Explainable AI for Linking Clinical Deterioration Patterns with Escalating Hospital Treatment Costs Early

Author

Johnbull Anderson

Abstract

 Healthcare systems increasingly require methods that identify clinical deterioration before worsening illness produces avoidable treatment escalation, prolonged hospitalization, and rising expenditure. Conventional early-warning scores and cost analyses often examine clinical risk and financial outcomes separately, limiting understanding of how evolving patient conditions translate into resource-intensive care. This study proposes an explainable artificial intelligence framework for linking longitudinal clinical deterioration patterns with escalating hospital treatment costs early in the care pathway. The approach integrates demographic characteristics, comorbidities, vital signs, laboratory trends, medication changes, diagnostic activity, complications, ward utilization, intensive-care exposure, procedures, and patient-level cost data. Predictive models estimate deterioration and subsequent expenditure, while explainability methods quantify the contribution, direction, and temporal importance of individual clinical and utilization features. By identifying when and why predicted costs increase, the framework distinguishes expenditure associated with genuine clinical complexity from potentially modifiable escalation in resource use. Patient-level stratification supports early clinical review, targeted intervention, capacity planning, and financial forecasting. The framework positions explainable AI as decision support that connects clinical trajectories with economic consequences without replacing clinician judgment or implying causality from predictive associations.

Keywords

Explainable Artificial Intelligence; Clinical Deterioration; Hospital Treatment Costs; Predictive Analytics; Healthcare Resource Utilization; Early Risk Stratification

DOI : https://doi.org/10.5281/zenodo.22767565

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References

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