SOFTWARE BASED CLINICAL DECISION SUPPORT SYSTEMS FOR REDUCING DIAGNOSTIC ERRORS AND IMPROVING TREATMENT ACCURACY

Abstract

Software-based clinical decision support systems help healthcare professionals reduce diagnostic errors and improve treatment accuracy by combining patient data with evidence-based clinical knowledge. This study examines a system that integrates electronic health records, laboratory findings, medical imaging, medication history, symptoms, and clinical guidelines within a unified decision-support platform. The software can identify possible diagnoses, detect drug interactions, flag abnormal results, and recommend tests or treatment pathways. Risk scoring and predictive models support early recognition of complications and help clinicians prioritize high-risk patients. Explainable alerts, confidence scores, and guideline references improve transparency and allow physicians to review recommendations before taking action. Role-based access, data encryption, audit logs, and model validation strengthen privacy, accountability, and reliability. Overall, clinical decision support software can enhance diagnostic consistency, reduce preventable mistakes, support personalized treatment selection, and improve patient safety while preserving the clinician’s responsibility for final medical decisions across hospitals, clinics, and emergency care settings.