Simple Models for Identifying At-Risk Populations

Hospitals using trauma registries have powerful opportunities to identify at-risk populations through simple, actionable data models. Leveraging these platforms' built-in analytics and reporting tools, trauma programs can enhance patient outcomes, target high-risk groups, and align with ACS and TQIP best practices.

Simple Models for Identifying At-Risk Populations

1. Demographic Risk Model — Uses age, sex, payer status, and race/ethnicity to flag vulnerable groups.

2. Clinical Severity Model — Based on ISS, GCS, and mechanisms of injury to prioritize high-severity patients.

3. Process Compliance Model — Examines delays in CT, OR, or VTE prophylaxis to uncover care gaps.

4. Social Determinants Overlay — Integrates registry data with CDC Social Vulnerability Index (SVI) to track equity gaps.

Integrating into Education

These models support training across multiple disciplines:

• MPH Programs — Applying registry data to injury epidemiology and health equity.

• Data Analytics — Teaching risk-adjusted benchmarking and validation pipelines.

• Medical Education — Linking bedside decisions with registry-derived patient risk profiles.

Credits & Acknowledgments This guide integrates insights from ACS Trauma Quality Programs, TQIP, registry professionals, and public health research teams working to improve trauma care and equity outcomes.