Enhancing compensation administration in healthcare: A Workday ERP Perspective

Manoj Varma Lakhamraju *

HR Technology, Teckpros LLC, Charlotte, NC, USA.
 
Research Article
International Journal of Science and Research Archive, 2024, 12(02), 3055-3064.
Article DOI: 10.30574/ijsra.2024.12.2.1147
Publication history: 
Received on 18 May 2024; revised on 20 July 2024; accepted on 23 July 2024
 
Abstract: 
Effective compensation management plays a critical role in healthcare organizations, directly impacting employee satisfaction, retention, and the bottom line of patient care. The complexity of healthcare payrolls due to multiple employee responsibilities, changing schedules, compliance, and employee support poses a major challenge for HR leaders. Traditional payroll processes often lack the flexibility and analytical resources needed to address these issues, leading to poor management and employee dissatisfaction. This article examines the transformative potential of Workday ERP, a cloud-based enterprise resource planning solution, to improve financial management in healthcare. Workday ERP offers a powerful platform equipped with real-time analytics, automation, and centralized data management that enables healthcare organizations to achieve better outcomes and get paid. Unlike traditional systems, Workday’s HCM module combines payroll with performance planning, talent management, and benefits to provide a comprehensive view of employee health and work. By streamlining workflows and automating routine processes, Workday increases the accuracy and fairness of pay models, reduces errors, and reduces compliance risk. This integration increases efficiency and helps distribute revenue more equitably, encouraging employee engagement and retention. The purpose of this article is to identify the limitations of traditional pay systems, such as scalability issues, inadequate trackability, and the inability to provide medical expense models for different employees. Using Workday ERP, healthcare organizations can address these issues by using AI-driven insights and predictive analytics to ensure that revenue is aligned with trending business and company standards. The platform’s scalability also allows healthcare organizations to adjust their payment models in response to staff growth, changing policies, or changing organizational goals. This article describes the use of new techniques developed specifically for medical purposes. This approach emphasizes collaboration between partners, data migration strategies, and testing phases to ensure minimal disruption during deployment. By integrating feedback and further development, healthcare organizations can progressively improve and optimize their post-utilization payment models, leading to long-term benefits. This article reports on the success of an ERP implementation at a healthcare organization through data analytics and comparative analysis, highlighting significant improvements in performance management, employee satisfaction, and fair compensation. The report also cites the potential of integrating future technologies like AI and machine learning into the Workday platform to pave the way for more complex and personalized payroll models. In summary, adopting Workday ERP provides healthcare organizations with a flexible way to improve payroll management. By addressing current and future workforce needs, Workday ERP provides healthcare leaders with the tools they need to drive growth, increase compliance, and keep employees happy and effective. The paper calls for continued exploration of AI-driven payroll models and hopes that advances in predictive analytics will complement payroll strategies in the healthcare industry.
 
Keywords: 
Compensation Management; Healthcare Organizations; Employee Satisfaction; Retention; HR leaders; Workday ERP; Cloud-Based Enterprise Resource Planning; Financial Management; Real-Time Analytics; Payroll Automation; Compliance Risks; Scalability; Predictive Analytics; Human Capital Management (HCM); Performance Management; Employee Engagement; Personalized Payroll Models; Workforce Optimization; AI-driven Insights; Regulatory Compliance; Machine Learning
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0