MétaCan
Menu
Back to cohort
Record W2102255819 · doi:10.12927/hcq.2013.20889

Innovation in Managing the Referral Process at a Canadian Pediatric Hospital

2009· article· en· W2102255819 on OpenAlexaffabout
Daune MacGregor, Sandra Parker, Sharon MacMillan, Irene Blais, Eugene Wong, Chris Robertson, Cindy Bruce-Barrett

Bibliographic record

VenueHealthcare Quarterly · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsReferralConfidentialityTriageMedical emergencyMedicineAmbulatory careHealth careAmbulatoryNursingFamily medicineComputer scienceComputer security

Abstract

fetched live from OpenAlex

The provision of timely and optimal patient care is a priority in pediatric academic health science centres. Timely access to care is optimized when there is an efficient and consistent referral system in place. In order to improve the patient referral process and, therefore, access to care, an innovative web-based system was developed and implemented. The Ambulatory Referral Management System enables the electronic routing for submission, review, triage and management of all outpatient referrals. The implementation of this system has provided significant metrics that have informed how processes can be improved to increase access to care. Use of the system has improved efficiency in the referral process and has reduced the work associated with the previous paper-based referral system. It has also enhanced communication between the healthcare provider and the patient and family and has improved the security and confidentiality of patient information management. Referral guidelines embedded within the system have helped to ensure that referrals are more complete and that the patient being referred meets the criteria for assessment and treatment in an ambulatory setting. The system calculates and reports on wait times, as well as other measures.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.268
Teacher spread0.251 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueHealthcare QuarterlySame topicHealthcare Systems and TechnologyFrench-language works237,207