Is simple better? Comparing Computational Cost and Carbon Impact of Machine Learning Models for Traumatic Brain Injury Prediction; A Case Study for Sustainable Digital Health Implementation

Background Machine learning (ML) models for traumatic brain injury (TBI) prediction increasingly demand extensive data, computational resources, and energy consumption, yet simpler models may offer comparable clinical benefit with lower barriers to deployment. This study compares predictive performance, computational efficiency, carbon footprint, and real-world feasibility of resource-efficient ("pauci-parameter") versus data-intensive ("multiparameter") ML models for predicting TBI patient care pathways and outcomes. Methods External validation study in a level 1 trauma center (n=534 adult TBI patients with GCS<9 and/or intracranial injuries). Seven models tested: two pauci-parameter models using only routine prehospital variables (PREHOSP, 15 variables) or CT image analysis (CT-TIQUA), and five multiparameter models integrating clinical and imaging data. Primary outcome: positive likelihood ratio for predicting neurocritical care intensity, mortality (7/30-day, 6-month), and functional outcome (Glasgow Outcome Scale Extended). Secondary outcomes: computation time, carbon footprint, clinical implementability. Results Multiparameter models showed superior performance but did not consistently translate to better clinical utility. PREHOSP (pauci-parameter) showed comparable performance to complex models for most outcomes. The best-performing multiparameter model (MULTI-PRE) required 100-fold longer inference time and 10-fold higher carbon emissions per prediction versus simple models, while net clinical benefit was nearly identical (0.06 vs 0.05). Models using only prehospital data demonstrated greater generalizability and lower deployment barriers. Interpretation Computational complexity and resource intensity should factor equally with predictive performance in clinical AI deployment decisions. For sustainable digital health implementation--especially in resource-limited settings--simpler models with comparable clinical benefit may enable broader access while reducing environmental and financial costs.