Greater Philadelphia Business Coalition on Health - Predictive Modeling Tool for Employer Disease Burden and Intervention Strategies

GPBCH Background Established in 2012, the Greater Philadelphia Business Coalition on Health (GPBCH) is a employer led membership organization more than 50 employer members (including Aramark, City of Philadelphia, Comcast NBCUniversal, Five Below, IBX, Merck & Co., and Pfizer) that represent 800,000 lives locally and 1.65 million lives nationally.

GPBCH’s mission seeks to increase the value of health benefit spending for the region’s employers by improving workforce and community health, increasing healthcare quality and safety, and reducing health care costs.

Project Abstract

Employers across the country are facing increasing financial pressure as chronic diseases drive higher medical, disability, and workers’ compensation costs while simultaneously reducing workforce productivity. Conditions such as diabetes, cardiovascular disease, and cancer account for a significant share of this burden. Yet, decades of evidence from the Centers for Disease Control and Prevention (CDC), state health departments, and leading organizations such as the American Diabetes Association, American Heart Association, and American Cancer Society show that prevention, early detection, and effective management can meaningfully improve outcomes.

This internship project focuses on developing a predictive, data-driven tool that helps employers understand the potential disease burden within their workforce, their community, and identify actionable strategies to mitigate risk and improve employee health.

Project Objectives

The intern will:

· Catalog Public Health Data Sources: Build an inventory of credible datasets—federal, state, and nonprofit—that quantify disease prevalence at granular geographic levels (zip code, town, county). Our initial priority will be focusing on the state of Delaware data and employers.

· Identify Employer-Relevant Interventions: Compile a structured list of evidence-based tools, programs, and resources employers can deploy to address specific chronic conditions.

· Develop an AI-Enabled Predictive Algorithm: Create a model that integrates demographic characteristics of an employer’s population with external disease prevalence data to estimate potential disease burden. GPBCH is open to the ideal technological solution proposed by the student group, which may include using Artificial Intelligence in the development of this tool.

· Match Burden to Solutions: Design logic that aligns predicted risks with appropriate employer interventions, benefits programs, and community resources.

· Build an Employer-Facing Interface: Develop a clear, compelling output format—such as dashboards, visual summaries, or reports—that communicates findings and motivates employer action.

Expected Impact

The final tool will equip employers with actionable insights to guide investment in prevention, early detection, and chronic disease management—ultimately improving worker health while reducing long-term costs.