MétaCan
Menu
Back to cohort
Record W2810419608 · doi:10.1177/2327857918071022

Optimizing EMR User Experience: A Human Factors Approach to Hardware Assessment and Design for Inpatient and Emergency Units

2018· article· en· W2810419608 on OpenAlex
Catherine Dulude, Chantal Trudel, W. James King, Karen Macaulay, Jennifer Gillert, Leilla Czunyi, Sanaz Hafezi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the International Symposium on Human Factors and Ergonomics in Health Care · 2018
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of OttawaCarleton UniversityChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsWorkflowDocumentationContext (archaeology)Computer scienceParticipatory designKey (lock)Process managementComponent (thermodynamics)User-centered designKnowledge managementEngineering managementOperations managementEngineeringDatabaseHuman–computer interactionComputer securityParallels

Abstract

fetched live from OpenAlex

Many factors contribute to the successful implementation and adoption of electronic medical records (EMRs). Easy access to the EMR, where and when required by clinicians, is a key component of adoption and end-user satisfaction with the system. A pediatric hospital implementing an integrated EMR used multiple methods within an iterative human-centered design (HCD) framework to develop hardware and access solutions supporting future EMR workflows in Inpatient and Emergency Departments. Context of use analysis, participatory design methods, preliminary analysis of evaluative simulations and tacit knowledge of the project team led to the development of guiding principles for hardware implementation and solutions supporting just-in-time documentation within the constraints of existing facility design.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.123
GPT teacher head0.425
Teacher spread0.303 · 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