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

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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article numbere048657
Pages (from-to)1-10
Number of pages10
JournalBMJ Open
Volume11
Issue number8
DOIs
Publication statusPublished - 25 Aug 2021

Bibliographical note

Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • geriatric medicine
  • health & safety
  • health informatics
  • information technology
  • quality in health care
  • risk management

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