Hallgren Kevin A, Bauer Amy M, Atkins David C
Department of Psychiatry and Behavioral Sciences, Behavioral Research in Technology and Engineering (BRiTE) Center, University of Washington, WA, USA.
Depress Anxiety. 2017 Jun;34(6):494-501. doi: 10.1002/da.22640. Epub 2017 Apr 28.
Clinical decision making encompasses a broad set of processes that contribute to the effectiveness of depression treatments. There is emerging interest in using digital technologies to support effective and efficient clinical decision making. In this paper, we provide "snapshots" of research and current directions on ways that digital technologies can support clinical decision making in depression treatment. Practical facets of clinical decision making are reviewed, then research, design, and implementation opportunities where technology can potentially enhance clinical decision making are outlined. Discussions of these opportunities are organized around three established movements designed to enhance clinical decision making for depression treatment, including measurement-based care, integrated care, and personalized medicine. Research, design, and implementation efforts may support clinical decision making for depression by (1) improving tools to incorporate depression symptom data into existing electronic health record systems, (2) enhancing measurement of treatment fidelity and treatment processes, (3) harnessing smartphone and biosensor data to inform clinical decision making, (4) enhancing tools that support communication and care coordination between patients and providers and within provider teams, and (5) leveraging treatment and outcome data from electronic health record systems to support personalized depression treatment. The current climate of rapid changes in both healthcare and digital technologies facilitates an urgent need for research, design, and implementation of digital technologies that explicitly support clinical decision making. Ensuring that such tools are efficient, effective, and usable in frontline treatment settings will be essential for their success and will require engagement of stakeholders from multiple domains.
临床决策涵盖了一系列广泛的过程，这些过程有助于提高抑郁症治疗的效果。人们越来越关注利用数字技术来支持有效且高效的临床决策。在本文中，我们提供了关于数字技术如何支持抑郁症治疗临床决策的研究及当前方向的“快照”。我们先回顾了临床决策的实际方面，然后概述了技术在哪些研究、设计和实施机会中可能会增强临床决策。围绕旨在改善抑郁症治疗临床决策的三个既定行动来组织对这些机会的讨论，包括基于测量的护理、综合护理和个性化医疗。研究、设计和实施工作可以通过以下方式支持抑郁症的临床决策：（1）改进将抑郁症症状数据纳入现有电子健康记录系统的工具；（2）加强对治疗保真度和治疗过程的测量；（3）利用智能手机和生物传感器数据为临床决策提供信息；（4）增强支持患者与提供者之间以及提供者团队内部沟通和护理协调的工具；（5）利用电子健康记录系统中的治疗和结果数据来支持个性化抑郁症治疗。当前医疗保健和数字技术都在迅速变化的环境促使迫切需要对明确支持临床决策的数字技术进行研究、设计和实施。确保这些工具在一线治疗环境中高效、有效且可用对于其成功至关重要，并且需要多个领域的利益相关者参与其中。
