Evaluating the Effectiveness of Offering Personalized Employee Virtual Assistant (EVA) in the Employee Assistance Program (EAP)

Alekhya Gandra

Abstract: Employee Assistance Programs (EAPs) are critical resources provided within organizations with the objective of providing support for employees facing professional and personal - related issues. Health Insurance providers like Elevance Health offer integrated EAP services as part of their insurance plans to enterprises. However, the challenges that the traditional EAPs often face are lower engagement and utilization rates resulting in delayed response times for varying reasons including stigma and lack of employee awareness. [1] Artificial Intelligence (AI) technologies provides a potential to enhance traditional EAPs by integrating personalized employee virtual assistants (EVA) [2]. Unlike Chatbots, which are often used as information acquisition interface, a cognitive - based virtual assistant will provide personalized recommendation to employees based on data collected from various sources including wearable devices, employee organization and personal data [3]. By analyzing employee engagement, satisfaction and outcomes, this research investigates weather personalized EVAs enhance the effectiveness of existing Employee Assistance Programs (EAPs). The study also explores the solution to develop and integrate EVA into EAP and examines the potential benefits, challenges, and ethical considerations of integrating EVAs into existing EAP frameworks.

Keywords: Employee Virtual Assistant, EVA, Employee Assistance Program, EAP, Artificial Intelligence, AI, Machine Learning, ML, Natural Language Processing, NLP, Application Programming Interfaces, API, Health Care

How to Cite?: Alekhya Gandra, "Evaluating the Effectiveness of Offering Personalized Employee Virtual Assistant (EVA) in the Employee Assistance Program (EAP)", Volume 13 Issue 8, August 2024, International Journal of Science and Research (IJSR), Pages: 1548-1552, https://www.ijsr.net/getabstract.php?paperid=SR24826194504, DOI: https://dx.doi.org/10.21275/SR24826194504