IMPACT OF ARTIFICIAL INTELLIGENCE ON CLINICAL DECISION-MAKING AND SUPPORT SYSTEMS IN HOSPITAL ENVIRONMENTS

Abstract

Background: The growing complexity of clinical practice and demand for high-quality care have amplified the need for advanced decision-making tools in hospitals. Artificial Intelligence (AI) has emerged as a transformative force in enhancing Clinical Decision Support Systems (CDSS), with the potential to improve accuracy, efficiency, and outcomes in healthcare delivery.

Objective: To assess the impact of integrating AI into CDSS on clinical decision-making quality, workflow efficiency, patient outcomes, and clinician satisfaction in tertiary hospital settings.

Methods: This quasi-experimental study was conducted over eight months in two tertiary care hospitals in Lahore. A total of 150 clinicians were divided equally into pre- and post-implementation groups. Data collection tools included the Clinical Decision Quality Score (CDQS), time-motion analysis for workflow efficiency, hospital records for patient outcomes, and the modified Technology Acceptance Model (TAM) for clinician satisfaction. Statistical analysis included paired t-tests, chi-square tests, and repeated measures ANOVA to evaluate normally distributed data.

Results: Post-implementation of AI-CDSS, the mean CDQS significantly improved from 78.4 to 86.9 (p < 0.001). Decision time per patient and turnaround time reduced notably (p < 0.001), while actioned alerts increased from 63% to 82% (p < 0.01). Patient outcomes also improved, with reductions in 30-day readmission rates (16.4% to 11.1%), average hospital stay (5.7 to 4.9 days), and adverse events (11.8% to 7.3%). Clinician satisfaction scores showed significant enhancement across all TAM dimensions (p < 0.001).

Conclusion: AI integration into CDSS demonstrably improved decision accuracy, clinical workflow, patient safety, and provider satisfaction, advocating its broader implementation in hospital settings.