TY - GEN
T1 - Design Thinking Approach for Resource Allocation in Emergency Departments
AU - Ouda, E.
AU - Sleptchenko, A.
AU - Simsekler, M. C.E.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In hospital settings, Emergency Departments (EDs) often face overcrowding and inefficiencies due to unpredictable patient demand. This study addresses these challenges by integrating design thinking principles with experimental design methodologies. We introduce a novel approach combining design thinking, regression modeling, and Discrete Event Simulation (DES) to enhance resource allocation and reduce patient Length of Stay (LOS) in EDs. Design thinking involves phases such as understanding, abstraction, ideation, testing, and implementation, with each phase incorporating DES or regression modeling to evaluate system performance. Multiple regression models examine relationships between nurses, beds, physicians, and patient stays, offering insights into key performance indicators and resource allocation. The research aims to develop an optimal resource allocation strategy by exploring interactions between resource management and patient waiting times. The findings highlight the critical resource factors that influence patient flow. A case study demonstrates significant reductions in patient LOS, promising an improved patient experience. Our framework equips healthcare institutions with knowledge for informed decision making and strategic changes, aligning with the overall objectives of the study.
AB - In hospital settings, Emergency Departments (EDs) often face overcrowding and inefficiencies due to unpredictable patient demand. This study addresses these challenges by integrating design thinking principles with experimental design methodologies. We introduce a novel approach combining design thinking, regression modeling, and Discrete Event Simulation (DES) to enhance resource allocation and reduce patient Length of Stay (LOS) in EDs. Design thinking involves phases such as understanding, abstraction, ideation, testing, and implementation, with each phase incorporating DES or regression modeling to evaluate system performance. Multiple regression models examine relationships between nurses, beds, physicians, and patient stays, offering insights into key performance indicators and resource allocation. The research aims to develop an optimal resource allocation strategy by exploring interactions between resource management and patient waiting times. The findings highlight the critical resource factors that influence patient flow. A case study demonstrates significant reductions in patient LOS, promising an improved patient experience. Our framework equips healthcare institutions with knowledge for informed decision making and strategic changes, aligning with the overall objectives of the study.
KW - design of experiments
KW - design thinking
KW - discrete event simulation
KW - emergency department
KW - Healthcare
UR - https://www.scopus.com/pages/publications/85217992216
U2 - 10.1109/IEEM62345.2024.10857017
DO - 10.1109/IEEM62345.2024.10857017
M3 - Conference contribution
AN - SCOPUS:85217992216
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 1109
EP - 1113
BT - IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2024
PB - IEEE Computer Society
T2 - 2024 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2024
Y2 - 15 December 2024 through 18 December 2024
ER -