Early Warning at Scale: Development and Validation of a Predictive Model for Patient Deterioration across an 8,000-Staff Regional Referral Network
DOI:
https://doi.org/10.47941/ijhs.4046Keywords:
Patient Deterioration, Early Warning Score, Machine Learning, Clinical Decision Support, Regional Referral Network, Implementation ScienceAbstract
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.
Findings: Rule-based scores such as NEWS2 offer limited sensitivity and specificity relative to machine learning tools, but most evidence comes from large academic centers and integrated systems. The framework shows how such a system can be sized for a multi-campus referral network, routing escalating risk to nurses, rapid response teams, and physicians while limiting alarm fatigue. No empirical patient-level outcomes are reported.
Unique Contribution to Theory, Policy and Practice: For theory, the paper integrates technical model design, fairness evaluation, and implementation science into a single development framework. For practice, it provides a literature-grounded blueprint and evaluation protocol that mid-sized referral networks can use to plan deployment. For policy, it supports pre-specified, transparent validation and subgroup performance auditing before clinical prediction models are deployed.
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Copyright (c) 2026 Rajesh Babu Kodali, Sailesh Kotha
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