Quantitative Methods and Decision Analysis

Health analytics practitioners use an array of quantitative methods to evaluate questions of efficiency and effectiveness in health care. This advanced course for healthcare analysts and other professionals integrates and builds on prior coursework in statistics and data mining and provides additional exposure to advanced methodologies, such as event sequencing, analytical groupers, simulation and predictive modeling. Heavy emphasis is given to the application of methods to contemporary analytic challenges in health care, including demand/utilization forecasting, risk stratification, population health management, quality measurement, fraud detection, and cost containment from readmissions. Throughout this course, students will complete a series of hands-on assignments in the use of specific analytic approaches, as well as a final course project.

Learning Outcomes

• Understand/compare the array of quantitative methods available from diverse fields of statistics, data mining and domain-specific fields
• Utilize a variety of clinical and administrative data sets to identify cohorts for research, quality measurement or interventions
• Evaluate a simulation model, interpret the findings and assess model performance
• Utilize appropriate methods to determine which interventions are most likely to lead to positive outcomes
• Identify the best approaches to profile the highest-cost patients of members and evaluate mechanisms to lower costs

Skills You Will Gain

• Decision analysis
• Predictive modeling and visualization
• Risk assessment and stratification
• Cost containment
• Fraud detection