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
Academic Units
Section Number
Instruction Method
Online class

Section Notes

Course begins September 27 and ends December 6. Students will use SAS Studio for the course to complete assignments. The required course pack/reader will be available one week before the start date of the course.  

This is not a self-paced course. Students will progress through the course together. Course modules are released weekly, where lessons typically load on Wednesdays and are due the following Tuesdays. You can log in and work on courses at any time within that week to view lectures and complete assignments. The previous week's lesson will remain available for the duration of the course. If you enroll after the course start date, you will need to contact the instructor regarding missed lessons.

Refund Deadline: October 10, 2023.  Refunds and/or enrollment transfers will not be approved after this date.

This is the last time this course will be offered. If you do not complete this course this quarter, there will not be an option to transfer to a later quarter.

Enrollment Policies

Click here to view complete enrollment policy information including details on withdrawals and transfers.

Refund Deadline: October 10, 2023.  Refunds and/or enrollment transfers will not be approved after this date.


Methods I: Applied Healthcare Statistics 142HCA202

Technical Requirements

To ensure your success in this online course, please review our technical requirements page. Students will use SAS Studio for the course to complete assignments. Additional, program specific software and analytical tool requirements may be found in the Frequently Asked Questions.

Section Course Materials

202HCA203 Course reader required.

https://cognella.com/students/. One week prior to the class start date, you will receive a welcome email with a direct link to purchase the reader.

Data Science for Business: What you need to know about data mining and data-analytic thinking


SAS Programming with Medicare Administrative Data