背景:技术的使用对患者安全和护理质量产生了重大影响,并且在全球范围内有所增加。在文学中,据报道,人们每年因不良事件(AE)而死亡,并且存在用于调查和测量AE的各种方法。然而,有些方法的范围有限,数据提取,以及对数据标准化的需求。在巴西,关于触发工具的应用研究很少,这项研究是第一个在动态护理中创建自动触发因素的研究。
目的:本研究旨在为巴西的门诊医疗机构开发基于机器学习(ML)的自动触发器。
方法:将在设计思维框架内进行混合方法研究,并将这些原则应用于创建自动触发器,在(1)同情和定义问题的阶段之后,涉及观察和询问,以理解用户和手头的挑战;(2)构思,生成问题的各种解决方案;(3)原型设计,涉及构建最佳解决方案的最小表示;(4)测试,获得用户反馈以改进解决方案;以及(5)实施,在那里测试精制溶液,评估变化,并且考虑了缩放。此外,将采用ML方法开发自动触发器,与该领域的专家合作,根据当地情况量身定制。
结果:该协议描述了一项处于初步阶段的研究,在任何数据收集和分析之前。该研究于2024年1月获得了该机构内组织成员的批准,并获得了圣保罗大学和该研究机构的道德委员会的批准。2024年5月。截至2024年6月,第一阶段开始于定性研究的数据收集。在本研究的第1阶段和第2阶段的结果之后,将考虑另一篇专注于解释ML方法的论文。
结论:在门诊环境中开发自动触发因素后,将有可能更及时地预防和识别AE的潜在风险,提供有价值的信息。这项技术创新不仅促进了临床实践的进步,而且有助于传播与患者安全相关的技术和知识。此外,卫生保健专业人员可以采取循证预防措施,降低与不良事件和医院再入院相关的成本,提高门诊护理的生产力,并为安全做出贡献,质量,以及所提供护理的有效性。此外,在未来,如果结果成功,有可能在所有单位应用它,按照机构组织的计划。
■PRR1-10.2196/55466。
BACKGROUND: The use of technologies has had a significant impact on patient safety and the quality of care and has increased globally. In the literature, it has been reported that people die annually due to adverse events (AEs), and various methods exist for investigating and measuring AEs. However, some methods have a limited scope, data extraction, and the need for data standardization. In Brazil, there are few studies on the application of trigger tools, and this
study is the first to create automated triggers in ambulatory care.
OBJECTIVE: This
study aims to develop a machine learning (ML)-based automated trigger for outpatient health care settings in Brazil.
METHODS: A mixed methods research will be conducted within a design thinking framework and the principles will be applied in creating the automated triggers, following the stages of (1) empathize and define the problem, involving observations and inquiries to comprehend both the user and the challenge at hand; (2) ideation, where various solutions to the problem are generated; (3) prototyping, involving the construction of a minimal representation of the best solutions; (4) testing, where user feedback is obtained to refine the solution; and (5) implementation, where the refined solution is tested, changes are assessed, and scaling is considered. Furthermore, ML methods will be adopted to develop automated triggers, tailored to the local context in collaboration with an expert in the field.
RESULTS: This protocol describes a research
study in its preliminary stages, prior to any data gathering and analysis. The
study was approved by the members of the organizations within the institution in January 2024 and by the ethics board of the University of São Paulo and the institution where the
study will take place. in May 2024. As of June 2024, stage 1 commenced with data gathering for qualitative research. A separate paper focused on explaining the method of ML will be considered after the outcomes of stages 1 and 2 in this study.
CONCLUSIONS: After the development of automated triggers in the outpatient setting, it will be possible to prevent and identify potential risks of AEs more promptly, providing valuable information. This technological innovation not only promotes advances in clinical practice but also contributes to the dissemination of techniques and knowledge related to patient safety. Additionally, health care professionals can adopt evidence-based preventive measures, reducing costs associated with AEs and hospital readmissions, enhancing productivity in outpatient care, and contributing to the safety, quality, and effectiveness of care provided. Additionally, in the future, if the outcome is successful, there is the potential to apply it in all units, as planned by the institutional organization.
UNASSIGNED: PRR1-10.2196/55466.