User-Centered Design of an Electronic COPD Patient Questionnaire for Clinical Decision Support.

PURPOSE: Effective COPD management depends on structured symptom and risk assessment, individualized therapy, and action plan provision, yet these are inconsistently delivered in routine care. Electronic health record (EHR)-integrated clinical decision support systems (CDSSs) can help close these gaps but require accurate, structured patient data to drive their recommendations. Digital pre-visit questionnaires are increasingly used to collect such data, but patient preferences for their content and design remain poorly understood, particularly in COPD.

PATIENTS AND

METHODS

We used a user-centered design process to develop an electronic COPD questionnaire to inform an EHR-integrated CDSS. Content was mapped to planned CDSS actions and built into a mobile-optimized prototype using eHealth design principles. We face validated the prototype with 10 people living with COPD, then refined it through successive focus groups until saturation. Usability was assessed with the System Usability Scale (SUS) and Likert ratings and transcripts were analyzed thematically.

RESULTS

Face validation (n=10) and three focus groups (n=13) were conducted to saturation. Four critical issues were identified and addressed. Usability was high throughout (mean SUS 88.07, "excellent"), with strong Likert ratings for functionality, navigation, and appearance. Thematic analysis yielded four themes: standardized measures versus real-life experience; usability and format as enablers; support for communication, memory, and efficient care; and desire for transparency and education.

CONCLUSION

Through iterative co-design with older adults, we developed an EHR-integrated electronic COPD questionnaire with excellent usability and relevance to patients' lived experience. Patients anticipated benefits beyond data capture: improved symptom recall, visit preparation, and provider efficiency. This is one of the first electronic COPD questionnaires developed through structured patient co-design and qualitative evaluation, providing a transferable model for tools that inform clinical decision support.