Using Human‐Centred Design to Codesign Patient Engagement Tools With a Patient Advisory Council: Successes and Challenges
Bibliographic record
Abstract
BACKGROUND: Co-build is one of the four pillars of the Patient Engagement Framework from the Canadian Institutes of Health Research Strategy for Patient Oriented Research. Collaborating with Patient Research Partners (PRPs) using co-build approaches can enhance the applicability of healthcare tools produced. Human Centred Design (HCD), a problem-solving methodology focused on creating functional solutions for users, offers a promising approach to co-building patient engagement tools. OBJECTIVE: To describe the process of using a HCD approach to co-build patient engagement tools with PRPs and to identify successes and challenges encountered. METHODS: A HCD working group was formed from a Patient Advisory Council (PAC) that supports a research program to optimize laboratory test ordering in hospitalized patients. The HCD working group included nine PRPs, two patient engagement team members, and a HCD specialist. The working group employed the Double Diamond 4D design methodology: Discover, Define, Design, and Deliver, along with patient engagement principles of mutual respect, inclusiveness, support, and co-build. At the conclusion of the HCD process, we conducted a semi-structured debrief session to obtain perspectives on challenges and successes from all working group members. These were then summarized and collated iteratively with feedback from the group members. RESULTS: The working group met 31 times in 12 months and co-developed three patient engagement tools (an infographic, a video, and a website) to educate and engage hospitalized patients about the bloodwork process. HCD working group members valued the diverse and inclusive environment within the group, the available enrichment opportunities in HCD and qualitative research, and presence of patient engagement team members. Challenges noted included delays in timelines due to difficulties with consensus-building and redundancy in discussion topics. CONCLUSION: HCD approaches can be effectively combined with the principles of patient engagement to facilitate co-building with PRPs in healthcare. Future research is required to further the evidence for these strategies and their application in co-building processes, including use of clear project mapping and timelines and transparent consensus-building approaches. PATIENT OR PUBLIC CONTRIBUTION: A PAC that consisted of nine PRPs guided this study. PRPs collaborated throughout the study. The current six PRPs were involved in the decision to write and are co-authors on this manuscript. PAC members had participated equally in the conduct of a prior qualitative study to understand patient needs about bloodwork processes in hospitals. With the guidance of a HCD specialist, PRPs contributed to decisions on content, wording, and imagery for the tools. The PAC members are currently collaborating on a study to implement these tools in hospitals and to evaluate the utility from a patient perspective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".