Analytical Solutions to Common Healthcare Problems
- 4.4
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Course Summary
Learn how to analyze and solve common healthcare problems using analytical solutions with this course. Discover how to apply statistics and data analysis to healthcare issues and improve patient outcomes.Key Learning Points
- Gain a deep understanding of how to use statistical methods to solve common healthcare problems
- Learn how to apply data analysis to improve patient outcomes
- Explore real-world case studies and examples to enhance your learning
Related Topics for further study
- Data Analysis
- Healthcare Statistics
- Patient Outcomes
- Healthcare Quality Improvement
- Real-World Case Studies
Learning Outcomes
- Understand how to use statistical methods to analyze and solve common healthcare problems
- Apply data analysis to improve patient outcomes
- Enhance your problem-solving skills through real-world case studies
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with healthcare terminology
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-Paced
Similar Courses
- Healthcare Analytics
- Healthcare Quality Improvement
- Data Analysis in Healthcare
Related Education Paths
Related Books
Description
In this course, we’re going to go over analytical solutions to common healthcare problems. I will review these business problems and you’ll build out various data structures to organize your data. We’ll then explore ways to group data and categorize medical codes into analytical categories. You will then be able to extract, transform, and load data into data structures required for solving medical problems and be able to also harmonize data from multiple sources. Finally, you will create a data dictionary to communicate the source and value of data. Creating these artifacts of data processes is a key skill when working with healthcare data.
Outline
- Solving the Business Problems
- Course Introduction
- Module 1 Introduction
- Provider Profiling
- How to Make Fairer Comparisons Using Risk Adjustment
- How Risk Adjustment is Performed
- Patient Safety: Measuring Adverse Events
- Super-Utilizers of Health Resources
- Fraud Detection
- A Note From UC Davis
- Module 1 Quiz
- Algorithms and "Groupers"
- Module 2 Introduction
- Clinical Identification Algorithms (CIA)
- HEDIS and AHRQ Quality Measures
- Analytical Groupers
- Open Source Groupers - Grouping Diagnoses and Procedures
- Open Source Groupers - Comorbidity, Patient Risk, and Drugs
- Commercial Groupers
- Module 2 Quiz
- ETL (Extract, Transform, and Load)
- Module 3 Introduction
- Analytical Processes and Planning
- Data Mining and Predictive Modeling - Part 1
- Data Mining and Predictive Modeling - Part 2
- Extracting Data for Analysis
- Transforming Data for Analytical Structures
- Module 3 Quiz
- From Data to Knowledge
- Module 4 Introduction
- Solving Analytical Problems with Risk Stratification
- Risk Stratification: Variables, Groupers, Predictors
- Risk Stratification: Model Creation/Evaluation and Deployment of Strata
- Medicare Claims Data - Source and Documentation
- Final Tips to Help Understand and Interpret Healthcare Data
- Course Summary
- Welcome to Peer Review Assignments!
- Module 4 Quiz
Summary of User Reviews
Key Aspect Users Liked About This Course
The course is well-structured and provides practical solutions to real-world healthcare problems.Pros from User Reviews
- The course content is relevant and up-to-date.
- The instructors are knowledgeable and engaging.
- The course provides practical solutions to real-world healthcare problems.
- The course is well-structured and easy to follow.
- The course provides opportunities for hands-on learning.
Cons from User Reviews
- The course may be too basic for those with advanced healthcare knowledge.
- The course may not be suitable for those looking for theoretical discussions on healthcare problems.
- The course may be too focused on the US healthcare system.
- The course may be too short for some learners.
- The course may not provide enough depth on certain healthcare problems.
Brian Paciotti
4.4 Raiting
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