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

Ludlow, Kristiana, Westbrook, Johanna, Jorgensen, Mikaela, Lind, Kimberly E., Baysari, Melissa T., Gray, Leonard C., Day, Richard O., Ratcliffe, Julie, Lord, Stephen R., Georgiou, Andrew, Braithwaite, Jeffrey, Raban, Magdalena Z., Close, Jacqueline, , Zheng, Wu Yi, Debono, Deborah, Nguyen, Amy, Siette, Joyce, Seaman, Karla, Miao, Melissa, Root, Jo, Roffe, David, O'Toole, Libby, Carrasco, Marcela, Thompson, Alex, Shaikh, Javed, Wong, Jeffrey, Stanton, Cynthia, & (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), Article number: e048657.

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Description

Introduction: 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.

Impact and interest:

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2 citations in Web of Science®
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ID Code: 234957
Item Type: Contribution to Journal (Journal Article)
Refereed: Yes
Additional Information: Funding Information: This project was funded by an Australian National Health and Medical Research Council (NHMRC) Partnership Project Grant in partnership with Anglicare (1170898). The NHMRC did not have any influence on the design of the study and collection, analysis, and interpretation of data. MZR is supported by an NHMRC Early Career Fellowship (1143941). JWe is supported by a NHMRC Elizabeth Blackburn Leadership Investigator Grant (1174021). JB is supported by an NHMRC Leadership Investigator Grant (1176620), the NHMRC Partnership Centre in Health System Sustainability (Grant ID 9100002) and APP1135058.
Measurements or Duration: 10 pages
Keywords: geriatric medicine, health & safety, health informatics, information technology, quality in health care, risk management
DOI: 10.1136/bmjopen-2021-048657
ISSN: 2044-6055
Pure ID: 114804497
Divisions: Current > Research Centres > Centre for Healthcare Transformation
Current > QUT Faculties and Divisions > Faculty of Health
Current > Schools > School of Nursing
Funding Information: Funding This project was funded by an Australian National Health and Medical Research Council (NHMRC) Partnership Project Grant in partnership with Anglicare (1170898). The NHMRC did not have any influence on the design of the study and collection, analysis, and interpretation of data. MZR is supported by an NHMRC Early Career Fellowship (1143941). JWe is supported by a NHMRC Elizabeth Blackburn Leadership Investigator Grant (1174021). JB is supported by an NHMRC Leadership Investigator Grant (1176620), the NHMRC Partnership Centre in Health System Sustainability (Grant ID 9100002) and APP1135058.
Copyright Owner: Author(s) (or their employer(s)) 2021
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Deposited On: 30 Aug 2022 02:21
Last Modified: 26 Jul 2024 21:33