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.
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.
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.
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.
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.
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.
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.
FDNA Telehealth Data helps identify genetic conditions and rare diseases with medical publications data and biometric AI. The program identifies diseases through an initial analysis of facial features in a photo.
FDNA Telehealth makes use of published medical data and a network of hospitals, clinics, and professionals across the nation.
Dell Technologies APEX provides IT and AI solutions, especially for IoT, edge computing, and cybersecurity. They structure workloads and manage data and data analysis for a wide range of uses and industries. Some uses include smart cities, creative work, sustainability, and government solutions; meanwhile, industries particularly suited to APEX include retail, healthcare, utilities, and education.
Pollen Sense Data offers live pollen, mold, & dust counts and forecasts. They can even identify mold and pollen species up to a 30 kms away