Health Data 101
4.5 (13 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
516 students enrolled

Health Data 101

An introduction to Health Data for data analysts
Highest Rated
4.5 (13 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
516 students enrolled
Created by Eddie Jay
Last updated 1/2019
English
English [Auto-generated]
Current price: $11.99 Original price: $24.99 Discount: 52% off
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This course includes
  • 1 hour on-demand video
  • 15 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Health Data - sources, types, uses
  • 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 course introduces 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.


Through this course, you will

  • gain a highly valuable skill in the healthcare sector

  • understand how health data records information about each patient and medical encounter

  • learn features of health data that enable you to perform more insightful analyses

  • be able to communicate more effectively with clinical and analytic colleagues

  • be empowered to improve care processes and make a difference to many people’s health and lives


The 4 sections we will cover

  1. Where health data come from:  5 main sources including health insurance claims and EHR

  2. What health data look like:         Structured vs Unstructured data

  3. Features of health data:              Hierarchical structures, Disease etiology, chronology, supply vs demand

  4. Issues of health data:                  Gaps, Errors, and how to practically deal with these

Who this course is for:
  • Data Analysts
  • Medical Office Practice Managers
  • Anyone who works with health data
  • Clinical Coders
Course content
Expand all 16 lectures 47:46
+ Where Health Data Come From
6 lectures 13:22

Welcome to Health Data 101. Thank you for choosing my course!

Preview 01:00

Health insurance claims data are a major source of health care data. As long as the the medical services are paid by health insurers, you will see the claims. This is a relative well structured and complete source, although clinical detail can be lacking at times.

Preview 02:09

Electronic health records are another major source of health data. EHR systems enable medical professionals to record information about patient visits. Clinical richness is the major advantage of EHRs, but accuracy can vary.

Preview 02:19

Research reports contain great health data, usually of a more scientific nature. When designed well, these can offer great insights. But be aware of sample size, biases of and any financial influence on the study.

Preview 02:57

Public health organizations contribute enormously to health data, especially for prevalence and incidence rates. Learn where to find these and know the limitations of each.

Public Health
02:17

Wearables can track numerous measures including heart rate, blood pressure. They hold great promise for health care, but some challenges remain.

Wearables
02:40
+ What Health Data Look Like
2 lectures 05:26

The most pervasive type of data is structured data. These general have (row/column) tabular form with clear linkages across different tables. We illustrate examples of diagnoses, procedures, drugs and laboratory tests here.

Structured Data
03:11

Unstructured data such as clinical notes and medical images, hold great clinical content. These can be analyzed using different set of tools than structured data.

Unstructured Data
02:15
+ Features of Health Data
5 lectures 17:40

Health data often have hierarchical structures. These structures represent layers of intelligence, that enable faster learning, analyses, and reduces impact of small sample size issues.

ICD10 diagnosis codes have very clear hierarchical structure. ICD9 also do, but to a lesser extent.

Hierarchical structure - Diagnoses
04:17

Drug codes, NDCs, are bewilderingly numerous. WHO created a ATC classification that has a clear hierarchical structure, and enables easier, faster and more complete analyses.

Preview 02:36

Procedure codes, reflecting the complexities of medical interventions, exist in a variety of forms, including HCPCS/CPT, ICD10PCS, ICD9CM. These also exhibit a hierarchical structure.

Hierarchical structure - Procedures
03:51

Laboratory test results are codified using LOINC codes. The analysis of unstructured data also relies on creating hierarchy and structure from the unstructured forms.

Hierarchical structure - LOINCs and other
02:23

Disease Etiology, Chrology concepts enables you to do more insightful and more impactful analyses. Demand drives supply in healthcare.

Disease Etiology, Chronology, Supply vs Demand
04:33
+ Issues of Health Data
3 lectures 11:18

Gaps in data exist, where certain elements of data is completely missing from your datasets. e.g. no drug fill data in EHRs or no data across different medical institutions.

Gaps
05:17

Examples of errors in health data and how to correct these. Pragmatism is key as perfect data does not exist.

A few practical considerations of health data, e.g. accuracy of source, extent of automation.

Errors Examples/Corrections & Data Use Considerations
04:50

Congrats on finishing the course. Feel free to let me know your questions.

Completion and Wrap Up!
01:11