Early Warning at Scale: Development and Validation of a Predictive Model for Patient Deterioration across an 8,000-Staff Regional Referral Network

Purpose

This paper develops a conceptual framework and validation protocol for a machine learning based early warning system to detect patient deterioration across a regional referral network of approximately 8,000 staff. Methodology: The study is conceptual and methodological. Drawing on the early warning and machine learning literature, it specifies data sources, feature engineering, three candidate models, and a tiered alerting workflow, together with a TRIPOD+AI and PROBAST aligned protocol covering retrospective derivation, silent-mode testing, staged implementation, fairness auditing, and implementation evaluation using the Consolidated Framework for Implementation Research and RE-AIM. The framework is scoped to a network of the scale represented by Vandalia Health and its CAMC Health System campuses in West Virginia.