Development of a Cocreated Decision Aid for Patients With Depression-Combining Data-Driven Prediction With Patients' and Clinicians' Needs and Perspectives: Mixed Methods Study.

Q2 Medicine
Kaying Kan, Frederike Jörg, Klaas J Wardenaar, Frank J Blaauw, Maarten F Brilman, Ellen Visser, Dennis Raven, Dwayne Meijnckens, Erik Buskens, Danielle C Cath, Bennard Doornbos, Robert A Schoevers, Talitha L Feenstra
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引用次数: 0

Abstract

Background: Major depressive disorders significantly impact the lives of individuals, with varied treatment responses necessitating personalized approaches. Shared decision-making (SDM) enhances patient-centered care by involving patients in treatment choices. To date, instruments facilitating SDM in depression treatment are limited, particularly those that incorporate personalized information alongside general patient data and in cocreation with patients.

Objective: This study outlines the development of an instrument designed to provide patients with depression and their clinicians with (1) systematic information in a digital report regarding symptoms, medical history, situational factors, and potentially successful treatment strategies and (2) objective treatment information to guide decision-making.

Methods: The study was co-led by researchers and patient representatives, ensuring that all decisions regarding the development of the instrument were made collaboratively. Data collection, analyses, and tool development occurred between 2017 and 2021 using a mixed methods approach. Qualitative research provided insight into the needs and preferences of end users. A scoping review summarized the available literature on identified predictors of treatment response. K-means cluster analysis was applied to suggest potentially successful treatment options based on the outcomes of similar patients in the past. These data were integrated into a digital report. Patient advocacy groups developed treatment option grids to provide objective information on evidence-based treatment options.

Results: The Instrument for shared decision-making in depression (I-SHARED) was developed, incorporating individual characteristics and preferences. Qualitative analysis and the scoping review identified 4 categories of predictors of treatment response. The cluster analysis revealed 5 distinct clusters based on symptoms, functioning, and age. The cocreated I-SHARED report combined all findings and was integrated into an existing electronic health record system, ready for piloting, along with the treatment option grids.

Conclusions: The collaboratively developed I-SHARED tool, which facilitates informed and patient-centered treatment decisions, marks a significant advancement in personalized treatment and SDM for patients with major depressive disorders.

开发一种共同创建的抑郁症患者决策辅助工具——将数据驱动预测与患者和临床医生的需求和观点相结合:混合方法研究
背景:重度抑郁症显著影响个体的生活,不同的治疗反应需要个性化的方法。共享决策(SDM)通过让患者参与治疗选择来增强以患者为中心的护理。迄今为止,在抑郁症治疗中促进SDM的工具是有限的,特别是那些将个性化信息与一般患者数据结合起来并与患者共同创造的工具。目的:本研究概述了一种仪器的开发,旨在为抑郁症患者及其临床医生提供(1)有关症状、病史、情境因素和潜在成功治疗策略的系统信息,以及(2)客观的治疗信息,以指导决策。方法:该研究由研究人员和患者代表共同领导,确保有关仪器开发的所有决策都是协作做出的。数据收集、分析和工具开发在2017年至2021年间使用混合方法进行。定性研究提供了对最终用户的需求和偏好的洞察。一项范围综述总结了现有文献中已确定的治疗反应预测因素。K-means聚类分析应用于根据过去类似患者的结果建议潜在成功的治疗方案。这些数据被整合到一个数字报告中。患者权益组织开发了治疗方案网格,以提供基于证据的治疗方案的客观信息。结果:开发了纳入个体特征和偏好的抑郁症共同决策工具(I-SHARED)。定性分析和范围审查确定了治疗反应的4类预测因素。聚类分析显示基于症状、功能和年龄的5个不同的聚类。共同创建的I-SHARED报告结合了所有发现,并与治疗选择网格一起集成到现有的电子健康记录系统中,准备进行试点。结论:合作开发的I-SHARED工具促进了知情和以患者为中心的治疗决策,标志着重度抑郁症患者个性化治疗和SDM的重大进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Participatory Medicine
Journal of Participatory Medicine Medicine-Medicine (miscellaneous)
CiteScore
3.20
自引率
0.00%
发文量
8
审稿时长
12 weeks
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