File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Digitalisation for Optimising Nursing Staff Demand Modelling and Scheduling in Nursing Homes

TitleDigitalisation for Optimising Nursing Staff Demand Modelling and Scheduling in Nursing Homes
Authors
KeywordsNursing staff
Staff demand modelling
Staff scheduling
Cloud computing
Nursing homes
Issue Date2021
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/techfore
Citation
Technological Forecasting and Social Change, 2021, v. 164, p. article no. 120512 How to Cite?
AbstractThe 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.
Persistent Identifierhttp://hdl.handle.net/10722/300669
ISSN
2023 Impact Factor: 12.9
2023 SCImago Journal Rankings: 3.118
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLeung, PPL-
dc.contributor.authorWu, CH-
dc.contributor.authorKwong, CK-
dc.contributor.authorIp, WH-
dc.contributor.authorChing, WK-
dc.date.accessioned2021-06-18T14:55:17Z-
dc.date.available2021-06-18T14:55:17Z-
dc.date.issued2021-
dc.identifier.citationTechnological Forecasting and Social Change, 2021, v. 164, p. article no. 120512-
dc.identifier.issn0040-1625-
dc.identifier.urihttp://hdl.handle.net/10722/300669-
dc.description.abstractThe 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.-
dc.languageeng-
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/techfore-
dc.relation.ispartofTechnological Forecasting and Social Change-
dc.subjectNursing staff-
dc.subjectStaff demand modelling-
dc.subjectStaff scheduling-
dc.subjectCloud computing-
dc.subjectNursing homes-
dc.titleDigitalisation for Optimising Nursing Staff Demand Modelling and Scheduling in Nursing Homes-
dc.typeArticle-
dc.identifier.emailChing, WK: wching@hku.hk-
dc.identifier.authorityChing, WK=rp00679-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.techfore.2020.120512-
dc.identifier.scopuseid_2-s2.0-85097788421-
dc.identifier.hkuros323022-
dc.identifier.volume164-
dc.identifier.spagearticle no. 120512-
dc.identifier.epagearticle no. 120512-
dc.identifier.isiWOS:000618702300017-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats