I. General Information

1. Course Title:
Introduction to Health Data Analysis

2. Course Prefix & Number:
HINS 1154

3. Course Credits and Contact Hours:

Credits: 3

Lecture Hours: 3

4. Course Description:

This introductory course provides the student with the foundation and knowledge of healthcare data analysis. This course will cover how to manage, analyze, and present data. The course covers how to identify problems and create recommendations from the data that can be used by healthcare organizations to make effective decisions.

5. Placement Tests Required:

Accuplacer (specify test):

No placement tests required

Score:

6. Prerequisite Courses:

There are no prerequisites for this course.

9. Co-requisite Courses:

There are no corequisites for this course.

II. Transfer and Articulation

2. Transfer - regional institutions with which this course has a written articulation agreement:

College of St. Scholastica, Articulation agreement signed Summer 2020 (General Elective)

MSU - Moorhead, Articulation agreement signed Spring 2019 (MSU-M Electives)

III. Course Purpose

1. Program-Applicable Courses – This course is required for the following program(s):

Healthcare Administrative Specialist, AAS
Healthcare Administrative Specialist, Diploma

IV. Learning Outcomes

1. College-Wide Outcomes

College-Wide Outcomes/Competencies	Students will be able to:
Apply abstract ideas to concrete situations	Explain the practical use of statistics and manipulation of data in the healthcare setting by identifying problems and finding the correct data to generate statistics to present the information to others.
Apply ethical principles in decision-making	Explain the purpose of data quality and define characteristics of quality data.

2. Course Specific Outcomes - Students will be able to achieve the following measurable goals upon completion of the course:

• Explain the importance of statistics and data analytics in healthcare;
• Differentiate common healthcare data sets and data bases;
• Construct a variety of tables using specified guidelines;
• Describe the inpatient census, inpatient service days, and how they are calculated;
• Compute length of stay, average length of stay, leave of absence days, and bed turnover rates;
• Collect data on morbidity in healthcare settings and compute rates for infections and general complications;
• Apply productivity standards to various functions within health information management;
• Demonstrate understanding of inpatient prospective payment systems and case mix and how the calculations are performed;
• Analyze case mix index data to identify fraudulent coding trends;
• Explain delinquency rate calculations for accreditation purposes;
• Explain monthly budgets and budget variances; and 
• Explain the purpose of data quality and define characteristics of quality data.

V. Topical Outline

Listed below are major areas of content typically covered in this course.

1. Lecture Sessions

1. Statistical Terms and Concepts in Health Data Management
• Types of statistics
• Healthcare data classifications
• Obtain and compare healthcare data
2. Basic Math Concepts
• Fractions, decimals, and percentages
• Ratio, rate, and proportion
• Volume, frequency, and frequency distribution
3. Data Presentation
• Tables
• Graphs and Charts
4. Administrative Data
• Outpatient statistics
• Inpatient statistics
5. Clinical Facility Data
• Morbidity rate
• Consultation rate
• Obstetric rates
• Mortality rates
• Autopsy rates
6. Public Health Data
• Types of rates and differences in constants
• Natality data
• Mortality statistics
• Morbidity data
7. Departmental Data
• Measures of productivity
• Reimbursement statistics
• Compliance statistics
8. Financial Data
• Budgeting overview
• Capital budget
• Operational budget
9. Scrubbing and Mapping Data
• Data quality
• Data scrubbing
• Data mapping
10. Predicting Data
• Populations and samples
• Probability
11. Industry Comparative Data and Data Reporting
• World Health Organization (WHO)
• Centers for Medicare and Medicaid Services (CMS)
• National Center for Health Statistics (NCHS)