Session ID: 

Clinical Analytics Prediction Engine (CAPE)

12:00pm - 1:00pm Friday, February 15
Orlando - Orange County Convention Center


NorthShore has a long history implementing predictive models into clinical workflows by identifying individuals at risk of targeted outcomes embedded in the EHR. However, these models have produced an interesting problem where each predictive model was creating confusion as clinicians were presented with different scores without a prescription for what to do about it. Our vision was to integrate the disparate models into an engine to produce a unified lexicon to identify and communicate patients at high risk of multiple outcomes across the continuum of care, segment patients into sub-populations that share risk profiles and intervention amenability, cascade that into a coordinated set of interventions and care pathways to appropriate care teams and rapidly learn from these interventions. CAPE has produced reductions in mortality, readmission and per-capita spending and is the genesis of a learning health system at the intersection of clinical analytics and integrated care delivery.

Learning Objectives: 

  • Define, clearly, the problem with healthcare’s current state of predictive modeling implementations and how they often fail to support clinical workflows and describe the CAPE framework for how to bring multiple predictive models into a single prescriptive engine
  • Describe an inventory of key patient outcomes to predict and how to achieve a high accuracy for prediction including both retrospective and prospective validation processes
  • Demonstrate the importance of tightly integrated predictive models into the EHR using real-time processing via the Predictive Model Markup Language (PMML), including implications for displaying the results and risk factors of a model to front-line clinicians
  • Discuss the implications of a learning health system and how CAPE can help to achieve a better understanding of the "impactability" of patient populations based on multiple risk models and propose specific intervention bundles catered to the needs of those sub-populations
  • Discuss the key cultural implications that an integrated predictive engine is able to facilitate and how it can enable the care team to improve patient outcomes while lowering costs


Assistant Vice President, Clinical Analytics,
Northshore University Healthsystem
Infectious Disease Physician,
Northshore University Healthsystem


Chief Quality, Chief Clin Transformation Officer