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Outcome Measures for Oncology Alternative Payment Models: Practical Considerations and Recommendations
Jakub P. Hlávka, PhD; Pei-Jung Lin, PhD; and Peter J. Neumann, ScD

Outcome Measures for Oncology Alternative Payment Models: Practical Considerations and Recommendations

Jakub P. Hlávka, PhD; Pei-Jung Lin, PhD; and Peter J. Neumann, ScD
This review presents a set of evidence-based outcome measures for oncology alternative payment models, drawing on evidence from existing and proposed quality measures.
Recommendations for Outcome-Based Measures in Oncology

A synthesis and recommendations for future core outcome sets in oncology are available in Table 3. Measures that are generally seen as being closely tied to the quality of care received by oncology patients were classified into 5 quality domains identified by a CMMI APM toolkit39: clinical care, safety, care coordination, patient and caregiver experience, and population health and prevention. When possible, this set of outcome measures should be tailored to unique patient populations, diseases, providers, or other factors in individual payment models. In addition, some measures, such as hospice care—albeit appropriate for patients with more advanced disease—may not be relevant for patients with curable, early-stage cancer. Future oncology APMs should implement outcome measures relevant to the disease type and stage(s). For a detailed justification and discussion of individual categories and measures, please see eAppendix B.

Collecting outcomes data in all 5 domains of cancer care is fraught with challenges that have been documented in multiple studies. For example, to measure and track outcomes properly, programs often require big data that involve multiple sources, such as EHRs, health insurance claims, and patient/caregiver surveys; however, whether data are complete and accessible and can be translated into clinical practice remains a challenging issue.40 Many outcome-based measures rely on administrative claims data, which tend to have a long report lag. Some outcomes data, such as hospice care, may be challenging to access, especially when the patient is transferred from one payer to another. Chung and Basch41 discuss specific challenges related to collecting and using patient-generated health data (including PROs), ranging from “provider concerns, workflow issues, standardization of patient-generated health data and interoperability of devices/sensors, security and privacy issues” to a “lack of the necessary EHR functionalities and software innovations.” Additionally, statistical challenges related to missing values, highly dimensional data sets, and confounding (bias) require robust statistical approaches that are not yet available in broad clinical practice.42 Nonetheless, new approaches are being tested as outcome measures gain support from clinicians, patients, and payers, including a collaborative pilot on establishing a framework to evaluate real-world end points in advanced non–small cell lung cancer led by the Friends of Cancer Research and supported by both public and private stakeholders.43


As highlighted in this paper, both OCM and other quality initiatives in oncology rely on process- or outcome-based quality measures to determine the quality of care and—in some cases—the level of payment. Given evidence from the literature and an analysis of 7 oncology quality assessment frameworks, we presented a set of outcome-based measures for consideration in future payment models in oncology. Although some measures may be omitted in specific cases, we believe the inclusion of measures related to all 5 domains—clinical care, safety, care coordination, patient and caregiver experience, and population health and prevention—is highly desirable in future oncology APMs. Selective measurement of 1 outcome domain may create perverse incentives for providers to improve performance by underutilizing appropriate care and jeopardize optimal patient outcomes. Where appropriate, indication-specific quality measures should be included to account for quality-of-care complexities associated with individual cancer types and disease stages.

Overcoming hurdles to broader utilization of outcome-based measures in oncology will require a consensus between both payers and providers. These efforts should highlight the benefits of implementing outcome-based measures in oncology APMs (especially relative to the cost of implementation) and solutions to data and evaluation challenges (including risk adjustment and bias control). Future research is also needed to develop best practices for the inclusion and implementation of outcome measures in oncology clinical pathways.44 Additional considerations include developing strategies for quality control, dispute resolution, and administrative burden on providers and payers.

Given the steadily increasing costs of oncology care and, in some cases, the availability of multiple high-cost treatment options for patients with cancer, oncology care is in need of a more rigorous approach to quality assessment. The success of emerging oncology APMs will depend on a robust set of quality indicators that are relevant to patients, providers, and payers alike. 


The authors thank Jacqueline Vanderpuye-Orgle, Jeffrey Lemay, Zachary Wessler, and Harshali Patel for helpful comments on an earlier draft of this paper. The authors retained full control over research design, analysis, and findings presented herein.

Author Affiliations: University of Southern California Schaeffer Center for Health Policy and Economics (JPH), Los Angeles, CA; RAND Corporation (JPH), Los Angeles, CA; Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center (PJL, PJN), Boston, MA.

Source of Funding: This research was funded by a grant from Amgen Inc via a contract with Tufts Medical Center. Additional research support for Dr Hlávka was provided by the National Institute on Aging of the National Institutes of Health under Award Number R01AG062277. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author Disclosures: Dr Lin reports being a consultant or paid advisory board member for Takeda, Otsuka, and Alliance for Aging Research. Dr Neumann reports being a consultant or paid advisory board member for Precision Health Economics, Avexis, AbbVie, Novartis Pharmaceuticals, Research Triangle Institute, Merck, Genentech, Bluebird Bio, GlaxoSmithKline, DePuy, Otsuka, ICON, Indivior, Acorda, and Biogen, and has received lecture fees for speaking at the invitation of AbbVie, Pfizer, Celgene, Sanofi, and GlaxoSmithKline. Dr Hlávka reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (JPH, PJL, PJN); acquisition of data (JPH); analysis and interpretation of data (JPH, PJL, PJN); drafting of the manuscript (JPH, PJL); critical revision of the manuscript for important intellectual content (JPH, PJL, PJN); statistical analysis (JPH); provision of patients or study materials (PJL); obtaining funding (PJL); administrative, technical, or logistic support (PJL); and supervision (PJL, PJN).

Address Correspondence to: Pei-Jung Lin, PhD, Tufts Medical Center, 800 Washington St, Box 63, Boston, MA 02111. Email:

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