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Christopher Manz, MD, MSHP

Medical Oncology

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Christopher Manz, MD, MSHP

Physician

  • Instructor in Medical Oncology, Harvard Medical School

Centers/Programs

Clinical Interests

  • Oncology

Contact Information

  • Office Phone Number617-632-3315
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Bio

Dr. Manz graduated from Duke University School of Medicine in 2012. He received his training in internal medicine at the Hospital of the University of Pennsylvania from 2012-2015, and subsequently worked as a hospitalist in oncology at the same hospital until 2017. After completing fellowship in hematology/oncology and a Masters of Science in Health Policy Research at the University of Pennsylvania, he joined Dana-Farber in GI Oncology and the Department of Population Sciences 2020.

Board Certification:

  • Internal Medicine

Fellowship:

  • University of Pennsylvania

Residency:

  • Hospital of the University of Pennsylvania

Medical School:

  • Duke University School of Medicine

Recent Awards:

  • Penn Department of Medicine Measey Physician Scientist Fellowship Award
  • ASCO Conquer Cancer Young Investigator Award

Research

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.

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.
View in: PubMed

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 Apr; 18(4):e495-e503.
View in: PubMed

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.
View in: PubMed

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.
View in: PubMed

Disparities in cancer prevalence, incidence, and mortality for incarcerated and formerly incarcerated patients: A scoping review. Cancer Med. 2021 10; 10(20):7277-7288.
View in: PubMed

Racial Disparities in Colorectal Cancer Recurrence and Mortality: Equitable Care, Inequitable Outcomes? J Natl Cancer Inst. 2021 06 01; 113(6):656-657.
View in: PubMed

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.
View in: PubMed

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 12 01; 6(12):e204759.
View in: PubMed

Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer. JAMA Oncol. 2020 11 01; 6(11):1723-1730.
View in: PubMed

The Changing Characteristics of Technologies Covered by Medicare's New Technology Add-on Payment Program. JAMA Netw Open. 2020 08 03; 3(8):e2012569.
View in: PubMed

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.
View in: PubMed

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.
View in: PubMed

Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. JAMA Netw Open. 2019 10 02; 2(10):e1915997.
View in: PubMed

Mind the gap: how vulnerable patients fall through the cracks of cancer quality metrics. BMJ Qual Saf. 2020 02; 29(2):91-94.
View in: PubMed

Getting in sync with adherence to endocrine therapy in breast cancer. Cancer. 2019 11 15; 125(22):3917-3920.
View in: PubMed

Marketing to physicians in a digital world. N Engl J Med. 2014 Nov 13; 371(20):1857-9.
View in: PubMed

Research Departments:

Location

Dana-Farber Cancer Institute
450 Brookline Ave
Dana Building 1111
Boston, MA 02215
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