A dashboard of predictive analytics and decision support: Improving the quality of aged care in Australia

A dashboard of predictive analytics and decision support: Improving the quality of aged care in Australia



A dashboard of predictive analytics and decision support to drive care quality and person-centred outcomes in aged care

This project is supported by an NHMRC Partnership Project grant with Anglicare (APP1170898).


Project members


Professor Johanna Westbrook

Professor and Director


Professor Andrew Georgiou

Professor


A/Prof Magdalena Raban

Associate Professor


Dr Nasir Wabe
Senior Research Fellow


Dr Amy Nguyen

Research Fellow


Dr Karla Seaman
Research Fellow


Dr Sandun Malpriya Silva
Research Fellow


Dr Guogui Huang
Postdoctoral Research Fellow

Sangita Neupane
Research Assistant

Project description, aims and methods

Description

The quality of aged care is a concern for all Australians. The ongoing Royal Commission into Aged Care Quality and Safety indicates a system facing significant challenges. Australians need better quality aged care and greater transparency; a system that responds to older peoples’ needs, identifies when they are at risk, and answers in effective and compassionate ways.

Our aged care sector is data rich but information poor. Although providers now collect vast amounts of electronic clinical and care management data, very little attention has been placed on the power of analytics to exploit this data to deliver actionable information about aged care quality. This includes enabling preventive action by creating predictive risk models to provide earlier identification of older Australians who are at risk of adverse events, and implementing real-time integrated data views, such as clinical dashboards, that provide overviews and alerts of areas of care requiring attention.

Aims

This is a five-year mixed-methods study that aims to:

  1. Co-develop an aged care dashboard which presents a visual overview of key indicators and embedded decision support to guide care decisions in residential and community-based aged care settings
  2. Identify and assess design and work process features which support the integration of dashboard use and decision support by multiple user groups (e.g. facility managers, staff and GPs, clients/families) into their everyday practice and lives
  3. Implement and evaluate the impact of the dashboard on evidence-based care decisions (e.g. medication use) and client outcomes (e.g. hospitalisations; client reported wellbeing) using a hybrid stepped-wedge cluster randomised controlled trial, and process and economic evaluations.

Further information

Project Sponsors
  • National Health and Medical Research Council
  • Anglicare
Collaborative Partners
  • Anglicare
  • Sydney North Primary Health Network
  • Northern Sydney Local Health District
  • Deeble Institute for Health Policy Research
  • Australian Healthcare and Hospitals Association
  • Aged Care Quality and Safety Commission

Related publications

  1. Seaman, K., Ludlow, K., Wabe, N., Dodds, L., Siette, J., Nguyen, A., Jorgensen, M., Lord, S. R., Close, J. C. T., O'toole, L., Lin, C., Eymael, A. & Westbrook, J. 2022. The use of predictive fall models for older adults receiving aged care, using routinely collected electronic health record data: A systematic review. BMC Geriatrics, 22, 210.
  2. Wabe, N., Seaman, K. L., Nguyen, A. D., Siette, J., Raban, M. Z., Hibbert, P., Close, J. C. T., Lord, S. R. & Westbrook, J. I. 2022. Epidemiology of falls in 25 Australian residential aged care facilities: a retrospective longitudinal cohort study using routinely collected data. Int J Qual Health Care, 34.
  3. Wabe, N., Siette, J., Seaman, K. L., Nguyen, A. D., Raban, M. Z., Close, J. C. T., Lord, S. R. & Westbrook, J. I. 2022. The use and predictive performance of the Peninsula Health Falls Risk Assessment Tool (PH-FRAT) in 25 residential aged care facilities: a retrospective cohort study using routinely collected data. BMC Geriatrics, 22, 271.
  4. Huang, G., Wabe, N., Raban, M. Z., Seaman, K. L., Silva, S. M. & Westbrook, J. I. 2023. The relationship between fall incidents and place of birth in residential aged care facilities: a retrospective longitudinal cohort study. BMC Geriatrics, 23, 257.
  5. Siette, J., Dodds, L., Sharifi, F., Nguyen, A., Baysari, M., Seaman, K., Raban, M., Wabe, N. & Westbrook, J. 2023. Usability and acceptability of clinical dashboards in aged care: a systematic review. JMIR Aging, 6, e42274.
  6. Seaman, K., Huang, G., Wabe, N., Nguyen, A., Pinto, S. & Westbrook, J. 2024. Hospitalisations before and after entry into a residential aged care facility: An interrupted time series analysis. Australas J Ageing, 43, 61-70.
  7. Seaman, K., Meulenbroeks, I., Nguyen, A., Silva, S., Wabe, N., Huang, G., Hibert, P., Paudel, P. & Westbrook, J. 2023. Innovative approaches to analysing aged care falls incident data: international classification for patient safety and correspondence analysis. Int J Qual Health Care, 35.
  8. Meulenbroeks, I., Mercado, C., Gates, P., Nguyen, A., Seaman, K., Wabe, N., Silva, S. M., Zheng, W. Y., Debono, D. & Westbrook, J. 2024. Effectiveness of fall prevention interventions in residential aged care and community settings: An umbrella review. BMC Geriatrics, 24, 75.
  9. Wabe, N., Meulenbroeks, I., Huang, G., Silva, S. M., Gray, L. C., Close, J. C. T., Lord, S. & Westbrook, J. I. 2024. Development and internal validation of a dynamic fall risk prediction and monitoring tool in aged care (FRIPAC) using routinely collected electronic data: A landmarking approach. J Am Med Inform Assoc, 31, 1113-1125.
  10. Huang, G., Wabe, N., Raban, M. Z., Silva, S. S. M., Seaman, K., Nguyen, A. D., Meulenbroeks, I. & Westbrook, J. I. 2024. The relationship between participation in leisure activities and incidence of falls in residential aged care. PLoS One, 19, e0302678.
  11. Ludlow K, Westbrook J, Jorgensen M, Lind KE, Baysari MT, Gray LC, et al. 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. 2021;11(8):e048657.
  12. Lind K.E, Raban M.Z, Brett L, Jorgenson M.L, Georgiou G, Westbrook J.I (2020). Measuring the prevalence of 60 health conditions in older Australians in residential aged care with electronic health records: a retrospective dynamic cohort study. Population Health Metrics

News and Media

 
Predictive falls risk model for aged care

Dr Karla Seaman attended the Australian Association of Gerontology national conference this week and reported on the research currently being undertaken in the Aged Care Evaluation and Research (ACER) team working with aged care provider Anglicare to develop a predictive falls risk model for the aged care sector. This research was reported on in Australian Ageing Agenda, please see the full article here.

Contact

Dr Amy Nguyen
Research Fellow

Related Links

Integrating aged care services in the community

Related stream of research

Aged Care Evaluation Research (ACER) team

Project Status

Current

Centres Related to this Project

Centre for Health Systems and Safety Research

Content owner: Australian Institute of Health Innovation Last updated: 26 Apr 2024 1:31pm

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