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Course Catalog

309.635.81 Population Health: Analytic Methods and Visualization Techniques

Department:
Health Policy and Management
Term:
4th term
Credits:
3 credits
Academic Year:
2019 - 2020
Location:
Internet
Auditors Allowed:
No
Grading Restriction:
Letter Grade or Pass/Fail
Contact:
Hadi Kharrazi
Course Instructor :
Resources:
Prerequisite:

Introduction to Online Learning is required prior to participating in any of the School's Internet-based courses. 309.631.81 course is recommended but not required.

Description:

Population health management is one of the key factors of a successful value-based care organization. To improve the population management process, health plans/systems have a growing need to better analyze the trends in their population denominators and allocate their resources more efficiently. This course addresses this growing need of our health industry by preparing the students to understand the process of analyzing population health data and helping them to lead analytical teams of value-based care providers such as accountable care organizations (ACOs) and patient-centered medical homes (PCMHs).

Introduces students to concepts, methods, and issues related to the application of data science to population health. Covers the uses of informatics to define and identify populations and sub-populations of interest, and describe the health status and needs of them. Describes the process of analyzing population health data from checking data quality to developing predictive models of utilization. Examines different data sources / methods to risk stratify a population of interest and compares the advantage and disadvantages of each data source / method. Describes various techniques to visualize data quality, depict the denominator selection process, and illustrate the risk adjustment results for large populations

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Summarize population health data sources, tools, methods and visualization techniques
  2. Describe the common population health data sources (e.g., insurance claims, electronic health records, health information exchanges) and their potential data quality issues (e.g., completeness, accuracy, timeliness, provenance)
  3. Use population health tools to group underlying populations into subpopulation and prepare the data for analysis (e.g., use of the Johns Hopkins ACG tool, along with a combination of SQL and R)
  4. Explain the advantages and disadvantages of various population health analytic methods (e.g., regression methods vs. machine learning methods)
  5. Identify most effective visualization techniques that can be used to convey impactful results to different end users (e.g., patients, clinicians, care managers/coordinators, health system admin, and policy makers)
  6. Describe pertinent government policies that relate to the use of health informatics to improve population health
Methods of Assessment:

Participation 10%; Discussion Board 15%; Quizzes 25%; Mid-term Project 20%; Final-term Project 30%

Enrollment Restriction:

Not open to undergraduate students

Instructor Consent:

No consent required