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Record W4365799213 · doi:10.2196/42843

Consumers’ Needs for Laboratory Results Portals: Questionnaire Study

2023· article· en· W4365799213 on OpenAlexaffvenueabout
Helen Monkman, Janessa Griffith, Leah MacDonald, Blake Lesselroth

Bibliographic record

VenueJMIR Human Factors · 2023
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsDouglas CollegeUniversity of Victoria
Fundersnot available
KeywordsUsabilityCLARITYEmpowermentPatient portalDescriptive statisticsHealth carePsychologyMedicineWorld Wide WebNursingComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.136
GPT teacher head0.505
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes3
Has abstractyes

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