The introduction of digital technologies has submerged the healthcare industry in vast amounts of data from diverse data sources. According to Information Management, “healthcare data is produced from a large variety of sources such as electronic health records, diagnostics, imaging data, genetic data, clinical records, clinical trials, adverse events reporting, sensors, probes, wearable devices, etc. In 2020, worldwide digital healthcare data is expected to reach 25 exabytes as per a survey in the Nature magazine.”
Such diverse and substantial data offers particular promise for the healthcare industry, because it can help combat deadly diseases, create more personalized treatment programs, and help ensure patients are receiving the best care possible. However, to put the data to use it must be continuously captured, stored, and analyzed, which is quite the challenge.
Healthcare providers are now looking for data-driven solutions to put their data to use and transform the way they operate their business and care for their patients. Not any old data-driven solution will work in today’s world, though. You will need an end-to-end solution that analyzes data in real time as it moves across your entire enterprise. This includes data from other vendors, devices, etc. as well as multiple types of file formats to ensure you’re looking at the entire data picture rather than a small fraction. Such analyzation will help ensure the quality of the data so that the analysis is providing insight you can trust.
When looking for a data-driven solution for the healthcare industry you will want a self-service, big data analytics solution designed to handle not one, but rather multiple steps from data acquisition and preparation to data analysis and operationalization. The solution should come with the capability to analyze data across your enterprise by compiling, cleansing, and consolidating data for analysis.
Users need to be enabled to source data from multiple data platforms and applications within the healthcare industry, such as electronic health records, clinical records, clinical trials, wearables, etc. And then apply statistical and process controls, as well as machine-learning algorithms for segmentation, classification, recommendation, regression and forecasting to provide the information necessary for personalized care. Healthcare providers can then target members for disease management interventions before they’re diagnosed and without clinical staff reviewing medical records. The result is an overall healthier patient population.
To learn more about big data analytics in the healthcare industry, download this data sheet.
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