Clinician Perspectives on Using Computational Mental Health Insights From Patients' Social Media Activities: Design and Qualitative Evaluation of a Prototype.
Clinician Perspectives on Using Computational Mental Health Insights From Patients' Social Media Activities: Design and Qualitative Evaluation of a Prototype.
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DOI：
10.2196/25455
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发表时间：
2021-11-16
影响因子：
5.2
通讯作者：
De Choudhury M
中科院分区：
文献类型：
--
作者：
Yoo DW;Ernala SK;Saket B;Weir D;Arenare E;Ali AF;Van Meter AR;Birnbaum ML;Abowd GD;De Choudhury M
以前的研究表明，社交媒体数据，沿着机器学习算法，可用于生成计算心理健康见解。这些计算的见解有可能在心理治疗咨询过程中支持临床医生与患者的沟通。然而，临床医生如何感知和设想在咨询过程中使用计算的见解一直探索不足。本研究的目的是了解临床医生对患者社交媒体活动的计算心理健康见解的看法。我们专注于在心理治疗咨询过程中使用这些见解的机会和挑战。我们开发了一个原型，可以分析同意患者的Facebook数据，并直观地表示这些计算见解。我们将这些见解整合到现有的面向临床医生的评估工具中，即汉密尔顿抑郁评定量表和全球功能：社会量表。设计意图是临床医生将口头采访患者（例如，过去一周您的情绪如何？）同时他们回顾了患者社交媒体活动的相关见解（例如，抑郁症提示帖子的数量）。使用原型，我们进行了采访（n=15）和3个焦点小组（n=13）与精神卫生临床医生：精神科医生，临床心理学家，并持牌临床社会工作者。转录的定性数据进行了分析，使用主题分析。临床医生报告说，该原型可以支持临床医生与患者在疾病设置、症状交流和导航患者口头报告方面的合作。他们提出了潜在的使用场景，例如在咨询前审查原型，并在患者错过咨询时使用原型。他们还推测了潜在的负面后果：患者可能会觉得他们正在被监控，这可能会产生负面影响，并且原型的使用可能会增加临床医生的工作量，这已经很难管理了。最后，我们的参与者表达了对原型的担忧：他们不确定患者的社交媒体帐户是否代表他们的实际行为;他们想了解机器学习算法如何以及何时无法满足他们的信任期望;他们担心无法正确响应洞察的情况，特别是临床环境之外的紧急情况。我们的研究结果支持了从患者的社交媒体账户数据中获得的计算心理健康见解的潜力，特别是在心理治疗咨询的背景下。然而，透明的算法信息和机构支持等社会技术问题应在未来设计可实施和可持续技术的努力中得到解决。
Previous studies have suggested that social media data, along with machine learning algorithms, can be used to generate computational mental health insights. These computational insights have the potential to support clinician-patient communication during psychotherapy consultations. However, how clinicians perceive and envision using computational insights during consultations has been underexplored. The aim of this study is to understand clinician perspectives regarding computational mental health insights from patients’ social media activities. We focus on the opportunities and challenges of using these insights during psychotherapy consultations. We developed a prototype that can analyze consented patients’ Facebook data and visually represent these computational insights. We incorporated the insights into existing clinician-facing assessment tools, the Hamilton Depression Rating Scale and Global Functioning: Social Scale. The design intent is that a clinician will verbally interview a patient (eg, How was your mood in the past week?) while they reviewed relevant insights from the patient’s social media activities (eg, number of depression-indicative posts). Using the prototype, we conducted interviews (n=15) and 3 focus groups (n=13) with mental health clinicians: psychiatrists, clinical psychologists, and licensed clinical social workers. The transcribed qualitative data were analyzed using thematic analysis. Clinicians reported that the prototype can support clinician-patient collaboration in agenda-setting, communicating symptoms, and navigating patients’ verbal reports. They suggested potential use scenarios, such as reviewing the prototype before consultations and using the prototype when patients missed their consultations. They also speculated potential negative consequences: patients may feel like they are being monitored, which may yield negative effects, and the use of the prototype may increase the workload of clinicians, which is already difficult to manage. Finally, our participants expressed concerns regarding the prototype: they were unsure whether patients’ social media accounts represented their actual behaviors; they wanted to learn how and when the machine learning algorithm can fail to meet their expectations of trust; and they were worried about situations where they could not properly respond to the insights, especially emergency situations outside of clinical settings. Our findings support the touted potential of computational mental health insights from patients’ social media account data, especially in the context of psychotherapy consultations. However, sociotechnical issues, such as transparent algorithmic information and institutional support, should be addressed in future endeavors to design implementable and sustainable technology.
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