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Designing a clinical decision support tool to promote timely palliative care in heart failure
Yutong Men1, Nathan Wright2, Giselle O'Connor2
1Yale School of Public Health, 60 College St, New Haven, CT, 06510, USA.
Background:
Palliative care improves quality of life and reduces avoidable healthcare utilization for people with heart failure, yet referrals remain inconsistent and delayed. Clinical decision support offers a promising strategy to facilitate timely palliative care, but no tool currently exists to support palliative care decision-making specifically for this population.
Objectives:
To identify clinician needs, contextual factors, and design requirements to inform the development of a clinical decision support tool to promote timely palliative care for hospitalized patients with heart failure.
Methods:
Guided by the User-Centered Framework for Implementation of Technology, we conducted a qualitative descriptive study using focus groups and interviews with referring clinicians (hospitalists and cardiologists) and palliative care clinicians across two hospitals in an academic health system. Rapid qualitative and content analysis were used to identify themes related to workflow, decision-making, and tool design.
Results:
Twenty-five clinicians participated. Clinicians described workflow challenges, goals for earlier palliative care involvement, and barriers such as uncertainty about referral timing. Informational needs included prognostic indicators and healthcare utilization data. Clinicians reported limited experience with palliative-specific decision support and expressed strong dislike for interruptive alerts that disrupt workflow. Preferred features included objective markers of clinical deterioration, tailored recommendations based on clinical acuity, integration into existing workflows, and clear visuals.
Conclusions:
Clinicians identified critical informational, contextual, and design requirements for a clinical decision support tool to promote timely palliative care in heart failure. These findings directly inform tool design, workflow integration, and implementation strategies, and will guide future pilot testing and clinical evaluation.
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