MétaCan
Menu
Back to cohort

The Introduction and Evaluation of Mobile Devices to Improve Access to Patient Records

2014· book-chapter· en· W4245334529 on OpenAlexaff
Jonn Wu, John Waldron, Shaina Reid, Jeff Barnett

Bibliographic record

VenueAdvances in healthcare information systems and administration book series · 2014
Typebook-chapter
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of VictoriaBC Cancer Agency
Fundersnot available
KeywordsWorkflowWorkstationComputer scienceOperating systemDatabase

Abstract

fetched live from OpenAlex

Prompt and efficient access to patient records is vital in providing optimal patient care. The Cancer Agency Information System (CAIS) is the primary patient record repository for the British Columbia Cancer Agency (BCCA) but is only accessible on traditional computer workstations. The BCCA clinics have significant space limitations resulting in multiple healthcare professionals sharing each workstation. Furthermore, workstations are not available in examination rooms. A novel and cost-efficient solution is necessary to improve clinician access to CAIS. This prompted the BCCA and the Provincial Health Services Authority (PHSA) Information Management Information Technology Services (IMITS) team to embark on an innovative provincial collaboration to introduce and evaluate the impact of a mobile device to improve access to CAIS. The project consisted of 2 phases with over 90 participants from multiple clinical disciplines across BCCA sites and other PHSA facilities. Phase I evaluated the adoptability, effectiveness, and costs associated with providing access to CAIS using desktop virtualization via Citrix. Citrix is a server solution that provides remote access to clients via the Web or to dummy terminals in a network. Phase II incorporated the feedback and findings from Phase I to develop a customized mobile application. Phase II also addressed privacy and security requirements and included additional users and workflows. This is explored in this chapter.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.003
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 head0.036
GPT teacher head0.411
Teacher spread0.374 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in healthcare information systems and administration book seriesSame topicElectronic Health Records SystemsFrench-language works237,207