Healthcare call center efficiency improvement using a simulation approach to achieve the organization’s target

ABSTRACT

Healthcare call centers serve an important role in the healthcare system to support patients’ access to care and satisfaction and optimize medical resource allocation. Limited research has provided sufficient evidence on how to increase healthcare call center’s efficiency from managerial aspects. In this study, a simulation model is built based on real-life healthcare call center settings and call information, to identify the opportunities for improvement. This call center processes approximately 2638 calls for 25 clinic sites every day. The call abandon rate was at 16.7%, which was far from the organization’s goal of 5%. Some improvement opportunities based on managerial interests such as cross-coverage, non-scheduling call transition, staff availability and staff allocation without increasing staff numbers, are identified and their operational impacts are evaluated by the simulation model. Moreover, a genetic algorithm is used to find the optimal shift for staff allocation. The simulation results demonstrate the validity of the proposed model, provide valuable insights for decision-making before the implementation and provide other healthcare call centers’ alternatives to improve performance. The recommendations were communicated to call center staff and consentaneous opportunities were implemented. The results showed a significant reduction of 39.5% in the call abandon rate.

KEYWORDS:

• Healthcare service
• strategic decision-making
• genetic algorithm
• staffing
• call center

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Lan Jiang

Dr. Lan Jiang is a Research Fellow working with Dr. Huang.

Yu-Li Huang

Dr. Yu-Li Huang is a Senior Associate Consultant, Scientific Director for the Applied Operations Research, and an Associate Professor of Health Care Systems Engineering at Mayo Clinic. He received his PhD, MSE, and BSE in Industrial and Operations Engineering from the University of Michigan.