Data Science for Healthcare Claims Data

Data Science for Healthcare Claims Data, available at $84.99, has an average rating of 4.43, with 161 lectures, 5 quizzes, based on 726 reviews, and has 5153 subscribers.

You will learn about In this course, you will learn and practice, how to transform raw healthcare claims data into valuable knowledge and actionable insights. This course is ideal for individuals who are This course is for professionals that are involved with healthcare providers and health insurers that need to generate actionable insights out of the large volume of claims data generated by these organizations. Examples 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. It is particularly useful for This course is for professionals that are involved with healthcare providers and health insurers that need to generate actionable insights out of the large volume of claims data generated by these organizations. Examples 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.

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Summary

Title: Data Science for Healthcare Claims Data

Price: $84.99

Average Rating: 4.43

Number of Lectures: 161

Number of Quizzes: 5

Number of Published Lectures: 161

Number of Published Quizzes: 5

Number of Curriculum Items: 170

Number of Published Curriculum Objects: 170

Original Price: $119.99

Quality Status: approved

Status: Live

What You Will Learn

In this course, you will learn and practice, how to transform raw healthcare claims data into valuable knowledge and actionable insights.

Who Should Attend

This course is for professionals that are involved with healthcare providers and health insurers that need to generate actionable insights out of the large volume of claims data generated by these organizations. Examples 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.

Target Audiences

This course is for professionals that are involved with healthcare providers and health insurers that need to generate actionable insights out of the large volume of claims data generated by these organizations. Examples 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 most commonly available and widely used type of data in healthcare is claims data. Claims data is sometimes also called billing data, insurance 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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to the course

Lecture 2: Claims Data Defined

Lecture 3: Why analyze healthcare claims data

Lecture 4: Who this course is for

Chapter 2: Theory of Healthcare systems

Lecture 1: The four functions of any healthcare system

Lecture 2: The three key actors in claims data

Lecture 3: Vertical integration of healthcare system functions

Chapter 3: Healthcare provider payment systems

Lecture 1: Introduction to healthcare provider payment systems

Lecture 2: Fee-for-service

Lecture 3: Capitation

Lecture 4: Bundled payments

Lecture 5: Global budgets

Lecture 6: Summary of healthcare provider payment systems

Chapter 4: Theory of claims data

Lecture 1: The two core challenges for healthcare payers

Lecture 2: Fact tables and dimension tables

Lecture 3: Authorisation signals

Chapter 5: Merging healthcare claims data

Lecture 1: Introduction to merging data

Lecture 2: Merging data from a data warehouse

Lecture 3: Merging an episode of care

Chapter 6: Higher level categorization

Lecture 1: Introduction to higher level categorization

Lecture 2: Consult the data dictionary

Lecture 3: Consult the dimension tables

Lecture 4: (Re)Discover the underlying logic of codes

Lecture 5: Use existing hierarchies of (inter)national coding systems

Lecture 6: Ask a domain expert

Lecture 7: Summary of higher level categorization

Chapter 7: Relevant resources for this course

Lecture 1: Get all relevant resources here

Chapter 8: Basic exploration of healthcare claims data

Lecture 1: Getting started with the practice dataset

Lecture 2: Basic filtering of data in Excel

Lecture 3: Introduction to pivot tables

Lecture 4: Working with a pivot table in Excel

Lecture 5: Selecting aggregations in a pivot table

Lecture 6: Grouping by date in a pivot table

Lecture 7: Using a pivot table to create and control a chart

Lecture 8: Introduction to vertical lookup

Lecture 9: Vertical look-up part 1: Exploring the look-up table in Excel

Lecture 10: Vertical look-up part 2: Applying the function

Lecture 11: Vertical look-up part 3: Filling down the results

Lecture 12: A note on filling down in Excel

Lecture 13: Vertical look-up part 4: Finalizing the dataset

Lecture 14: Benefit of introducing categories in claims data

Chapter 9: Extract, Transform and Load (ETL) from the data warehouse using SQL

Lecture 1: Background information about the practice data warehouse

Lecture 2: Relational data schema

Lecture 3: A note about the new Big Query Interface

Lecture 4: Getting started with Google Big Query

Lecture 5: Access the Medicare dataset in the new Big Query interface

Lecture 6: Introduction to SQL in Google Big Query interface

Lecture 7: Writing a simple SQL script to extract healthcare claims data

Lecture 8: Merging data using SQL

Lecture 9: Visualizing the data in Big Query

Lecture 10: Calculating the age of the patient at the time of knee replacement

Lecture 11: Confirming the correct code using the where clause and a regular expression

Lecture 12: Inspecting the compatibility between the tables

Lecture 13: Concatenate and cast data to allow compatibility

Lecture 14: Create a subquery

Lecture 15: Date difference function to calculate age

Chapter 10: Absolute and relative comparisons

Lecture 1: Absolute and relative comparisons

Lecture 2: Using a 100% Stacked column chart for relative comparisons

Lecture 3: Using percentages for relative numbers

Lecture 4: Per capita calculations using distinct count

Lecture 5: Using distinct count for relative comparisons in Excel

Chapter 11: Process Mining with healthcare claims data

Lecture 1: Introduction to process mining

Lecture 2: Benefits of process mining with healthcare claims data

Lecture 3: Process mining tools

Lecture 4: Warning! Please read this word of caution before using Celonis

Lecture 5: Getting started with Celonis Free Plan

Lecture 6: Configure the dataset for process mining

Lecture 7: August 2023 update: New interface after file upload

Lecture 8: Introduction to process mining with Celonis part 1

Lecture 9: Introduction to process mining with Celonis part 2

Lecture 10: Discover patient pathways using process mining (part 1)

Lecture 11: Discover patient pathways using process mining (part 2)

Lecture 12: Isolate a sub process by focussing on the sub process spider activity

Lecture 13: Introduction to specifying a sequence order

Lecture 14: Theory of sequence order when dealing with identical timestamps

Lecture 15: A note about specifying a sequence order

Lecture 16: Manipulating the raw data to specify a sequence order (part 1)

Lecture 17: Manipulating the raw data to specify a sequence order (part 2)

Lecture 18: A note about concatenation

Lecture 19: Confirm the correct sequence in a new process map

Lecture 20: Detect anomalies by comparing the processes of different providers

Lecture 21: Moving from process mining to statistics and machine learning

Chapter 12: Proxy diagnosis and cohort analysis

Instructors

Dennis Arrindell
Data-driven Health Economist

Rating Distribution

1 stars: 10 votes
2 stars: 9 votes
3 stars: 76 votes
4 stars: 246 votes
5 stars: 385 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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