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. 2025 Jul 21:17:e67170.
doi: 10.2196/67170.

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

Kaying Kan  1 , Frederike Jörg  1   2 , Klaas J Wardenaar  1 , Frank J Blaauw  3 , Maarten F Brilman  1 , Ellen Visser  1 , Dennis Raven  1 , Dwayne Meijnckens  4 , Erik Buskens  5   6 , Danielle C Cath  1   7 , Bennard Doornbos  7 , Robert A Schoevers  1 , Talitha L Feenstra  1   5   8
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Development of a Cocreated Decision Aid for Patients With Depression-Combining Data-Driven Prediction With Patients' and Clinicians' Needs and Perspectives: Mixed Methods Study

Kaying Kan et al. J Particip Med. .

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.

Keywords: clinical decision support; data-driven; decision support; depressed; depression; depressive disorder; individualized care; individualized medicine; major depressive disorder; mental disorder; mental health; mental illness; personalized care; personalized medicine; precision care; precision medicine; shared decision-making.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1.
Figure 1.. The clustering model for the presentation of treatment data of patients with a clinically relevant improvement on the OQ-45 (Outcome Questionnaire-45) total score.
Figure 2.
Figure 2.. A snapshot of the graphical interface of the Instrument for shared decision-making in depression report.
Figure 3.
Figure 3.. Illustration of the clustering algorithm in the I-SHARED (Instrument for Shared Decision-Making in Depression) report.
See this image and copyright information in PMC

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