to quality assessment. The success of oncology APMs will require a robust set of quality measures that are relevant to patients, providers, and payers.
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Project description:BackgroundAlternative payment models (APMs) in healthcare are emerging that reward quality of care over quantity of services. Most bundled payment programs that are described in published studies are related to episodes for a surgical inpatient hospital stay. With outpatient services, monthly capitated payments are an alternative to bundled payments for specialty services.ObjectiveTo assess the association of a capitated contractual arrangement between a primary care physician group and an oncology clinic group with the quality of care received.MethodsWe evaluated the effect of an oncology group's transition from a fee-for-service (FFS) arrangement to a partial-capitated-payment model with a primary care group. We compared outcomes for patients who received treatment after implementation of the new arrangement (ie, postcontract capitated group) with outcomes of patients receiving treatment before the change (ie, precontract capitated group). In addition, we conducted a parallel analysis of patients from a population that was not affected by the contract to assess temporal effects (ie, postcontract FFS group vs precontract FFS group). All patients were enrolled in Medicare Advantage plans of a single health plan (ie, Humana), and outcomes were measured using claims data provided by that company. Patients in the 2 precontract groups received treatment between July 1, 2010, and June 30, 2011; patients in the 2 postcontract groups received treatment between January 1, 2013, and December 31, 2013. Age- and sex-adjusted all-cause hospitalization, complications from cancer treatment, and ambulance transfers during 6 months of follow-up were evaluated.ResultsIn the population subject to the partial-capitated-payment model, the postcontract group (N = 305) was younger than the precontract group (N = 165). In a subset of patients in the 2 capitated groups who had Deyo-Charlson Comorbidity Index (CCI) RxRisk scores, the postcontract capitated group had significantly higher CCI scores. Adjusted odds ratios for the postcontract capitated group versus the precontract capitated group showed no difference in the likelihood that any of the outcomes would occur. However, the mean number of chemotherapy-related complications and ambulance transports were greater postcontract. In the parallel analysis of the population not affected by the new payment arrangement, no differences were found between the pre- and postcontract groups. This suggests that temporal changes potentially affecting patients in the capitated and FFS populations would not have influenced postcontract outcomes.ConclusionsAfter the implementation of partial-capitated payments for medical oncology services in the oncology practice, the likelihood of a patient experiencing at least 1 event of a specific adverse outcome did not change; however, the average number of some adverse events did increase, which may in part be explained by a higher level of underlying morbidity in the postcontract group. The overall findings of this study suggest that quality of care was not compromised in this APM.
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