Performance Measurement & Improvement Core
The Performance Measurement and Improvement Core is 1 of the 4 required core courses that build upon one another. This core comprises 2 courses: Quality, Safety and Performance Improvement; and Program Evaluation and Applied Epidemiology.
- Examine and compare alternative approaches to measuring the quality and safety of healthcare
- Explore strategies for changing clinical practice and improving quality of care within large healthcare organizations
- Study interventions designed to change practice and improve quality of care, including statistical process control methods, quasi-experimental designs and qualitative data
- Examine the strengths and limitations of health-information technology
- Recognize how healthcare policies and financial-incentive systems impede or encourage efforts to improve quality of care
- Evaluate the costs associated with low-quality healthcare and explore the relationship between quality and value
- Understand how to collect relevant data to estimate the potential impact of health services interventions on health outcomes
- Critically appraise publications dealing with health services, outcomes or clinical research
- Learn how to develop a conceptual framework for a program evaluation, showing the hypothesized causal variables and the expected outcomes
- Identify potential sources of bias and problems with measurement reliability and validity
Quality, Safety and Performance Improvement (HDS 203A)
Explore issues related to quality and safety in healthcare. The overarching goal of HDS 203A is to introduce the theory and practice of quality measurement. Three scientific disciplines are presented: quality measurement, quality improvement and program evaluation. The class also explores contextual factors that influence quality of care, including health policy and payment incentives, health information technology and controversies such as physician autonomy in an increasingly systematized healthcare environment.
Course material is closely linked to real-world applications, with examples drawn from ongoing hospital, health system and policy initiatives from around the country. Students learn via interactive lectures, journal club sessions analyzing relevant articles, homework assignments, and an in-depth course project.
Topics include the different types of measures (e.g., structure, process, outcome), data sources that can be used for measurement (e.g., claims data, electronic health record data, medical record data and patient outcome data), attributes of measures and data sources required to be valid reflections of quality, approaches to quality measure development and quality measures of importance nationally (e.g., HEDIS measures, Medicare quality measures for hospitals, etc.).
Next, the course covers strategies for changing clinical practice and improving quality, a field increasingly referred to as implementation science. Diverse schools of thought are drawn upon, including management science, behavioral economics, organizational psychology and performance improvement techniques (e.g., Lean Six Sigma). Evidence from applications to healthcare systems is reviewed, particularly systematic reviews from the Cochrane Collaboration.
The third major focus is on program evaluation methods. Students are introduced to statistical process control techniques as well as experimental and quasi-experimental study designs that are frequently used to evaluate changes in clinical practice. The advantages and limitations of both quantitative data and qualitative information will be discussed.
Program Evaluation and Applied Epidemiology (HDS 203B)
Builds on the framework presented in HDS 203A. Class topics include using epidemiological methods to assess the utilization and quality of medical care, evaluation and economic analysis of interventions, and health-policy analyses.
The class begins by defining the field of outcomes research and then addresses how healthcare systems measure outcomes and why it matters. Students then explore the difference between randomized controlled trials and pragmatic controlled trials, explore the pros and cons of cohort studies and distinguish these designs from case-control trials.
HDS 203B explores quasi-experimental designs frequently required for program evaluation, such as time series and difference-in-difference designs. The course also explores the influence of bias on data interpretation (e.g., selection bias, information bias, confounding, interactions and effect modification).