Evaluating the Effectiveness of Artificial Intelligence–Based Clinical Decision Support in Improving Early Disease Detection and Patient Care Outcomes

DOI: 10.66715/jirf/2026.v1.i2.222228 | Research Paper | 2026 | Volume 1 | Issue 02 | Page 222-228

Mahendra Pratap Swain, NDF-SRF-AICTE, Department of Pharmaceutical Sciences and Technology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India 835215

Evaluating the Effectiveness of Artificial Intelligence–Based Clinical Decision Support in.

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Abstract

Background: Artificial Intelligence (AI)–based Clinical Decision Support (CDS) systems have emerged as transformative tools in modern healthcare, offering the potential to augment clinical reasoning, enhance diagnostic accuracy, and accelerate early disease detection. However, evaluating their real-world clinical effectiveness across diverse healthcare settings remains critical for guiding implementation and improving patient outcomes.

Objective: This study aims to evaluate the effectiveness of AI-based Clinical Decision Support systems in improving early disease detection rates, reducing diagnostic errors, and optimizing key patient care outcomes within hospital and clinical environments.

Methods: A retrospective and prospective comparative analysis was conducted involving clinical data from adult patients evaluated across multiple healthcare facilities between January 2024 and December 2025. The study compared patient cohorts managed using AI-integrated CDS tools against a control group receiving standard care without AI augmentation. Primary endpoints included the time-to-detection for critical conditions (such as cardiovascular anomalies, oncological markers, and acute metabolic events), overall diagnostic accuracy, length of hospital stay, and 30-day readmission rates. Statistical evaluations utilized multivariate regression and propensity score matching to control for confounding variables.

Results: The implementation of AI-based CDS demonstrated a statistically significant reduction in the time-to-detection for early-stage pathologies, averaging a 28% decrease compared to standard care pathways (p<0.001). Diagnostic accuracy improved by 15.4%, driven largely by enhanced pattern recognition in imaging and electronic health record (EHR) data analytics. Furthermore, patients managed with AI support experienced a shortened average length of stay (by 1.2 days) and a lower 30-day readmission rate, highlighting downstream benefits to overall clinical workflow and patient recovery.

Conclusion: AI-based Clinical Decision Support systems significantly enhance early disease detection capabilities and contribute to measurable improvements in clinical outcomes and operational efficiency. Integrating these tools into routine clinical workflows supports timely, evidence-based interventions, though continuous monitoring and validation remain essential to mitigate algorithmic bias and ensure equitable care delivery.

Keywords: Artificial Intelligence, Clinical Decision Support (CDS), Early Disease Detection, Diagnostic Accuracy, Patient Outcomes, Health Informatics.