Session ID: 
BG5

Adding Community-level Social Determinants of Health Factors to Patient-level Data to Predict Stroke Risks

12:30pm - 1:30pm Monday, March 9
Orlando - Orange County Convention Center
W308A
Additional Registration
Extra Fee

Description

Applying big data and analytics to timely and accurately predict prevalent acute diseases such as stroke can significantly reduce health care spending and improve patient outcomes. This session explores insights from a study on whether and how we can improve our ability to distinguish between stroke and stroke-like conditions ('stroke mimics') at hospital admission. Machine learning (ML) models analyzed data from 2012-2014 patient discharge records and patient-level hospitalization data from all Florida hospitals, and social determinants of health (SDOH) information. Analysis shows that ML models can significantly improve the predictive accuracy of stroke diagnosis where the most important predictors are a patient's age, number of chronic conditions, and comorbidity index. While SDOH are associated with stroke incidence and severity, adding community-level SDOH factors contributes minimally to the diagnosis prediction.  Explore how developing and integrating individual-level SDOH screening tools into EHRs to improve patient risk assessment and prevention.
This session is part of a special program called Big Data Symposium. Extra fees and separate registration is required.
This session is part of a special program called HIMSS20 Big Data Symposium: Making it Work. Extra fees and separate registration is required.

Learning Objectives

  • Evaluate the extent to which data available only at admission can be used to provide a relatively reliable prediction of acute disease diagnosis, such as stroke, and which type of information is the most valuable
  • Assess the value of adding SDOH to the prediction of disease incidence and outcomes
  • Leverage SDOH data to achieve a more accurate risk assessment, and ultimately, better performance for healthcare providers and better outcomes for population health

Speaker(s)

Assistant Professor,
Florida International University
Professor, Mgmt. Sci. & Healthcare IS,
Carnegie Mellon University

Continuing Education Credits

CAHIMS
1.00
CME
1.00
CNE
1.00
CPHIMS
1.00

Audience

CIO/CTO/CTIO/Senior IT
Data Scientist
Technologist

Level

Intermediate