The sharing of population data between healthcare providers and payers is critical to maintaining quality care and qualified health plans. However, this data is often messy, consisting of numerous unstructured formats and growing in exponential values. Providers often track hundreds of distinct line items ranging from geographic location to patient ethnicity and spoken language to clinician notes from each individual visit. All of which gets shared with healthcare payers for analysis.
A single patient generates up to 80 megabytes in imaging and EMR data1 alone. With large healthcare payers ranging in 20-40 million customers, manually extracting insights from this content isn’t sustainable.
Analyzing all of this data is required for effective risk assessment, to understand health trends in a given population and adjust plans to deliver the best care possible. Without the right tools this leads to hundreds of hours of research time wasted, dozens of high-paid workers burning out from tedious tasks and missed insight caused by human error.
Advancements in deep learning are revolutionizing how healthcare payers manage, access and extract insights from their most important content. Today’s deep learning models are trained to understand groups of text and the nuance that comes with written language, such as semantically similar terms for the same topic.
Analysts at healthcare payers can apply powerful deep learning solutions from Vyasa to unstructured documents to extract insights such as: cancer screening results, written clinician notes, wellness tests, patient behaviors, etc.
By leveraging deep learning models in their text analytics, healthcare payers can improve research accuracy by 97%. Relevant and accurate data is delivered in milliseconds, meaning research times can be cut by as much as 90%. With access to more efficient processes, and more accurate insight available, healthcare providers can better understand customer needs, stay ahead of the latest healthcare trends and ultimately create smarter, more attractive plans.
1 Huesch, D., and Mosher, T. J. (2017). Using It or Losing It? The Case for Data Scientists Inside Health Care. NeJM Catalyst. Available online at: https://catalyst.nejm.org/case-data-scien- tists-inside-health-care/ (Accessed Jun 20, 2018).