Patients’ Perceptions of Using a Digital Previsit Tool in Outpatient Settings (Part 2): Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Patients with long-term conditions, such as stroke, require regular follow-up visits to health care professionals to identify changes in symptoms. The digital previsit tool Strokehälsa (Strokehealth) has been designed to encourage individuals with stroke to reflect on stroke-related health concerns before a follow-up visit, thereby potentially enhancing their engagement during the visit. Strokehealth has previously been evaluated using a patient satisfaction survey (part 1), but there remains a need to further explore patients' perceptions and needs to optimize its functionality before broader implementation. OBJECTIVE: The overall aim was to attain deeper insights into patients' views and experiences of using the digital previsit tool Strokehealth before a follow-up visit. A secondary aim was to identify potential improvements to the tool based on these insights. METHODS: For this qualitative study, patients who had used Strokehealth version 1.0 before a follow-up visit were recruited through the previous survey between November 2020 and June 2021. Individual semistructured interviews were conducted, and data were analyzed using reflexive thematic analysis. Subsequent workshops were held with people with firsthand experiences of stroke, other stakeholders (including health professionals and researchers), and a web consultant to finalize decisions regarding adjustments to be implemented in Strokehealth version 2.0. RESULTS: Interviews were conducted with 33 participants (23 men and 10 women), with a median age of 67 (IQR 55-76) years. Analysis of the data regarding participants' experiences of using Strokehealth revealed three overarching themes: (1) a supporting tool for preparing dialogue and identifying needs, (2) how Strokehealth is introduced and communicated affects perceived usability, and (3) the wording and structure of Strokehealth influences the response process. The findings captured various aspects of receiving and using the digital previsit tool, highlighting its simplicity and purpose. Overall, Strokehealth was well received and contributed to a sense of being well cared for. Participants generally not only found Strokehealth easy to use but also shared suggestions on how to better address stroke-related issues, such as mental fatigue or pain. Examples of changes that have been implemented in Strokehealth version 2.0, based on participant feedback, include improved explanatory texts and expanded opportunities for free text. CONCLUSIONS: The findings indicate that the freely available digital previsit tool Strokehealth was generally well received by patients with stroke who were scheduled for follow-up visits in outpatient settings. TRIAL REGISTRATION: Researchweb 275135; https://www.researchweb.org/is/vgr/project/275135.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".