Evaluation of a Digital Previsit Tool for Identifying Stroke-Related Health Problems Before a Follow-Up Visit (Part 1): Survey Study
Bibliographic record
Abstract
BACKGROUND: Stroke may lead to various disabilities, and a structured follow-up visit is strongly recommended within a few months after an event. To facilitate this visit, the digital previsit tool "Strokehealth" was developed for patients to fill out in advance. The concept Strokehälsa (or Strokehealth) was initially developed in-house as a Windows application, later incorporated in 1177.se. OBJECTIVE: The study's primary objective was to use a patient satisfaction survey to evaluate the digital previsit tool Strokehealth when used before a follow-up visit, with a focus on feasibility and relevance from the perspective of people with stroke. Our secondary objective was to explore the extent to which the previsit tool identified stroke-related health problems. METHODS: Between November 2020 and June 2021, a web-based survey was sent to patients who were scheduled for a follow-up visit after discharge from a stroke unit and had recently filled in the previsit tool. The survey covered demographic characteristics, internet habits, and satisfaction rated using 5 response options. Descriptive statistics were used to present data from both the previsit tool and the survey. We also compared the characteristics of those who completed the previsit tool and those who did not, using nonparametric statistics. Free-text responses were thematically analyzed. RESULTS: All patients filling out the previsit tool (80/171; age: median 67, range 32-91 years) were community-dwelling. Most had experienced a mild stroke and reported a median of 2 stroke-related health problems (range 0-8), and they were significantly younger than nonresponders (P<.001). The survey evaluating the previsit tool was completed by 73% (58/80; 39 men). The majority (48/58, 83%) reported using the internet daily. Most respondents (56/58, 97%) were either satisfied (n=15) or very satisfied (n=41) with how well the previsit tool captured their health problems. The highest level of dissatisfaction was related to the response options in Strokehealth (n=5). Based on the free-text answers to the survey, we developed 4 themes. First, Strokehealth was perceived to provide a structure that ensured that issues would be emphasized and considered. Second, user-friendliness and accessibility were viewed as acceptable, although respondents suggested improvements. Third, participants raised awareness about being approached digitally for communication and highlighted the importance of how to be approached. Fourth, their experiences with Strokehealth were influenced by their perceptions of the explanatory texts, the response options, and the possibility of elaborating on their answers in free text. CONCLUSIONS: People with stroke considered the freely available previsit tool Strokehealth feasible for preparing in advance for a follow-up visit. Despite high satisfaction with how well the tool captured their health problems, participants indicated that additional free-text responses and revised information could enhance usability. Improvements need to be considered in parallel with qualitative data to ensure that the tool meets patient needs. 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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".