From Testers to Cocreators—the Value of and Approaches to Successful Patient Engagement in the Development of eHealth Solutions: Qualitative Expert Interview Study
Bibliographic record
Abstract
BACKGROUND: Research has shown that patient engagement is most commonly done at the beginning of research or to test readily available prototypes and less commonly done in other phases such as the execution phases. Previous studies have reported that patients are usually assigned a consultative rather than a decision-making role in health service planning and evaluation. OBJECTIVE: This study had 2 objectives: to better understand the challenges and opportunities in the inclusion of patients in the development of eHealth technologies and ideas on how to overcome the identified gaps and to create a research-based end-to-end practical blueprint that can guide the relevant stakeholders to successfully engage patients as cocreators in all human-centered design phases rather than mere testers of preplanned prototypes. METHODS: Key informant interviews were conducted using in-depth semistructured interviews with 20 participants from 6 countries across Europe. This was followed by a focus group to validate the initial findings. Participants encompassed all the relevant stakeholder groups including patient experts, eHealth experts, health technology providers, clinicians, pharma executives, and health insurance experts. RESULTS: This study shows that engaging patients in eHealth development can help provide different types of value; namely, identifying unmet needs, better usability and desirability, better fit into the patient journey, better adoption and stickiness, better health outcomes, advocacy and trust, a sense of purpose, and better health equity and access. However, the participants agreed that patients are usually engaged too late in the development process, mostly assuming a sounding role in testing a ready-made prototype. The justification for these gaps in engagement is driven by some prominent barriers, notably compliance risks, patient-related factors, power dynamics, patient engagement as lip service, poor value perception, lack of resources, mistrust, and inflexibility. On the positive side, the participants also reflected on facilitators for better patient engagement; for instance, engaging through engagement partners, novel approaches such as the rise of professional patient experts, embedding patients in development teams, expectation management, and professional moderation services. CONCLUSIONS: Overcoming the current gaps in patient engagement in eHealth development requires consolidated efforts from all stakeholders in a complex health care ecosystem. The shift toward more patient-driven eHealth development requires education and awareness; frameworks to monitor and evaluate the value of patient engagement; regulatory clarity and simplification; platforms to facilitate patient access and identification; patient incentivization, transparency, and trust; and a mindset shift toward value-based health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".