Abstract

Objective: To synthesize recent evidence (2022–2025) on data-driven human resource (HR) strategies and present a conceptual framework linking HR analytics to workforce planning, talent management, operational efficiency, and patient outcomes in healthcare.
Methodology: A conceptual-analytical review was conducted using the keywords “data-driven HR,” “people analytics,” and “workforce analytics in healthcare.” High-quality peer-reviewed studies from 2022–2025 were selected and coded into themes. NVivo 14 facilitated thematic coding; Microsoft Excel (2022) and IBM SPSS 29 supported descriptive synthesis and cross-study comparison.
Results: Four themes emerged: workforce planning, talent management, technological enablers, and implementation challenges. Predictive staffing and turnover-risk analytics improved workforce allocation and retention. Competency mapping and analytics-informed training enhanced skill alignment and care quality. AI-enabled scheduling reduced burnout and improved patient safety. Key barriers included data integration, governance, algorithmic bias, and limited HR analytics capacity. Even simple dashboards in resource-limited settings improved workforce deployment.
Conclusion: Data-driven HR is a strategic enabler of resilient, efficient, patient-centered healthcare systems. Adoption requires leadership commitment, ethical governance, and capacity building. These practices are scalable and adaptable, offering actionable solutions for both high- and low-resource healthcare settings.

Key words: Data-driven HR, healthcare management, workforce analytics, artificial intelligence in HR.