Strategic Objectives
• Identify high-risk populations using advanced geospatial and economic modeling.
• Integrate non-medical data into clinical workflows for holistic patient care.
• Leverage machine learning to predict health crises before they occur.
• Optimize resource allocation by addressing root-cause social vulnerabilities.
The Core Challenge
Traditional healthcare overlooks the 80% of health outcomes driven by non-clinical factors, leading to reactive treatments and systemic inequities.
01
The Foundation of SDoH
02
The Economic Engine
03
Housing as Healthcare
04
The Education Gradient
05
Nutritional Landscapes
06
Environmental Exposure
07
The Power of Connection
08
Transportation Barriers
09
Data Sources for SDoH
10
Geospatial Analytics
11
Predictive Modeling Basics
12
Machine Learning in Public Health
13
Algorithmic Bias
14
Interoperability and Standards
15
Risk Stratification
16
Community Health Needs Assessment
17
Policy and Advocacy
18
The ROI of Social Care
19
Privacy and Ethics
20
Future Frontiers
21