Nair Monika, Andersson Jonas, Nygren Jens M, Lundgren Lina E
School of Health and Welfare, Halmstad University, Halmstad, Sweden.
Cambio Healthcare Systems AB, Stockholm, Sweden.
JMIR Form Res. 2023 Aug 23;7:e47335. doi: 10.2196/47335.
Artificial intelligence (AI) applications in health care are expected to provide value for health care organizations, professionals, and patients. However, the implementation of such systems should be carefully planned and organized in order to ensure quality, safety, and acceptance. The gathered view of different stakeholders is a great source of information to understand the barriers and enablers for implementation in a specific context.
This study aimed to understand the context and stakeholder perspectives related to the future implementation of a clinical decision support system for predicting readmissions of patients with heart failure. The study was part of a larger project involving model development, interface design, and implementation planning of the system.
Interviews were held with 12 stakeholders from the regional and municipal health care organizations to gather their views on the potential effects implementation of such a decision support system could have as well as barriers and enablers for implementation. Data were analyzed based on the categories defined in the nonadoption, abandonment, scale-up, spread, sustainability (NASSS) framework.
Stakeholders had in general a positive attitude and curiosity toward AI-based decision support systems, and mentioned several barriers and enablers based on the experiences of previous implementations of information technology systems. Central aspects to consider for the proposed clinical decision support system were design aspects, access to information throughout the care process, and integration into the clinical workflow. The implementation of such a system could lead to a number of effects related to both clinical outcomes as well as resource allocation, which are all important to address in the planning of implementation. Stakeholders saw, however, value in several aspects of implementing such system, emphasizing the increased quality of life for those patients who can avoid being hospitalized.
Several ideas were put forward on how the proposed AI system would potentially affect and provide value for patients, professionals, and the organization, and implementation aspects were important parts of that. A successful system can help clinicians to prioritize the need for different types of treatments but also be used for planning purposes within the hospital. However, the system needs not only technological and clinical precision but also a carefully planned implementation process. Such a process should take into consideration the aspects related to all the categories in the NASSS framework. This study further highlighted the importance to study stakeholder needs early in the process of development, design, and implementation of decision support systems, as the data revealed new information on the potential use of the system and the placement of the application in the care process.
人工智能（AI）在医疗保健中的应用有望为医疗保健机构、专业人员和患者带来价值。然而，此类系统的实施应经过精心规划和组织，以确保质量、安全和可接受性。不同利益相关者的综合观点是了解特定背景下实施障碍和推动因素的重要信息来源。
本研究旨在了解与未来实施心力衰竭患者再入院预测临床决策支持系统相关的背景和利益相关者观点。该研究是一个更大项目的一部分，该项目涉及系统的模型开发、界面设计和实施规划。
与来自地区和市级医疗保健机构的12名利益相关者进行了访谈，以收集他们对这种决策支持系统实施可能产生的潜在影响以及实施的障碍和推动因素的看法。根据非采用、放弃、扩大规模、传播、可持续性（NASSS）框架中定义的类别对数据进行了分析。
利益相关者总体上对基于AI的决策支持系统持积极态度和好奇心，并根据先前信息技术系统实施的经验提到了几个障碍和推动因素。对于拟议的临床决策支持系统，需要考虑的核心方面包括设计方面、在整个护理过程中获取信息以及融入临床工作流程。实施这样一个系统可能会导致与临床结果以及资源分配相关的一系列影响，这些在实施规划中都很重要。然而，利益相关者认为实施该系统在几个方面具有价值，强调对于那些可以避免住院的患者来说，生活质量得到了提高。
就拟议的AI系统可能如何影响患者、专业人员和组织并为其提供价值提出了一些想法，而实施方面是其中的重要部分。一个成功的系统可以帮助临床医生确定不同类型治疗的优先级需求，也可用于医院内部的规划目的。然而，该系统不仅需要技术和临床精度，还需要精心规划的实施过程。这样一个过程应考虑到NASSS框架中所有类别的相关方面。本研究进一步强调了在决策支持系统的开发、设计和实施过程早期研究利益相关者需求的重要性，因为数据揭示了有关该系统潜在用途以及该应用在护理过程中位置的新信息。
