Moseley Lisa, Laws Anna, Allen Michael, Ford Gary A, James Martin, McCarthy Stephen, McClelland Graham, Park Laura J, Pearn Kerry, Phillips Daniel, Price Christopher, Shaw Lisa, White Phil, Wilson David, McMeekin Peter, Scott Jason
Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK.
University of Exeter Medical School, Exeter, UK and NIHR South West Peninsula Applied Research Collaboration (ARC), Exeter, UK.
NPJ Digit Med. 2025 May 9;8(1):264. doi: 10.1038/s41746-025-01691-2.
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.
医疗保健领域创新的引入是一个复杂的社会技术过程，理想情况下应以高质量证据为依据。然而，证据并不总是以可用于决策的格式准备和呈现。敏捷方法与定性协同设计相结合，用于开发一个数字网络应用程序，该应用程序纳入了中风预后的机器学习模型，为在英国实施移动中风单元（MSU）的决策提供信息，随后使用出声思考法进行可用性测试。16名参与就模型参数和路径达成共识的利益相关者参与其中，并对数据进行了主题分析。确定了网络应用程序所需的改进，并对特定背景下决策的复杂性产生了新的见解，这也为参与者对MSU可行性的看法提供了信息。本研究提供了实证证据，支持开发创新且易于使用的数字传播方法，以参与决策过程并前瞻性地了解决策挑战。
