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Data Science for Healthcare Claims Data

Learn and practice how to transform raw healthcare claims data into valuable knowledge and actionable insights!
Instructor:
Dennis Arrindell
108 students enrolled
English [Auto]
In this course, you will learn and practice, how to transform raw healthcare claims data into valuable knowledge and actionable insights.

The most commonly available and widely used type of data in healthcare is claims data. Claims data is sometimes also called ‘billing data’ or administrative data. The reason why claims data is the most large scale, reliable and complete type of big data in healthcare is rather straightforward. It has to do with reimbursement, that is, the payment of health care goods and services depends on claims data. Healthcare providers may not always find the time to fill in all required paperwork in healthcare, but they will always do that part of their administration on which their income depends. Thus, in many cases, analyzing healthcare claims data is a much more pragmatic alternative for extracting valuable insights.

Claims data allows for the analysis of many non-biological elements pertaining to the organization of health care, such as patient referral patterns, patient registration, waiting times, therapy adherence, health care financing, patient pathways, fraud detection and budget monitoring. Claims data also allows for some inferences about biological facts, but these are limited when compared to medical records.

By following this course, students will gain a solid theoretical understanding of the purpose of healthcare claims data. Moreover, a significant portion of this course is dedicated to the application of data science and health information technology (Healthcare IT) to obtain meaningful insights from raw healthcare claims data.

This course is for professionals that (want to) work in health care organizations (providers and payers) that need to generate actionable insights out of the large volume of claims data generated by these organizations. In other words, people that need to apply data science and data mining techniques to healthcare claims data.

Examples of such people are: financial controllers and planners, quality of care managers, medical coding specialists, medical billing specialists, healthcare or public health researchers, certified electronic health records specialist, health information technology or health informatics personnel, medical personnel tasked with policy, personnel at procurement departments and fraud investigators. Finally, this course will also be very useful for data scientists and consultants that lack domain knowledge about the organization of healthcare, but somehow got pulled into a healthcare claims data project.

The instructor of this course is Dennis Arrindell, MSc., MBA. Dennis has a bachelor’s degree in Public Health, a master’s degree in Health Economics and a Master’s degree in Business Administration.

Upon completion of this course, students will be able to contribute significantly towards making healthcare organizations (providers and payers) more data driven.

What this course is NOT about:

– Although we will be applying some important statistics and machine learning concepts, this course is NOT about statistics or machine learning as a topic on itself.

– Although we will be using multiple software tools and programming languages for the practical parts of this course, this course is NOT about any of these tools (Excel, SQL, Python, Celonis for process mining) as topics on themselves.

Introduction

1
Claims Data Defined - Data Science for Healthcare Claims Data
2
Why analyze healthcare claims data
3
Who this course is for

Theory of Healthcare systems

1
The four functions of any healthcare system
2
The three key actors in claims data
3
Vertical integration of healthcare system functions
4
Healthcare systems quiz

Test your knowledge about healthcare systems by taking this quiz!

Healthcare provider payment systems

1
Introduction to healthcare provider payment systems
2
Fee-for-service
3
Capitation
4
Bundled payments
5
Global budgets
6
Summary of healthcare provider payment systems
7
Healthcare provider payment systems

take this quiz and test your knowledge about healthcare provider payment systems!

Theory of claims data

1
The two core challenges for healthcare payers
2
Fact tables and dimension tables
3
Authorisation signals

Merging healthcare claims data

1
Introduction to merging data
2
Merging data from a data warehouse
3
Merging an episode of care

Higher level categorization

1
Introduction to higher level categorization
2
Consult the data dictionary
3
Consult the dimension tables
4
(Re)Discover the underlying logic of codes
5
Use existing hierarchies of (inter)national coding systems
6
Ask a domain expert
7
Summary of higher level categorization
8
Higher level categorization quiz

Test your knowledge about this section by taking this quiz

Practice dataset

1
Get all the relevant datasets used throughout this course here

Basic exploration of healthcare claims data

1
Getting started with the practice dataset
2
Basic filtering of data in Excel
3
Introduction to pivot tables
4
Working with a pivot table in Excel
5
Selecting aggregations in a pivot table
6
Grouping by date in a pivot table
7
Using a pivot table to create and control a chart
8
Vertical look-up part 1: Exploring the look-up table in Excel
9
Vertical look-up part 2: Applying the function
10
Vertical look-up part 3: Filling down the results
11
A note on filling down in Excel
12
Vertical look-up part 4: Finalizing the dataset
13
Benefit of introducing categories in claims data

Extract, Transform and Load (ETL) from the data warehouse using SQL

1
Relational data schema
2
Getting started with Google Big Query
3
Introduction to SQL in Google Big Query interface
4
Writing a simple SQL script to extract healthcare claims data
5
Merging data using SQL
6
Visualizing the data in Big Query
7
Calculating the age of the patient at the time of knee replacement
8
Confirming the correct code using the where clause and a regular expression
9
Inspecting the compatibility between the tables
10
Concatenate and cast data to allow compatibility
11
Create a subquery
12
Date difference function to calculate age

Absolute and relative comparisons

1
Absolute and relative comparisons
2
Using a 100% Stacked column chart for relative comparisons
3
Using percentages for relative numbers
4
Per capita calculations using distinct count
5
Using distinct count for relative comparisons in Excel

Process Mining with healthcare claims data

1
Introduction to process mining
2
Benefits of process mining with healthcare claims data
3
Process mining tools
4
Getting started with Celonis Snap
5
Configure the dataset for process mining
6
Introduction to process mining with Celonis Snap part 1
7
Introduction to process mining with Celonis Snap part 2
8
Discover patient pathways using process mining (part 1)
9
Discover patient pathways using process mining (part 2)
10
Isolate a sub process by focussing on the sub process spider activity
11
Introduction to specifying a sequence order
12
Theory of sequence order when dealing with identical timestamps
13
A note about specifying a sequence order
14
Manipulating the raw data to specify a sequence order (part 1)
15
Manipulating the raw data to specify a sequence order (part 2)
16
A note about concatenation
17
Confirm the correct sequence in a new process map
18
Detect anomalies by comparing the processes of different providers
19
Moving from process mining to statistics and machine learning

Proxy diagnosis

1
Proxy diagnosis
2
Method for obtaining a proxy diagnosis
3
Why use a subquery for proxy diagnosis
4
Querying healthcare consumption of diabetics using a proxy diagnosis
5
Identify insuline users (diabetics)
6
Use identified diabetics to capture their full episode of care
7
Capture the episode of care for patients undergoing a total knee replacement

Tidying healthcare claims data

1
Tidying healthcare claims data with Excel
2
Converting the target variable to a binary field with Excel
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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7 hours on-demand video
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Certificate of Completion
Data Science for Healthcare Claims Data
Category:
Business
Teacher
Daniel W
Price:
$138.98 $109
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