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Analytical Solutions to Common Healthcare Problems

Approx. 11 hours to complete

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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

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

Intermediate

Course 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.

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