Accepted for/Published in: Journal of Participatory Medicine
Date Submitted: Oct 9, 2024
Date Accepted: May 25, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The development of a co-created decision aid for patients with depression: Combining data-driven prediction with patients’ and clinicians’ needs and perspectives
ABSTRACT
Background:
To enable patient-centred treatment choices, shared decision-making (SDM) is essential. To date, instruments facilitating SDM in depression treatment are scarce, especially those that add personalized information, next to more general patient information. Co-creation is essential and seldom used in the development of such tools.
Objective:
We describe the development of an instrument that provides patients with depression and their clinicians with: (1) systematic information regarding symptoms, medical history, situational factors and potentially successful treatment strategies in a digital report and (2) objective treatment information guiding treatment decisions.
Methods:
The study was co-led by researchers and patient representatives, indicating all decisions regarding the development of the instrument were taken together. Data collection, analyses and tool development took place between 2017 – 2021. A mixed-methods approach was applied. Qualitative research provided insight into the end-users’ needs and preferences. A scoping review provided a summary of the available literature on identified predictors of treatment response. K-means cluster analysis was applied to suggest potentially successful treatment options based on similar patients and their outcomes in the past. These data were combined in a digital report. Treatment option grids were developed by patient advocacy groups to provide objective information on evidence-based treatment options.
Results:
The ‘Instrument for SDM in Depression’ (I-SHARED) was developed, incorporating individual characteristics and preferences. Qualitative analysis and the scoping review resulted in the identification of four categories of predictors of treatment response. The cluster analysis identified five distinct clusters based on symptoms, functioning and age. The co-created I-SHARED report combined all findings and was integrated into an existing electronic health record system ready for piloting, together with the treatment option grids.
Conclusions:
The collaboratively developed decision aid for depression, including a clustering algorithm to predict potentially successful treatment options, has the potential to support SDM between patients and clinicians.
Citation
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