CASE STUDY: Combining Machine Learning and Business Rules to Put Patients at the Center at Kaiser Permanente

June 2, 2019

Prerequisite: None

David R. Herring

Director of Application Engineering for Business Automation

Kaiser Permanente

Kaiser Permanente is ideally poised to develop data-driven services that forecast critical behaviors in clinics. Historical data can drive predictions and simulations for a plethora of scenarios, ranging from scheduling and forecasting to utilization, and can then offer real-time advice that leads to better patient outcomes. Using the schedule of MDs required to staff an urgent care clinic as an example, this presentation will show how Kaiser ingests historical data into a machine learning platform, analyzes this data to predict future member loads, and then uses business rules to simulate best-practice Member Wait Times and MD utilization outcomes.

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TDWI Solution Summit Coronado

Coronado Island
Coronado, CA
June 2–4