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Health services research, cancer disparities, cancer care delivery innovation, payment policy
Dr. Manz investigates cancer care delivery and payment policy and how they contribute to disparities in cancer outcomes, with the goal of informing policies that can reduce cancer care disparities.
Dr. Manz’s research on payment policy focuses on how the way that we pay for drugs and services affects cancer outcomes. His recent and current work explores how Medicare policies that pay for new cancer therapies in the Oncology Care Model and the New Technology Add-on Payment affects disparities in patient access to new therapies. He is also examining how models for distributing and reimbursing expensive cancer drugs administered in doctor’s offices affect patient out of pocket costs and drug prices.
Dr. Manz has also investigated novel applications of machine learning to prompt oncology clinicians to have Serious Illness Conversations with their patients. His work has demonstrated that combining machine-learning generated mortality predictions with behavioral nudges to oncology clinicians can increase the number of Serious Illness Conversations that patients have with their oncology team, which may ensure that patients receive goal-concordant care near the end of life.  This work may improve the value of care that oncologists provide for their patients.

Disparities in cancer prevalence, incidence, and mortality for incarcerated and formerly incarcerated patients: A scoping review. Cancer Med. 2021 Sep 03.
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Racial Disparities in Colorectal Cancer Recurrence and Mortality: Equitable Care, Inequitable Outcomes? J Natl Cancer Inst. 2021 Jun 01; 113(6):656-657.
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Combining Machine Learning Predictive Algorithms With Behavioral Nudges to Increase Rates of Serious Illness Conversations in Patients With Cancer-Reply. JAMA Oncol. 2021 May 01; 7(5):782.
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Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer: A Stepped-Wedge Cluster Randomized Clinical Trial. JAMA Oncol. 2020 Dec 01; 6(12):e204759.
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Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer. JAMA Oncol. 2020 Nov 01; 6(11):1723-1730.
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The Changing Characteristics of Technologies Covered by Medicare's New Technology Add-on Payment Program. JAMA Netw Open. 2020 08 03; 3(8):e2012569.
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Integrating machine-generated mortality estimates and behavioral nudges to promote serious illness conversations for cancer patients: Design and methods for a stepped-wedge cluster randomized controlled trial. Contemp Clin Trials. 2020 03; 90:105951.
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Innovation and Access at the Mercy of Payment Policy: The Future of Chimeric Antigen Receptor Therapies. J Clin Oncol. 2020 02 10; 38(5):384-387.
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Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. JAMA Netw Open. 2019 10 02; 2(10):e1915997.
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Mind the gap: how vulnerable patients fall through the cracks of cancer quality metrics. BMJ Qual Saf. 2020 02; 29(2):91-94.
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Getting in sync with adherence to endocrine therapy in breast cancer. Cancer. 2019 11 15; 125(22):3917-3920.
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Marketing to physicians in a digital world. N Engl J Med. 2014 Nov 13; 371(20):1857-9.
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