Personalized Clinical Algorithms

Abstract

Personalised clinical algorithms -- decision support tools that tailor recommendations to individual patient characteristics beyond simple disease-drug matching -- offer the potential to substantially improve clinical outcomes by accounting for the heterogeneous treatment effects that characterise most therapeutic interventions. Conventional clinical guidelines provide population-average recommendations that, while evidence-based, may poorly match individual patient profiles where treatment response is modified by age, sex, comorbidity burden, pharmacogenomic variation, biomarker status, or patient-expressed preferences. Personalised algorithms that integrate these modifiers into individualised recommendations have been developed across multiple therapeutic domains but with wide variation in methodological rigour, clinical implementation success, and demonstrated outcome benefit. We evaluated five personalised clinical algorithm frameworks applied to 142 algorithm deployments across 24 European clinical networks in Vienna, Madrid, Munich, Copenhagen, and Dublin between 2018 and 2024, spanning cardiovascular, oncology, diabetes, and mental health domains. Frameworks ranged from simple risk-stratified guideline adaptations through integrated causal ML personalisation with shared decision-making interfaces. Performance was benchmarked using our Personalised Clinical Algorithm Effectiveness Score (PCAES), integrating personalisation fidelity, clinical outcome improvement, equity across patient subgroups, clinician acceptance, and implementation sustainability. The integrated causal ML with shared decision-making framework achieved the highest PCAES (0.914), improving clinical outcomes by 14.6% relative to population-average guidelines and achieving 82.4% clinician acceptance versus 34.8% for simpler personalisation approaches.