EconPapers    
Economics at your fingertips  
 

Rehabilitation Staff Scheduling Problem Considering Mental Workload in Elderly Daytime Care Facility

Ryohei Matsumoto, Tetsuo Yamada () and Masato Takanokura
Additional contact information
Ryohei Matsumoto: The University of Electro-Communications
Tetsuo Yamada: The University of Electro-Communications
Masato Takanokura: Kanagawa University

A chapter in Intelligent Engineering and Management for Industry 4.0, 2022, pp 117-126 from Springer

Abstract: Abstract Recently, in Japan, the importance of elderly daytime care facilities which provide nursing care services has been increasing. Rehabilitation services such as massages by licensed staff and exercises using equipment and machines are offered in these facilities. Multiple staff members execute the rehabilitation services according to a staff schedule. However, the staff in the facilities often face problems of heavy workload. The workload problems of the staff were not grasped earlier since analysis of the workload based on the industrial engineering (IE) method was not conducted in the facilities. Therefore, there is a possibility that the workloads of the staff are not well-balanced in the schedule. In addition, it is considered that the staff have two types of workloads: physical and mental, since they offer their services to human beings instead of working with products and materials in factories. Thus, it is necessary to consider the mental workloads on staff in planning staff schedules in order to ensure that they are well-balanced. This study addresses a staff scheduling problem for elderly daytime care facilities considering mental workloads of the staff and plans a balanced schedule to incorporate the mental workloads. A balanced schedule for mental workloads among staff members is planned based on the workload survey that included an interview and questionnaire, conducted on actual staff members. The results are discussed by comparing the findings with the current schedule and the balanced one for physical workloads.

Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-94683-8_11

Ordering information: This item can be ordered from
http://www.springer.com/9783030946838

DOI: 10.1007/978-3-030-94683-8_11

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-04-02
Handle: RePEc:spr:sprchp:978-3-030-94683-8_11