This course aims to provide students with the concepts, data, and methodology needed to critically evaluate new medical technologies in order to secure financial investment, reimbursement, and regulatory compliance goals such as FDA approval. The course is intended to provide students with an understanding of the analytical tools required to evaluate medical technologies.
After completing this course, students will be able to:
- Recognize the reimbursement systems that support the use of medical technology.
- Recognize the role of the government and regulatory agencies in the development and implementation of new medical technologies.
- Identify a population that will benefit from a medical technology.
- Use health care data to evaluate a medical technology.
- Perform cost/benefit and cost/effectiveness analysis of a new technology.
1. Medical Technology Assessment in the Current Health Care Financing and Regulatory Environment
- Welcome to the Medical Technology and Evaluation Course
- About Your Instructor, Eric Barrette!
- Introduction to the medical technology clients
- The unique need for medical technology assessment
- How do new technologies impact health care expenditures?
- The new technology regulatory environment
- Pharmaceuticals Regulation
- Medical Device Regulation
- Government vs. Private Payers
- Inpatient Hospital Payments
- Prescription Drug Payments
2. Clinical Trials and Insurance Claims Data
- Introduction to Clinical Trial
- The Role of Randomization
- Blinded Designs
- Introduction to Administrative Data
- Elements of Claims Data1
- Sources of Claims Data
- Bias in Data
- Combining Trial Data
- Using Claims Data
3. The Elements of Medical Technology Assessments
- The Types and Uses of Assessments
- CEA vs CUA
- Introductions to Costs
- Quality Adjusted Life Years (QALYs)
- Measuring Quality of Life (QoL)
4. Methodological Approaches and Considerations
- Uses of CEA
- Constructing an ICER
- Interpreting an ICER
- Baye’s Rule
- Decision Models
- Introduction to Uncertainty
- Monte Carlo Simulation
- Markov Models