Consumers’ Needs for Laboratory Results Portals: Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Over the last decade, there has been an increase in the number of health care consumers (ie, patients, citizens, and laypeople) with access to their laboratory results through portals. However, many portals are not designed with the consumer in mind, which can limit communication effectiveness and consumer empowerment. OBJECTIVE: We aimed to study design facilitators and barriers affecting consumer use of a laboratory results portal. We sought to identify modifiable design attributes to inform future interface specifications and improve patient safety. METHODS: A web-based questionnaire with open- and closed-ended items was distributed to consumers in British Columbia, Canada. Open-ended items with affinity diagramming and closed-ended questions with descriptive statistics were analyzed. RESULTS: Participants (N=30) preferred reviewing their laboratory results through portals rather than waiting to see their provider. However, respondents were critical of the interface design (ie, interface usability, information completeness, and display clarity). Scores suggest there are display issues impacting communication that require urgent attention. CONCLUSIONS: There are modifiable usability, content, and display issues associated with laboratory results portals that, if addressed, could arguably improve communication effectiveness, patient empowerment, and health care safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".