Digitalisation for optimising nursing staff demand modelling and scheduling in nursing homes

Polly P.L. Leung, C.H. Wu, C.K. Kwong, W.H. Ip and W.K. Ching

Technological Forecasting and Social Change, 2021, vol. 164, issue C

Abstract: The increasing need for improving the utilisation of existing nursing staff and better workload distribution has made nursing staff demand modelling and nurse scheduling critical. However, both rely on human experience, often leading to ineffective planning. This study describes the use of cloud computing together with mobile devices for planning the optimal size and mix of nursing teams and scheduling in nursing homes. In the context of residential care, cloud computing can make the determination and planning of nursing staff demand more efficient and cost effective, while mobile devices can facilitate easy and rapid dissemination of planning information. This study applies nondominated sorting genetic algorithm II in cloud computing to solve the integrated nursing staff demand modelling and scheduling problem. A case study of a subvented nursing home in Hong Kong was conducted to illustrate the effectiveness of the model and framework. Results show that the total overtime work (in hours) was minimised, while the nurse-to-resident ratios were significantly improved. The model permits an analysis of the impact of digital technologies on healthcare at the strategic level. Further, results suggest that it is cost beneficial to introduce digitalisation to integrated nursing staff demand modelling and scheduling problems in nursing homes.

Keywords: Nursing staff, Staff demand modelling; Staff scheduling; Cloud computing; Nursing homes (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:164:y:2021:i:c:s004016252031338x

DOI: 10.1016/j.techfore.2020.120512

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