Health Data 101
- 1 hour on-demand video
- 15 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- 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
- Though some experience working in a clinical or health insurance setting could further contextualize the content
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
Where health data come from: 5 main sources including health insurance claims and EHR
What health data look like: Structured vs Unstructured data
Features of health data: Hierarchical structures, Disease etiology, chronology, supply vs demand
Issues of health data: Gaps, Errors, and how to practically deal with these
- Data Analysts
- Medical Office Practice Managers
- Anyone who works with health data
- Clinical Coders
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.
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.
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.
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.
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.