position is a rare opportunity to apply analysis of real-world data from electronic health records, insurance claims databases and medical-device generated data to the development of new medical therapies and devices. The goals of the position include: 1) generating insights from predictive models and machine learning on the procedures and medical devices that work best by patient type, 2) understand why some patients are more responsive to procedures that include medical devices than other patients, and 3) identifying the long-term outcomes associated with procedures that match patients well versus less well-matched procedures. The initial project will focus on total hip and knee replacement surgeries.
The Medical Device Epidemiology department conducts observational research to support product development, licensing and acquisition, product launch, and post-market safety and value assessments of medical device products, and fosters methodological excellence across the sector. Typical research addresses a variety of questions related to safety, product development, health economics and outcomes research, and other clinical or commercial activities. These may involve analyses of safety signals, disease burden/progression/ co-morbidities, standard of care, cost/reimbursement patterns, market size, site of care, and clinical/health economic outcomes and predictive modeling to better understand patient risk factors.
Duties & Responsibilities
An important part of the position is to act with integrity and conduct research of high scientific standard that benefits patients. The role will involve participation in various project teams and task forces in addressing issues raised by the Data Enablement project utilizing real-world evidence to advance the innovation of medical devices. Dissemination of scientific information through technical reports or models, presentations, and publications in peer-reviewed literature, as agreed by the team, is an important part of the position.
Close collaboration with cross-functional teams involving R&D leaders, data scientists, healthcare researchers and healthcare providers is a key requirement.
• Participating in various work streams/ task forces to standardize research and analytical processes to improve efficiency and quality of deliverables
• Facilitating access to key healthcare databases and providing core analytics capability for clinicians and researchers across the sector.
• Knowledge of healthcare databases, economic and predictive modeling, and other related analytical methodology is required
• The candidate must have a sophisticated understanding and the ability to analyze and interpret quantitative data using statistical software such as SAS or R.
• Experience with writing methods sections of study proposals/ protocols/ or background epidemiologic material on specific disease or therapeutic areas is a must.