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Lincoln's data science teams build analytic products across several Highmark business functions including clinical services and revenue cycle. Those products have leveraged advanced analytics to generate personalized member insights that drive efficiencies and savings for the health plan, providers, and members. Aside from workflow app development, his teams have developed several robotic process automations that work side by side with clinicians. Those bots employ image processing and advanced analytics in their work. Several workflow apps employ real time machine learning and natural language processing to enhance identification and stratification of members. Prior to working at Highmark, Lincoln worked as a data scientist at the UPMC Technology Development Center and then at one of their portfolio companies. He graduated from Rutgers Medical School and received his MS from Princeton University working on the use of real time machine learning to stratify and identify members at risk of various outcomes.
Data analytics are vital to the success for healthcare payers to reduce resources and cost, and improve beneficiary experience while providing better patient care. The majority of that data is unstructured and in many instances difficult to access and analyze. This session will discuss several interesting use cases, opportunities, and challenges that need to be overcome to deliver value to the healthcare payer community.