Engaging patients as healthcare partners through the meaningful use of Voxe: A digital patient-reported outcome platform
Bibliographic record
Abstract
Background: As health services shift towards more patient-centred care, the importance of patient-reported outcome measures (PROMs) is increasingly recognized. PROMs can effectively capture patients’ perspectives and enable meaningful engagement. This research program aims to improve health outcomes for pediatric patients by systematically implementing PROMs into clinical practice. We have targeted methodological and practical decisions needed to guide effective integration of PROMs into care settings with a phased approach, including a systematic review (Phase 1), key stakeholder interviews (Phase 2), and a consensus workshop (Phase 3). The preliminary evidence that informed this project addressed critical elements within implementation science, including assessing fit and readiness for change, establishing stakeholder buy-in and fostering a supportive environment. In this study, we designed (Phase 4) and tested the usability (Phase 5) of an electronic PROM (ePROM) platform called Voxe. Methods: A user-centred approach, in which end-users (i.e., patients and healthcare providers (HCPs)) are central to the design process and usability testing, guided Voxe platform creation. Iterative testing sessions involved participants from The Hospital for Sick Children (SickKids) and Children’s Hospital of Eastern Ontario (CHEO) completing (1) tasks on design wireframes and prototypes to evaluate effectiveness and efficiency, (2) the Microsoft Desirability Toolkit, a system usability scale, and (3) a semi-structured interview to assess satisfaction and gather user feedback. This methodology was implemented to ʻtest, learn and improveʼ Voxe prior to full development and launch. Results: Forty-nine patients aged 8-17 years (n=25 solid organ transplant patients receiving care at SickKids; n=24 hematology and oncology patients receiving care at CHEO) and 38 of their HCPs (n=22 HCPs from SickKids; n=16 HCPs from CHEO) participated. Iterative and sequential testing rounds demonstrated improved effectiveness as the proportion of successfully completed tasks increased from 74% to 85%. Efficiency improved as time-to-task decreased from 23.2 to 15.8 seconds. Patients described Voxe as “fun”, “friendly”, “helpful”, “easy”, “calm”, “clear” and “creative”. Patients shared “[Voxe] makes you feel like you’re welcome in the hospital” and “…it feels like you can get better with this app”. HCPs highlighted that Voxe is “intuitive” and enables “a more patient-centered model of care”. HCPs also remarked “it [Voxe] is very user friendly”, “it [Voxe] is pretty clear and easy to use”, and “I can see Voxe naturally fitting into what we do already”. Conclusion: Findings will influence how Voxe looks and operates to drive successful and sustainable adoption and the meaningful use of digital solutions and shared data for information and care management. Although solid organ transplant patients, hematology and oncology patients, and their HCPs participated in the design and testing, Voxe could be implemented with any pediatric population as it was built to accommodate any ePROM. Voxe acknowledges and supports patients as partners in their health and healthcare and fosters meaningful patient engagement. Future research will assess the implementation effectiveness of the Voxe ePROM platform. Ultimately, Voxe leverages eHealth technology as an innovative approach to meaningfully capture and integrate patients’ voices and transform their care experiences.
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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 | 0.050 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".