JMIR Publications

ABSTRACT

Background:

Human-centered design (HCD) plays a crucial role in healthcare by helping designers understand patient needs and improve patient experiences and safety through empathetic engagement. In the early design stages, qualitative and generative methods are commonly used to explore patients’ contexts and needs, emphasizing active patient involvement to ensure that design insights accurately reflect real experiences and enhance both design effectiveness and patient empowerment. However, certain challenges arise in the early stage of the HCD process, including (1) high vulnerability of patient participants, (2) fewer diverse and representative patient groups due to recruitment challenges, and (3) problem framing across diverse patient experiences. To address these challenges while embracing the system-level HCD perspective, we propose a data-enabled mixed method combining large-scale patient digital research (Module A) and in-depth patient engagement research (Module B).

Objective:

We conducted an exploratory case study on developing a remote patient monitoring system for colorectal cancer patients in the follow-up stage with a focus on quality of life. The study aimed to demonstrate the value of our proposed method and examine how it addresses the three challenges identified in the background section.

Methods:

In Module A, we adopted a combined approach using machine learning and patient journey mapping, referred to as the patient community journey map. In Module B, we conducted a diary study using a prototype with 4 patients, followed by 30-minute follow-up interviews.

Results:

Based on 212,107 posts, 37 topics and 10 upper clusters were produced in Module A. Topics related to the home context contained more emotional content compared with those in the hospital context. During the follow-up stage, patients placed greater emphasis on their social and mental health than during the diagnosis and treatment stages, revealing a gap in existing remote patient monitoring systems. Addressing this reframed gap, we developed a prototype that was used in the diary study in Module B. Patients responded positively to the prototype, noting that remote monitoring that involves social and mental well-being can help them better understand themselves, enhance self-awareness, and improve communication with their doctors.

Conclusions:

In this study, we developed and identified the value of combining large-scale patient digital research (Module A) with in-depth patient engagement research (Module B) in the early stages of human-centered healthcare design for patients. The value of this method lies in its ability to support problem reframing and foster empathy and understanding of patient vulnerability before conducting engagement research. At the same time, it captures the diversity and depth of patient voices, providing evidence-based insights that ultimately strengthen the effectiveness of patient engagement research. The insights and capabilities provided by this method can help overcome the challenges encountered in the early stages of human-centered design for patients.