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dc.contributor.authorLudlow, K.
dc.contributor.authorWestbrook, J.
dc.contributor.authorJorgensen, M.
dc.contributor.authorLind, K.E.
dc.contributor.authorBaysari, M.T.
dc.contributor.authorGray, L.C.
dc.contributor.authorDay, R.O.
dc.contributor.authorRatcliffe, J.
dc.contributor.authorLord, S.R.
dc.contributor.authorGeorgiou, A.
dc.contributor.authorBraithwaite, J.
dc.contributor.authorRaban, M.Z.
dc.contributor.authorClose, J.
dc.contributor.authorBeattie, E.
dc.contributor.authorZheng, W.Y.
dc.contributor.authorDebono, D.
dc.contributor.authorNguyen, A.
dc.contributor.authorSiette, J.
dc.contributor.authorSeaman, K.
dc.contributor.authorMiao, M.
dc.contributor.authorRoot, J.
dc.contributor.authorRoffe, D.
dc.contributor.authorO'Toole, L.
dc.contributor.authorCarrasco, M.
dc.contributor.authorThompson, A.
dc.contributor.authorShaikh, J.
dc.contributor.authorWong, J.
dc.contributor.authorStanton, C.
dc.contributor.authorHaddock, R.
dc.date.accessioned2021-09-24T20:59:01Z
dc.date.available2021-09-24T20:59:01Z
dc.date.issued2021
dc.identifier.citationLudlow, K., Westbrook, J., Jorgensen, M., Lind, K. E., Baysari, M. T., Gray, L. C., Day, R. O., Ratcliffe, J., Lord, S. R., Georgiou, A., Braithwaite, J., Raban, M. Z., Close, J., Beattie, E., Zheng, W. Y., Debono, D., Nguyen, A., Siette, J., Seaman, K., … Haddock, R. (2021). Co-designing a dashboard of predictive analytics and decision support to drive care quality and client outcomes in aged care: A mixed-method study protocol. BMJ Open, 11(8).
dc.identifier.issn2044-6055
dc.identifier.doi10.1136/bmjopen-2021-048657
dc.identifier.urihttp://hdl.handle.net/10150/661905
dc.description.abstractIntroduction There is a clear need for improved care quality and quality monitoring in aged care. Aged care providers collect an abundance of data, yet rarely are these data integrated and transformed in real-time into actionable information to support evidence-based care, nor are they shared with older people and informal caregivers. This protocol describes the co-design and testing of a dashboard in residential aged care facilities (nursing or care homes) and community-based aged care settings (formal care provided at home or in the community). The dashboard will comprise integrated data to provide an € at-a-glance' overview of aged care clients, indicators to identify clients at risk of fall-related hospitalisations and poor quality of life, and evidence-based decision support to minimise these risks. Longer term plans for dashboard implementation and evaluation are also outlined. Methods This mixed-method study will involve (1) co-designing dashboard features with aged care staff, clients, informal caregivers and general practitioners (GPs), (2) integrating aged care data silos and developing risk models, and (3) testing dashboard prototypes with users. The dashboard features will be informed by direct observations of routine work, interviews, focus groups and co-design groups with users, and a community forum. Multivariable discrete time survival models will be used to develop risk indicators, using predictors from linked historical aged care and hospital data. Dashboard prototype testing will comprise interviews, focus groups and walk-through scenarios using a think-aloud approach with staff members, clients and informal caregivers, and a GP workshop. Ethics and dissemination This study has received ethical approval from the New South Wales (NSW) Population & Health Services Research Ethics Committee and Macquarie University's Human Research Ethics Committee. The research findings will be presented to the aged care provider who will share results with staff members, clients, residents and informal caregivers. Findings will be disseminated as peer-reviewed journal articles, policy briefs and conference presentations. © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
dc.language.isoen
dc.publisherBMJ Publishing Group
dc.rightsCopyright © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC.
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectgeriatric medicine
dc.subjecthealth & safety
dc.subjecthealth informatics
dc.subjectinformation technology
dc.subjectquality in health care
dc.subjectrisk management
dc.titleCo-designing a dashboard of predictive analytics and decision support to drive care quality and client outcomes in aged care: A mixed-method study protocol
dc.typeArticle
dc.typetext
dc.contributor.departmentDepartment of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, The University of Arizona
dc.identifier.journalBMJ Open
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleBMJ Open
refterms.dateFOA2021-09-24T20:59:01Z


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Copyright © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC.
Except where otherwise noted, this item's license is described as Copyright © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC.