Author Interviews, Cost of Health Care, JAMA / 01.11.2019
Safety Net Hospital Earn Modest Profit from Medicare’s Discount Drug Program
MedicalResearch.com Interview with:
[caption id="attachment_51983" align="alignleft" width="133"]
Dr. Conti[/caption]
Rena M. Conti, PhD, Associate Professor
Department of Markets, Public Policy and Law
Questrom School of Business
Boston University Boston, MA 02215
Co-Authors:
Sayeh S. Nikpay, PhD
Melinda B. Buntin, PhD
Vanderbilt University School of Medicine
MedicalResearch.com: What is the background for this study?
Response: The federal 340B program provides deep discounts on the acquisition cost of prescription drugs for participating hospitals and places no limits on what hospitals charge patients and insurers. Congress intended 340B profits generated from hospital participation to subsidize the provision of safety net care for patients residing in the community.
This study is the first to estimate the size of profits hospitals participating in the 340B drug discount program collect from Medicare patients for the outpatient clinic administration of prescription drugs.
Dr. Conti[/caption]
Rena M. Conti, PhD, Associate Professor
Department of Markets, Public Policy and Law
Questrom School of Business
Boston University Boston, MA 02215
Co-Authors:
Sayeh S. Nikpay, PhD
Melinda B. Buntin, PhD
Vanderbilt University School of Medicine
MedicalResearch.com: What is the background for this study?
Response: The federal 340B program provides deep discounts on the acquisition cost of prescription drugs for participating hospitals and places no limits on what hospitals charge patients and insurers. Congress intended 340B profits generated from hospital participation to subsidize the provision of safety net care for patients residing in the community.
This study is the first to estimate the size of profits hospitals participating in the 340B drug discount program collect from Medicare patients for the outpatient clinic administration of prescription drugs.

Dr. Villanti[/caption]
Andrea Villanti, PhD, MPH
Associate Professor
Department of Psychiatry
Vermont Center on Behavior and Health
University of Vermont
MedicalResearch.com: What is the background for this study?
Response: Our earlier work documented a significant association between first use of a flavored tobacco product and current tobacco use (

Dr. Helen Marsden PhD
Skin Analytics Limited
London, United Kingdom
MedicalResearch.com: What is the background for this study?
Response: In this technology age, with the explosion of interest and applications using Artificial Intelligence, it is easy to accept the output of a technology-based test - such as a smartphone app designed to identify skin cancer - without thinking too much about it. In reality, technology is only as good as the way it has been developed, tested and validated. In particular, AI algorithms are prone to a lack of “generalisation” - i.e. their performance drops when presented with data it has not seen before. In the medical field, and particularly in areas where AI is being developed to direct a patient’s diagnosis or care, this is particularly problematic. Inappropriate diagnosis or advice to patients can lead to false reassurance, heightened concern and pressure on NHS services, or worse. It is concerning, therefore, that there are a large number of smartphone apps available that provide an assessment of skin lesions, including some that provide an estimate of the probability of malignancy, that have not been assessed for diagnostic accuracy.
Skin Analytics has developed an AI-based algorithm, named: Deep Ensemble for Recognition of Malignancy (DERM), for use as a decision support tool for healthcare providers. DERM determines the likelihood of skin cancer from dermoscopic images of skin lesions. It was developed using deep learning techniques that identify and assess features of these lesions which are associated with melanoma, using over 7,000 archived dermoscopic images. Using these images, it was shown to identify melanoma with similar accuracy to specialist physicians. However, to prove the algorithm could be used in a real life clinical setting, Skin Analytics set out to conduct a clinical validation study.
Dr. Qing Chen[/caption]
Qing Chen, M.D., Ph.D.
Assistant Professor, Immunology, Microenvironment & Metastasis Program
Scientific Director, Imaging Facility
The Wistar Institute
MedicalResearch.com: What is the background for this study?
Response: We are focusing on how a specific type of brain cells, astrocytes, helps the cancer cells from melanoma and breast cancer to form metastatic lesions.
