Lecture: 4 hours/week
or
Hybrid: 2 hours/week in class and 2 hours/week online
In this course, students engage in a variety of learning activities such as lectures, case study analysis, independent research, exercises, training on data classification technology, participant presentations, classroom discussions and guest speakers.
- Population grouping methodology
- Case mix groups (CMG+) methodology
- Resource intensity weights (RIW) and expected length of stay (ELOS)
- Acute ambulatory care grouping methodology - Comprehensive Ambulatory Classification Systems (CACS)
- Analysis of quality, risk, utilization and financial management of healthcare using data from population, inpatient and ambulatory care grouping methodologies
- Concepts of ethics, equity, diversity and inclusivity supported by data management
At the end of the course, the successful student will be able to:
- apply case mix groups methodologies to analyze health outcomes from the quality, risk, utilization and financial management perspectives;
- apply population grouping methodologies to analyze health outcomes within various sectors;
- apply CACS methodologies to analyze health outcomes provided by ambulatory care services;
- create geographical information visualization using ArcGIS software (ESRI Canada) to facilitate monitoring and trending of health outcomes across the care continuum on a regional, territorial, provincial, and national level;
- determine methods to address concepts of ethics, equity, diversity and inclusivity supported by health data.
The course evaluation is consistent with the Douglas College Evaluation Policy. An evaluation schedule is presented at the beginning of the course. This is a graded course. All assignments must be completed to pass the course.
A list of required and optional textbooks, materials and electronic applications is provided for students at the beginning of each semester.