School of Medical Sciences, Fiji National University, Fiji.
Center for Cognitive Studies in Medicine and Public Health, The New York Academy of Medicine, USA.
Australas Psychiatry. 2021 Apr;29(2):200-203. doi: 10.1177/1039856220956458. Epub 2020 Sep 22.
To convert screening tools for depression and suicide risk into algorithmic decision support on smartphones for use by community health nurses (CHNs), and to evaluate the efficiency, effectiveness, and usability of the mHealth tool in providing mental health (MH) care.
Two scenarios of depression and suicide risk were developed and presented to 48 nurses using paper-based and mobile-based guidelines under laboratory (nonclinical) conditions. Participants read through the case scenarios to provide summaries, diagnoses, and management recommendations. Audiotapes were transcribed and analyzed for accuracy in scoring guidelines, therapy decisions, and time for tasks completion. The validated System Usability Scale (SUS) was used to measure mobile app usability.
Using mHealth-based guidelines, nurses took significantly less time to complete their tasks, and generated no errors of addition, as compared to paper-based guidelines. Although coding errors were noted when using the mHealth app, it did not influence treatment recommendations. The system usability scores for both guidelines were over 84%.
Usable mHealth technology can support task-sharing for CHNs in Fiji, for the efficient and accurate screening of patients for depression and suicide risks in a nonclinical setting. Studies on clinical implementation of the mHealth tool are needed and planned.
将抑郁症和自杀风险筛查工具转化为智能手机上的算法决策支持，以用于社区卫生护士（CHN），并评估移动医疗工具在提供心理健康（MH）护理方面的效率、有效性和可用性。
在实验室（非临床）条件下，使用基于纸质和基于移动的指南向 48 名护士呈现和开发了两种抑郁症和自杀风险场景。参与者阅读病例场景，提供摘要、诊断和管理建议。对录音带进行转录和分析，以评估在评分指南、治疗决策和任务完成时间方面的准确性。使用经过验证的系统可用性量表（SUS）来衡量移动应用程序的可用性。
与基于纸质的指南相比，使用基于移动医疗的指南，护士完成任务的时间明显减少，并且没有出现加法错误。尽管在使用移动医疗应用程序时注意到了编码错误，但它并未影响治疗建议。两种指南的系统可用性得分均超过 84%。
可用于移动医疗的技术可以支持斐济 CHN 的任务分担，以便在非临床环境中高效、准确地对患者进行抑郁症和自杀风险筛查。需要并计划进行移动医疗工具的临床实施研究。
