Qualitative User-Centered Design: Programming Changes in a Self-Management Blood Pressure Application.

Abstract

Electronic patient-centered clinical decision support enhances chronic disease self-management, yet few studies describe how qualitative user feedback is systematically translated into programming improvements. The Collaboration-Oriented Approach to Controlling High Blood Pressure (COACH) is a patient-facing decision support tool for hypertension management, designed for primary care and integration with electronic health records (EHRs) and patient portals.This study aimed to describe how end-user feedback informed COACH programming decisions and the MoSCoW (Must have, Should have, Could have, Won't have) method operationalized prioritization for implementation.This secondary analysis draws on qualitative data from a Consolidated Framework for Implementation Research (CFIR)-informed data collected during a preimplementation evaluation of COACH with 70 care team members and 18 patients across three academic health systems using Cerner/Oracle and Epic EHRs. CFIR-coded data were reviewed, and design or usability "recommendations" were translated into discrete programming requirements, prioritized using the MoSCoW, and, where possible, completed before a multisite trial.Seven themes emerged: (1) Timely, actionable alerts, (2) messaging features for follow-up, (3) motivating patients through feedback, (4) reducing data entry burden, (5) setting blood pressure goals, (6) supporting workflow integration, and (7) equity and accessibility. Of the 35 potential recommendations, 25 were translated into changes and prioritized as "Must have" (12), "Should have" (6), and "Could have" (7). Our team completed 19 high-priority features (e.g., setting the blood pressure goal, structured alerts, messaging templates, and visual feedback) before the trial launch.This study demonstrates a replicable process for integrating qualitative insights into digital health programming through structured prioritization, advancing implementation-focused clinical informatics, and guiding decision-making for the future integration of patient-generated health data, such as home blood pressure monitoring.