Population risk stratification has, so far, been biased toward structured data due to accessibility issues. As interest in long-term member wellness increases in importance it is the insights trapped in unstructured data that will become the differentiator in a changing and competitive market. The payers who are able to characterize member groups at a fundamentally more detailed level will have the advantage of population insight over those who struggle to do so.
Data sources that are increasing in scale and availability include electronic healthcare records (EHRs) data in Continuity of Care Document (CCD) format from providers, OCR notes about members, and nurses’ notes.
How can payers make effective use of unstructured data to stratify populations more effectively when much of their infrastructure is tied to structured data? Sources of unstructured data contain significantly more detail about members but are much more varied.
Here at Linguamatics Health, our Clinical NLP specialists understand the urgency and complexity of bringing together data sources, both structured and unstructured, in a workflow that gets you to insights you need quickly.
We also know the pain and cost of deploying solutions that either don’t integrate well into existing tools and processes; or take too much time and effort to deploy and learn. Linguamatics Health is designed to hit the ground running, indexing unstructured documents and immediately extracting the relevant and useful data.
We just published a case study on the work we are doing with a top healthcare payer.
This case study illustrates:
- How this workflow gathered and delivered insights from unstructured data to support risk stratification analytics for Congestive Heart Failure (CHF)
- How NLP text mining was successfully integrated into the Payer’s existing Hadoop and Netezza systems
- How this integration and workflow were made usable quickly – minimizing the time taken to deploy the solution so that the payer swiftly started getting value
- How this infrastructure is easily applied to supporting other disease areas in addition to CHF, including COPD, diabetes and obesity
Find out more – all you need to do is fill in a short webform or visit our population health management and analysis page:DOWNLOAD CASE STUDY