Ebnali Mahdi, Kennedy-Metz Lauren R, Conboy Heather M, Clarke Lori A, Osterweil Leon J, Avrunin George, Miccile Christian, Arshanskiy Maria, Phillips Annette, Zenati Marco A, Dias Roger D
Human Factors and Cognitive Engineering Lab, STRATUS Center for Medical Simulation, Brigham and Women's Hospital, Boston, MA, USA.
Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA.
Hum Comput Interact Theor Approaches Des Method (2022). 2022 Jun-Jul;13302:185-196. doi: 10.1007/978-3-031-05311-5_12. Epub 2022 Jun 16.
Several studies have reported low adherence and high resistance from clinicians to adopt digital health technologies into clinical practice, particularly the use of computer-based clinical decision support systems. Poor usability and lack of integration with the clinical workflow have been identified as primary issues. Few guidelines exist on how to analyze the collected data associated with the usability of digital health technologies. In this study, we aimed to develop a coding framework for the systematic evaluation of users' feedback generated during focus groups and interview sessions with clinicians, underpinned by fundamental usability principles and design components. This codebook also included a coding category to capture the user's clinical role associated with each specific piece of feedback, providing a better understanding of role-specific challenges and perspectives, as well as the level of shared understanding across the multiple clinical roles. Furthermore, a voting system was created to quantitatively inform modifications of the digital system based on usability data. As a use case, we applied this method to an electronic cognitive aid designed to improve coordination and communication in the cardiac operating room, showing that this framework is feasible and useful not only to better understand suboptimal usability aspects, but also to recommend relevant modifications in the design and development of the system from different perspectives, including clinical, technical, and usability teams. The framework described herein may be applied in other highly complex clinical settings, in which digital health systems may play an important role in improving patient care and enhancing patient safety.
多项研究报告称,临床医生在将数字健康技术应用于临床实践方面依从性较低且抵触情绪较高,尤其是在使用基于计算机的临床决策支持系统方面。可用性差以及与临床工作流程缺乏整合已被确定为主要问题。关于如何分析与数字健康技术可用性相关的收集数据,几乎没有相关指南。在本研究中,我们旨在基于基本的可用性原则和设计组件,开发一个编码框架,用于系统评估在与临床医生进行焦点小组讨论和访谈期间产生的用户反馈。该编码手册还包括一个编码类别,用于捕捉与每条具体反馈相关的用户临床角色,以便更好地理解特定角色的挑战和观点,以及多个临床角色之间的共享理解程度。此外,还创建了一个投票系统,以便根据可用性数据对数字系统的修改进行定量指导。作为一个用例,我们将此方法应用于一个旨在改善心脏手术室协调与沟通的电子认知辅助工具,结果表明该框架不仅对于更好地理解可用性欠佳的方面是可行且有用的,而且还能从不同角度(包括临床、技术和可用性团队)为系统的设计和开发推荐相关修改。本文所述的框架可应用于其他高度复杂的临床环境,在这些环境中,数字健康系统可能在改善患者护理和提高患者安全方面发挥重要作用。