Data Science for Healthcare Claims Data

Learn and practice how to transform raw healthcare claims data into valuable knowledge and actionable insights!

Created by: Dennis Arrindell
Data-driven Health Economist

What Will I Learn?

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

Requirements

• The components of this course that are dedicated to improving domain knowledge in the area of healthcare claims data are at beginner level. The components of this course that are dedicated to the application of data science tools on healthcare claims data are at an intermediate level. Some experience with transforming raw administrative data into actionable insights will be useful, but is not necessary.
• Students will need a computer with access to internet in order to perform the practical parts of this course.
• The majority part of this course does not require prior knowledge. However, a limited amount of parts of this course consists of data processing and analyzing using Excel, SQL, Python and business intelligence dashboards. For these parts only, prior knowledge and or familiarity with these tools is helpful, as this course may not provide you with enough information to learn these tools from scratch. This is due to the fact that the focus of this course is on the application of these tools to healthcare claims data, and not necessarily on learning how to work with these tools. There are many learning resources specifically dedicated to learning these tools. One of the concepts however, namely process mining, will be explained in more detail, because the learning resources about that field are relatively scarce. For process mining, we will be using Celonis Snap.
• If you are already very familiar with machine learning in Python, the part of this course dedicated to machine learning may feel oversimplified. However, the purpose of this course is not to teach machine learning in Python. The main purpose of these parts is to showcase how we can transform raw healthcare claims data in such a way that we can apply meaningful machine learning techniques on it. This is also known as feature engineering.

Target audience

• 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.