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Outcome measures for oncology alternative payment models: practical considerations and recommendations.

ABSTRACT: OBJECTIVES:This 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 DESIGN:Our research consists of a focused literature review, content analysis, and quality measure synthesis and categorization. METHODS:We 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. RESULTS:We 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. CONCLUSIONS:Oncology 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.

SUBMITTER: Hlavka JP

PROVIDER: S-EPMC6927528 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Outcome measures for oncology alternative payment models: practical considerations and recommendations.

Hlávka Jakub P JP Lin Pei-Jung PJ Neumann Peter J PJ

The American journal of managed care 20191201 12

<h4>Objectives</h4>This 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).<h4>Study design</h4>Our research consists of a focused literature review, content analysis, and quality measure synthesis and categorization.<h4>Methods</h4>We conducted a focused lit ...[more]

PMID: 31860235

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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.

Project description:Novel cancer therapies are associated with survival patterns that differ from established therapies, which may include survival curves that plateau after a certain follow-up time point. A fraction of the patient population is then considered statistically cured and subject to the same mortality experience as the cancer-free general population. Mixture cure models have been developed to account for this characteristic. As compared to standard survival analysis, mixture cure models can often lead to profoundly different estimates of long-term survival, required for health economic evaluations. This tutorial is designed as a practical introduction to mixture cure models. Step-by-step instructions are provided for the entire implementation workflow, i.e., from gathering and combining data from different sources to fitting models using maximum likelihood estimation and model results interpretation. Two mixture cure models were developed to illustrate (1) an "uninformed" approach where the cure fraction is estimated from trial data and (2) an "informed" approach where the cure fraction is obtained from an external source (e.g., real-world data) used as an input to the model. These models were implemented in the statistical software R, with the freely available code on GitHub. The cure fraction can be estimated as an output from ("uninformed" approach) or used as an input to ("informed" approach) a mixture cure model. Mixture cure models suggest presumed estimates of long-term survival proportions, especially in instances where some fraction of patients is expected to be statistically cured. While this type of model may initially seem complex, it is straightforward to use and interpret. Mixture cure models have the potential to improve the accuracy of survival estimates for treatments associated with statistical cure, and the present tutorial outlines the interpretation and implementation of mixture cure models in R. This type of model will likely become more widely used in health economic analyses as novel cancer therapies enter the market.

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Outcome measures for oncology alternative payment models: practical considerations and recommendations.

Publications

Outcome measures for oncology alternative payment models: practical considerations and recommendations.

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