Patient and Family Engagement Approaches for Digital Health Initiatives: Protocol for a Case Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Digital health initiatives such as patient portals, virtual care platforms, and smartphone-based apps are being implemented at a rapid pace in health care organizations worldwide. This is often done to improve access beyond traditional in-person care and enhance care quality. Recent studies have indicated that better outcomes of using these initiatives and technologies may be achieved when patients and their family members are engaged in all aspects of planning, implementation, use, and evaluation. However, little guidance exists for how health care administrators can achieve effective engagement in digital health initiatives specifically. OBJECTIVE The objective of this study is to document processes related to planning and implementing patient and family engagement (PFE) in digital health initiatives. This information will be used to develop tangible resources (eg, a field guide) that other organizations can use to implement PFE approaches for digital health initiatives in their organizations. METHODS A previously developed multidimensional conceptual framework for PFE in health and health care contexts will be used to guide this work. To understand the intricacies involved in using PFE approaches in digital health strategies, a case study will be conducted. More specifically, this work will employ an embedded single-case design with PFE in digital health initiatives at a large Canadian mental health and addictions teaching hospital. Multiple digital health projects being undertaken at the study site will be explored to better understand where the PFE is intended to support the design, implementation, and operation of the digital health platform or technology. These projects will form the individual units of analysis. Data collection will involve field notes and artifact collection by a participant observer and interviews with the various digital health project teams. Data analysis will include a content and thematic analysis, triangulation of the findings, and a chronological mapping of data to a PFE process. RESULTS Funding for this work was provided by the Canadian Institutes of Health Research (CIHR), via a Health System Impact Fellowship. As of August 2020, digital health projects that will form the case study units have been identified, and the participant observer has started to embed themselves into these projects. Although the development and collection of field notes and artifacts, respectively, have begun, interviews have not been conducted. The study is expected to conclude in September 2021. Once this study is complete, the development of a field guide and resources to support the uptake of PFE strategies in digital health will begin. CONCLUSIONS By better understanding the processes involved in PFE in digital health projects, guidance can be provided to relevant stakeholders and organizations about how to do this work in an effective manner. It is then anticipated that with the increasing use of PFE approaches, there may be improved uptake, experience, and outcomes associated with using digital health technologies. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/24274
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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 | 0.068 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.108 | 0.019 |
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".