System Quality & Patient Safety, Houston Methodist System, Houston, TX, United States.
CareSense, MedTrak, Inc, Conshohocken, PA, United States.
JMIR Mhealth Uhealth. 2020 Nov 11;8(11):e17577. doi: 10.2196/17577.
Several recently published studies and consensus statements have demonstrated that there is only modest (and in many cases, low-quality) evidence that mobile health (mHealth) can improve patient clinical outcomes such as the length of stay or reduction of readmissions. There is also uncertainty as to whether mHealth can improve patient-centered outcomes such as patient engagement or patient satisfaction. One principal challenge behind the "effectiveness" research in this field is a lack of common understanding about what it means to be effective in the digital space (ie, what should constitute a relevant outcome and how best to measure it). In this viewpoint, we call for interdisciplinary, conceptual clarity on the definitions, methodologies, and patient-centered outcomes frequently used in mHealth research. To formulate our recommendations, we used a snowballing approach to identify relevant definitions, outcomes, and methodologies related to mHealth. To begin, we drew heavily upon previously published detailed frameworks that enumerate definitions and measurements of engagement. We built upon these frameworks by extracting other relevant measures of patient-centered care, such as patient satisfaction, patient experience, and patient activation. We describe several definitional inconsistencies for key constructs in the mHealth literature. In an effort to achieve clarity, we tease apart several patient-centered care outcomes, and outline methodologies appropriate to measure each of these patient-care outcomes. By creating a common pathway linking definitions with outcomes and methodologies, we provide a possible interdisciplinary approach to evaluating mHealth technologies. With the broader goal of creating an interdisciplinary approach, we also provide several recommendations that we believe can advance mHealth research and implementation.
最近发表的几项研究和共识声明表明,移动医疗(mHealth)只能在适度(在许多情况下,质量较低)的情况下改善患者的临床结果,例如住院时间或减少再入院率。mHealth 是否能改善以患者为中心的结果,如患者参与度或患者满意度,也存在不确定性。该领域“有效性”研究背后的主要挑战之一是,缺乏对数字空间中有效性含义的共识(即,什么应该构成相关结果,以及如何最好地衡量它)。在这个观点中,我们呼吁在 mHealth 研究中对定义、方法和以患者为中心的结果进行跨学科、概念上的清晰理解。为了制定我们的建议,我们使用了滚雪球的方法来确定与 mHealth 相关的定义、结果和方法。首先,我们大量借鉴了以前发表的详细框架,这些框架列举了参与度的定义和测量方法。我们从这些框架中提取了其他与以患者为中心的护理相关的措施,如患者满意度、患者体验和患者激活。我们描述了 mHealth 文献中几个关键结构的定义不一致性。为了实现清晰,我们分解了几个以患者为中心的护理结果,并概述了适合测量这些患者护理结果的方法。通过创建一个将定义与结果和方法联系起来的共同途径,我们提供了一种评估 mHealth 技术的可能的跨学科方法。为了实现跨学科方法的更广泛目标,我们还提出了几项我们认为可以推进 mHealth 研究和实施的建议。