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Healthcare Data Healthcare Data

What Is Healthcare Data?

Healthcare data is a huge field: any data related to drug discovery, clinical trials, patient records maintenance, clinician assistance software, and demographic and population trends. This data also closely interacts with different data categories, like natural disaster data, insurance claims data, and geospatial data.

Where Does Healthcare Data Come From?

Healthcare data mainly comes from clinician records and EHR/EMR data. Governmental institutions also collect healthcare or healthcare-related data like natural disasters, climate and weather data, and more. Other companies collect related data like billing or collections agencies or pharmaceutical sales companies.

Additional sources of this data are health surveys and NLP tools.

What Types of Columns/Attributes Should I Expect When Working with This Data?

The massive amount of healthcare data available and the immense number of uses for the information depends on the specific use. For example, a dataset about prevalence of Autism Spectrum Disorders in a country would likely include a column for prevalence in the larger population.

Many healthcare databases are image-based as radiologists, MRI and PET scan technicians use machine learning programs to provide diagnoses.

What Is This Data Used For?

Professionals of all sorts use this data. Clinicians and other healthcare professionals use the data to improve patient care and manage their facilities. Pharmaceutical companies and researchers use the data to develop drugs and treatments. Non-profits and governmental institutions use healthcare population data, along with related climate or geospatial data, to track and reach vulnerable populations. Insurance companies and collections agents also need this information to perform their tasks—without violating patient privacy protections.

How Should I Test the Quality of Healthcare Data?

This data is difficult to test because of the massive amount of it and the confounding valences of any single data point. For example, a single patient may have multiple diagnoses or take multiple medications, the effects of which may complicate any conclusions you can draw from the datasets. Scaling this up to populations of entire cities or counties makes this exponentially harder.

Additionally, local policies restrict the data’s publication or use, which may frustrate data enthusiasts.

However, there are still ways to test the quality of the data, especially with programs in Hadoop and Apache Spark. To ensure the data is complete and accurate, focus on data cleansing and  updating the sources.

What Are the Most Important Factors I Should Vet when Selecting This Data?

It is most important to be sure that the data gathered follows all regulations and laws regarding collection and reporting of healthcare data.

Once confirmed, take care to compare incoming data values to a set of values that you have confirmed are valid.

Interesting Case Studies and Blogs to Look Into

CDC: Absenteeism in the Workplace | NIOSH
Artificial Intelligence-Powered Oncology Software

Tangible Examples of Impact

In September 2017, the FDA decided to allow digital imagery from whole slide scanners to become a primary diagnostic tool in addition to glass slides and frozen tissue specimens. This decision has created a large-scale data mining problem for insurance companies, hospitals, and big pharma. Right now, millions of digital images are not analyzed because of a lack of appropriate tools.

Healthcare Artificial Intelligence Value Proposition: A White Paper

Healthcare Data categories (9)

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