Transforming healthcare workforce planning through AI-augmented ERP Systems: A predictive analytics framework

This article examines the integration of Artificial Intelligence and Enterprise Resource Planning systems to transform workforce planning in healthcare environments. Traditional workforce planning in healthcare relies on historical averages and reactive approaches, creating inefficiencies in resource allocation and staff utilization. The article presents a framework for enhancing ERP systems with AI-driven predictive capabilities that leverage consolidated data from human resources, clinical operations, and scheduling modules. Through a detailed case study at an academic medical center, the implementation demonstrates significant improvements in forecasting accuracy, resource distribution, cost management, and staff satisfaction. The framework balances algorithmic capabilities with human oversight, establishing appropriate governance structures and change management protocols. Critical success factors include data readiness, leadership commitment, stakeholder engagement, and phased implementation approaches. The article discusses limitations in current implementations and future research directions while exploring broader implications for AI-human collaboration in complex decision environments beyond healthcare.

Authors

Publication Details

Journal
World Journal of Advanced Engineering Technology and Sciences
Published
2025-06-24
DOI
https://doi.org/10.30574/wjaets.2025.15.3.1145
Primary Topic
AI and HR Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Transforming healthcare workforce planning through AI-augmented ERP Systems: A predictive analytics framework

Kamini Yadav
World Journal of Advanced Engineering Technology and Sciences
AI and HR Technologies
article

Transforming healthcare workforce planning through AI-augmented ERP Systems: A predictive analytics framework

Kamini Yadav
article en

Abstract

This article examines the integration of Artificial Intelligence and Enterprise Resource Planning systems to transform workforce planning in healthcare environments. Traditional workforce planning in healthcare relies on historical averages and reactive approaches, creating inefficiencies in resource allocation and staff utilization. The article presents a framework for enhancing ERP systems with AI-driven predictive capabilities that leverage consolidated data from human resources, clinical operations, and scheduling modules. Through a detailed case study at an academic medical center, the implementation demonstrates significant improvements in forecasting accuracy, resource distribution, cost management, and staff satisfaction. The framework balances algorithmic capabilities with human oversight, establishing appropriate governance structures and change management protocols. Critical success factors include data readiness, leadership commitment, stakeholder engagement, and phased implementation approaches. The article discusses limitations in current implementations and future research directions while exploring broader implications for AI-human collaboration in complex decision environments beyond healthcare.

World Journal of Advanced Engineering Technology and SciencesVol. 15(3)
Openalex Percentile: Top 83%
AI and HR Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Transforming healthcare workforce planning through AI-augmented ERP Systems: A predictive analytics framework — Kamini Yadav · World Journal of Advanced Engineering Technology and Sciences (2025) | TGRS Research Map | TGRS