Health Data 101

An Introduction to Health Data for data analysts

What you'll learn
Introduction to Health Data - sources, types, uses
Introduction to Diagnosis, medical procedure, drug, laboratory codes
Features of health data that enhance analyses
Issues with health data and how to practically handle these
Requirements
No
Though some experience working in a clinical or health insurance setting could further contextualize the content
Description
This is an introductory course for health data, from the perspective of data analysts.Health data connects complex health care systems. An understanding of health data is fundamental to health analytics.The content is pitched at entry level health data analysts.Through this course, you will gain a highly valuable skill in the healthcare sectorunderstand how health data records information about each patient and medical encounterlearn a few features of health data that enable you to perform more insightful analysesbe able to communicate more effectively with clinical and analytic colleaguesbe empowered to improve care processes and make a difference to many people’s health and livesThe 4 sections we will cover Where health data come from: 5 main sources including health insurance claims and EHRWhat health data look like: Structured and Unstructured dataFeatures of health data: Hierarchical structures, Disease etiology, chronology, supply vs demandIssues of health data: Gaps, Errors, and how to practically deal with theseNEW!!! 2 Bonus Sections from my Predictive Modeling course on Planning and Getting buy in for an analysis.

Overview

Section 1: Where Health Data Come From

Lecture 1 Introduction

Lecture 2 Health Insurance Claims

Lecture 3 Electronic Health Records

Lecture 4 Research Reports

Lecture 5 Public Health

Lecture 6 Wearables

Section 2: What Health Data Look Like

Lecture 7 Structured Data

Lecture 8 Unstructured Data

Section 3: Features of Health Data

Lecture 9 Hierarchical structure - Diagnoses

Lecture 10 Hierarchical structure - Drugs

Lecture 11 Hierarchical structure - Procedures

Lecture 12 Hierarchical structure - LOINCs and other

Lecture 13 Disease Etiology, Chronology, Supply vs Demand

Section 4: Issues of Health Data

Lecture 14 Gaps

Lecture 15 Errors Examples/Corrections & Data Use Considerations

Lecture 16 Completion and Wrap Up!

Section 5: Bonus sections from my Predictive Modeling course

Lecture 17 Planning the analysis

Lecture 18 Getting Buy in for the analysis

Data Analysts,Medical Office Practice Managers,Anyone who works with health data,Clinical Coders

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