Browse by author
Lookup NU author(s): Lisa Moseley, Dr Graham McClellandORCiD, Professor Christopher PriceORCiD, Dr Lisa ShawORCiD, Professor Phil WhiteORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Commissioning of innovations in healthcare is a complex socio-technical process, ideally informed by high quality evidence. However, evidence is not always prepared and presented in a format usable for commissioning decisions. Agile methodology, combined with qualitative co-design, were used to develop a digital web application incorporating machine learning models of stroke outcomes to inform commissioning decisions for the implementation of mobile stroke units (MSUs) in England, followed by usability testing using think aloud methodology. Sixteen stakeholders involved in developing consensus on model parameters and pathways participated with data thematically analysed. Required improvements to the web application were identified and novel insights into the complexity of context-specific commissioning decisions were generated, which also informed participants’ views on the viability of MSUs. This study provides empirical evidence in support of developing innovative and accessible digital dissemination methods to engage with commissioning processes and prospectively understand commissioning challenges.
Author(s): Moseley L, Laws A, Allen M, Ford GA, James M, McCarthy S, McClelland G, Park LJ, Phillips D, Price C, Shaw L, White P, Wilson D, McMeekin P, Scott J
Publication type: Article
Publication status: Published
Journal: npj Digital Medicine
Year: 2025
Volume: 8
Online publication date: 09/05/2025
Acceptance date: 29/04/2025
Date deposited: 15/05/2025
ISSN (electronic): 2398-6352
Publisher: Nature Publishing Group
URL: https://doi.org/10.1038/s41746-025-01691-2
DOI: 10.1038/s41746-025-01691-2
Data Access Statement: The qualitative datasets generated and/or analysed during the current study are not publicly available due to not having ethical approval or participant consent for the sharing of recordings ortranscripts beyond select quotations in publications. The corresponding author (J.S.) can interrogate the data on behalf of others upon reasonable request up until 30 April 2032, after which all data will be deleted in line with university data retention policy. Ethical approval will be required for any re-use of data. The underlying code for this study is available on Github: https://github. com/stroke-modelling/muster_workshop_web_app_1 (version v0.0.11). There are no access restrictions
Altmetrics provided by Altmetric
