Cautiously optimistic: paediatric critical care nurses’ perspectives on data-driven algorithms in low-resource settings—a human-centred design study in Malawi
Nurses in the LRS studied, perceived that data-driven algorithms, especially for predicting patient deterioration, could improve the provision of critical care. This can be achieved by translating nurses' perspectives into design strategies, as has been carried out in this study. The lessons learned were summarised as actionable pre-implementation recommendations for the development and implementation of data-driven algorithms in LRS.
Authors
- Niels Henrik Chavannes (ORCID: https://orcid.org/0000-0002-8607-9199)
- Alice Likumbo
- Daniel Mwale (ORCID: https://orcid.org/0009-0003-6511-0542)
- Josephine Langton (ORCID: https://orcid.org/0000-0001-5354-2039)
- Bart Bierling (ORCID: https://orcid.org/0000-0001-5257-8558)
- Margot M. Rakers (ORCID: https://orcid.org/0000-0001-8233-1150)
- María José Villalobos-Quesada (ORCID: https://orcid.org/0000-0003-4930-1982)
- Hendrikus J. A. van Os (ORCID: https://orcid.org/0000-0002-8911-8608)
- Kiran Dellimore (ORCID: https://orcid.org/0000-0003-0311-2238)
- Job Calis (ORCID: https://orcid.org/0000-0001-6522-1347)
- Lezzie Chirambo
- Lieke de Mare
Institutions
- Leiden University Medical Center (NL)
- Kamuzu Central Hospital (MW)
- Amsterdam UMC Location University of Amsterdam (NL)
- Emma Kinderziekenhuis (NL)
- Blantyre Institute for Community Ophthalmology (MW)
- Queen Elizabeth Central Hospital (MW)
- Expertise Center Vocational Education (NL)
- Kamuzu University of Health Sciences (MW)
Publication Details
- Journal
- BMC Global and Public Health
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1186/s44263-024-00108-8
- Citations
- 3
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.36
Funders
- European Commission
- European and Developing Countries Clinical Trials Partnership