What Factors Influence User Satisfaction in Accessing Health Data?: Insights from a Nationally Representative Survey Study of UK Adults. (Preprint)
Bibliographic record
Abstract
BACKGROUND Personal Health Records (PHRs) are increasingly promoted as tools to enhance patient engagement and healthcare efficiency. Despite their growing prevalence, limited evidence exists from nationally representative samples assessing PHR satisfaction—especially when comparing individuals with and without prior PHR experience. OBJECTIVE To examine determinants of PHR user satisfaction in a nationally representative UK sample by comparing individuals with experience using PHRs to those without, including perceptions of utility, functionality, and cost-related benefits. METHODS We surveyed a nationally representative sample of UK adults (N = [insert N here]) through an online panel. Participants were divided into two groups: (1) those with PHR experience, who answered perception-based questions about actual use, and (2) those without PHR experience, who responded to analogous questions framed hypothetically. Descriptive statistics and t-tests were used to compare satisfaction levels, perceived utility, and feature valuation across the two groups. Results were also benchmarked against data from Canadian users for broader context. RESULTS Respondents with PHR experience rated their systems positively on ease of use, scheduling efficiency, and provider communication. UK users scored PHRs slightly lower than Canadian counterparts, though systems differed in design. Both groups highly valued access to health information, lab results, and appointment scheduling. Participants reported avoiding some clinic and emergency visits due to PHR use, resulting in savings on transportation, parking, and time off work. Those without experience tended to overestimate their ability to manage family members’ health and had high expectations for PHR functionality—expectations that were not implausible but reflected current implementation gaps. Experienced users also expressed interest in broader data access and greater system convenience. CONCLUSIONS PHR usage in the UK is associated with high user satisfaction and tangible material and non-material benefits. However, there is a gap between expected and actual functionality, particularly among non-users. Policymakers and developers should consider these findings when expanding access to PHRs and improving their usability and feature set. This study adds value by using a nationally representative sample and by comparing both user and non-user perspectives.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".