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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.

Long-term Effect of Machine Learning-Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer: A Randomized Clinical Trial. JAMA Oncol. 2023 Mar 01; 9(3):414-418.
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Effect of a MUC5AC Antibody (NPC-1C) Administered With Second-Line Gemcitabine and Nab-Paclitaxel on the Survival of Patients With Advanced Pancreatic Ductal Adenocarcinoma: A Randomized Clinical Trial. JAMA Netw Open. 2023 01 03; 6(1):e2249720.
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Association of Oncologist Participation in Medicare's Oncology Care Model With Patient Receipt of Novel Cancer Therapies. JAMA Netw Open. 2022 09 01; 5(9):e2234161.
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Oncologist Perceptions of Algorithm-Based Nudges to Prompt Early Serious Illness Communication: A Qualitative Study. J Palliat Med. 2022 11; 25(11):1702-1707.
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Breast Medical Oncologists' Perspectives of Telemedicine for Breast Cancer Care: A Survey Study. JCO Oncol Pract. 2022 09; 18(9):e1447-e1453.
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Oncologist phenotypes and associations with response to a machine learning-based intervention to increase advance care planning: Secondary analysis of a randomized clinical trial. PLoS One. 2022; 17(5):e0267012.
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Patient and clinician nudges to improve symptom management in advanced cancer using patient-generated health data: study protocol for the PROStep randomised controlled trial. BMJ Open. 2022 05 12; 12(5):e054675.
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Clinician perspectives on machine learning prognostic algorithms in the routine care of patients with cancer: a qualitative study. Support Care Cancer. 2022 May; 30(5):4363-4372.
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Impact of Behavioral Nudges on the Quality of Serious Illness Conversations Among Patients With Cancer: Secondary Analysis of a Randomized Controlled Trial. JCO Oncol Pract. 2022 04; 18(4):e495-e503.
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Automated Text-Based Symptom Monitoring With Rapid Clinician Triage for Patients With Cancer and Suspected or Confirmed COVID-19. JCO Clin Cancer Inform. 2021 10; 5:1134-1140.
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Treatment utilization patterns of newly initiated oral anticancer agents in a national sample of Medicare beneficiaries. J Manag Care Spec Pharm. 2021 Oct; 27(10):1457-1468.
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Disparities in cancer prevalence, incidence, and mortality for incarcerated and formerly incarcerated patients: A scoping review. Cancer Med. 2021 10; 10(20):7277-7288.
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Racial Disparities in Colorectal Cancer Recurrence and Mortality: Equitable Care, Inequitable Outcomes? J Natl Cancer Inst. 2021 06 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 05 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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