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Record W2025523512 · doi:10.1080/17538150802127223

Usability of a mobile electronic medical record prototype: a verbal protocol analysis

2008· article· en· W2025523512 on OpenAlexafffund
Robert Wu, Mike Orr, Mark Chignell, Sharon E. Straus

Bibliographic record

VenueInformatics for Health and Social Care · 2008
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of TorontoUniversity of CalgarySt. Michael's Hospital
FundersUniversity of Toronto
KeywordsUsabilityMobile deviceProtocol (science)Medical recordPoint of careComputer scienceElectronic medical recordPoint (geometry)Internet privacyMedical emergencyMedicineWorld Wide WebHuman–computer interactionNursingAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Point of care access to electronic medical records may provide clinicians with the information they want when they need it and may in turn improve patient safety. Yet providing an electronic medical record on handheld devices presents many usability challenges, and it is unclear whether clinicians will use them. METHODS: An iterative design process for the development and evaluation of a prototype of a mobile electronic medical record was performed. Usability sessions were conducted in which physicians were asked to 'think aloud' while working through clinical scenarios using the prototype. Verbal protocol analysis, which consists of coding utterances, was conducted on the transcripts from the sessions and common themes were extracted. RESULTS: Usability sessions were held with five family physicians and four internists with varying levels of computer expertise. Physicians were able to use the device to complete 52 of 54 required tasks. Users commented that it was intuitive (9/9), would increase accessibility (5/9) but for them to use it, it would need the system to be fast and time-saving (5/9). Users had difficulty entering information (5/9) and reading the screen (4/9). In terms of functionality, users had concerns about completeness of information (6/9), details of ordering (5/9) and desired billing functionality (5/9) and integration with other systems (4/9). CONCLUSIONS: While physicians can use mobile electronic medical records in realistic scenarios, certain requirements likely need to be met including a fast system with easy data selection, easy data entry and improved display before widespread adoption occurs.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.042
GPT teacher head0.467
Teacher spread0.425 · 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 designNot applicable
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

Citations30
Published2008
Admission routes2
Has abstractyes

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