Traumatic Brain Injury Prognostication

Inventor:
Tanya Egodage, MD – Associate Professor of Surgery, Associate Program Director, Surgical Critical Care Fellowship, Trauma & Surgical Critical Care 

Overview

A software-based clinical decision support platform that integrates clinical, imaging, and biomarker data to generate dynamic, real-time prognostic insights for patients with traumatic brain injury (TBI). The platform continuously updates outcome predictions as new patient data becomes available, supporting clinical decision-making across the continuum of care. 

Clinical Need

Accurate prognostication following traumatic brain injury remains a significant challenge due to the complexity of the condition and the fragmented nature of patient data. Existing prognostic tools often rely on limited inputs and provide static assessments that require clinicians to manually reinterpret new information as it becomes available. As a result, valuable clinical, imaging, and laboratory data may be underutilized, limiting predictive accuracy and consistency.

Solution

This platform provides an integrated, software-based platform that continuously aggregates patient data and updates prognostic outputs in real-time. Delivering clinical decision support directly within existing clinical workflows through integration with the electronic medical records including clinical, imaging, and laboratory data.  

Technology

The platform integrates multiple data streams—including physiologic and anatomic clinical data, CT/MRI imaging, and biochemical markers—into a unified predictive model. 

• Multimodal data fusion: Combines disparate clinical inputs into a single analytical framework 

• Continuously updating model: Predictions are recalculated as new data becomes available 

• Clinical system integration: Designed to interface with hospital systems electronic health records 

• Machine learning–enabled analysis: Supports dynamic prognostication rather than static scoring systems. 

Advantages

• Direct integration into existing clinical workflows
• Broader data integration than traditional prognostic tools
• Continuously updated outcome predictions
• Supports improved patient monitoring and clinical decision-making
• No additional hardware requirements 

Development

Stage: Clinical Evaluation & Prototype Development 

Opportunities

Collaborations, co-development, licensing, and health system integration partnerships.

Contact

Neal Lemon, PhD, MBA
AVP, Innovation & Technology Commercialization
Cooper University Health Care | Rowan University
lemon-neal@cooperhealth.edu | lemonna@rowan.edu