INTELLIGENT HEALTHCARE SOFTWARE FOR IMPROVING PATIENT MONITORING, CLINICAL WORKFLOW EFFICIENCY, AND HOSPITAL DECISION SUPPORT

Abstract

Enterprise systems manage workflows for finance, procurement, HR, inventory, customer service, compliance, and reporting, but failures often occur when dependencies are delayed, approvals are incomplete, data validation fails, or workload pressure increases. Traditional workflow monitoring usually detects failure after a task is already blocked or escalated. This article proposes a predictive workflow failure analysis framework for enterprise systems. The framework analyzes task sequence, user workload, approval timing, validation errors, dependency status, exception history, transaction value, and process deadlines to predict likely workflow failure. It supports early intervention, bottleneck prevention, exception reduction, and process reliability improvement. Expected results suggest reduced failure rates, faster recovery, and better workflow continuity compared with reactive monitoring. The key benefit is earlier visibility into workflow disruption risk.