Healthcare organizations face challenges in workforce allocation because fixed staffing approaches may not adequately reflect changing demand, workflow bottlenecks, staff qualifications, workload, and operational constraints. This study develops an Explainable Process Intelligence framework for Workforce Allocation and Operational Governance in Digital Healthcare Systems to connect healthcare process analysis with transparent staff-to-task recommendations. A quantitative, simulation-based design was used with a simulated healthcare event log and workforce records. The framework integrates process discovery, conformance analysis, bottleneck identification, demand estimation, constrained workforce allocation, explainable artificial intelligence, and governance monitoring. The allocation model was compared with a historical baseline roster using identical demand periods and eligibility rules. The proposed approach increased demand coverage from 78.1% to 88.7%, reduced median waiting time from 1.70 to 1.29 hours, decreased workload coefficient of variation from 0.31 to 0.21, and reduced overtime exposure from 12.4% to 8.9%. Explanations were available for accepted and rejected recommendations, while human reviewers retained authority to modify or reject recommendations. The findings indicate that integrating process intelligence, explainable allocation, and governance can support more transparent workforce planning, workload management, and operational oversight in digital healthcare environments.