Early risk stratification of COVID-19 severity in oncology patients is critical for improving clinical outcomes and optimizing hospital resource allocation. This study proposes a rule-based clinical decision support system (CDSS) designed for integration into digital triage workflows. In practical terms, the score is intended to be applied at hospital admission or triage, where demographic and comorbidity information is routinely available. The computed score can automatically flag high-risk oncology patients for intensified monitoring or early ICU evaluation, supporting rapid resource allocation while preserving clinician decision-making. Using retrospective clinical data from hospitalized oncological patients with confirmed SARS-CoV-2 infection, we developed a scoring algorithm based on four common comorbidities: age ≥ 70, obesity, diabetes mellitus, and hypertension. Each factor was assigned a weighted contribution to a cumulative score ranging from 0 to 7. Patients were classified into three risk levels (low, moderate, high), correlating with observed rates of ICU admission and mortality. The system is built for low-complexity implementation in electronic health records (EHRs) or web-based triage dashboards and includes a software logic model with pseudocode. Results indicate that the score effectively distinguishes patient risk levels with statistical significance (p < 0.01), and can function as an early triage mechanism. The proposed model does not require laboratory data or imaging, making it particularly suitable for rapid deployment in both hospital and remote settings. This work demonstrates a pragmatic, interpretable, and scalable approach to clinical decision support in pandemic contexts involving vulnerable populations such as cancer patients.
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