Participants Peter Salgo, MD; John L. Fox, MD, MHA; Ira M. Klein, MD, MBA, FACP; Michael Kolodziej, MD; Bryan Loy, MD; and Irwin W. Tischler, DO, evaluate the use of quality-adjusted life-years and comparative effectiveness research (CER) to inform treatment decisions.
While it is important to measure and assess quality-adjusted life-years, Dr Kolodziej advises that finding a way to evaluate poor patient outcomes when conducting CER would provide valuable additional information. Dr Loy adds that when evaluating CER, it is imperative to understand what matters most to the various stakeholders.
Dr Tischler explains that patients and their families are often unaware of the costs of therapy. Although it is a difficult discussion to lead, it is important for clinicians to discuss cost with patients. Dr Fox adds that these discussions can help patients understand their options and what to expect. It is also important that clinicians establish trust with patients and caregivers and provide information regarding the risks and benefits of treatment, comments Dr Kolodziej. When patients with terminal diseases are fully informed about their options, most decide not to receive additional treatment, but rather to be kept comfortable, remarks Dr Fox.
Oncology Onward: A Conversation With Penn Medicine's Dr Justin Bekelman
December 19th 2023Justin Bekelman, MD, director of the Penn Center for Cancer Care Innovation, sat with our hosts Emeline Aviki, MD, MBA, and Stephen Schleicher, MD, MBA, for our final episode of 2023 to discuss the importance of collaboration between academic medicine and community oncology and testing innovative cancer care delivery in these settings.
Listen
Emily Goldberg Shares Insights as a Genetic Counselor for Breast Cancer Risk Screening
October 30th 2023On this episode of Managed Care Cast, Emily Goldberg, MS, CGC, a genetic counselor at JScreen, breaks down how genetic screening for breast cancer works and why it is so important to increase awareness and education around these screening tools available to patients who may be at risk for cancer.
Listen
Machine Learning Model Predicts Hepatocellular Carcinoma Risk in Patients With MASLD
March 22nd 2024Machine learning models have potential for early identification of patients with metabolic dysfunction-associated steatotic liver disease (MASLD) who are at risk of hepatocellular carcinoma.
Read More