MétaCan
Menu
Back to cohort
Record W2783187789 · doi:10.1371/journal.pone.0190247

The impact of electronic consultation on a Canadian tertiary care pediatric specialty referral system: A prospective single-center observational study

2018· article· en· W2783187789 on OpenAlexaffabout
Clare Liddy, Amir Afkham, Julia Kurzawa, Nishard Abdeen, Tobey Audcent, Matthew Bromwich, Jason Brophy, Sasha Carsen, Annick Fournier, Leigh Fraser-Roberts, Hazen Gandy, Charles Hui, Donna L. Johnston, Kathryn Keely, Ken Kontio, Christine Lamontagne, Nathalie Major, Michael D. O’Connor, Dhenuka Radhakrishnan, Joe Reisman, Marjorie Robb, Lindy Samson, Erick Sell, William M. Splinter, Judy van Stralen, Sunita Venkateswaran, Kimmo Murto

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsOttawa HospitalChildren's Hospital of Eastern OntarioAgricultural Research Institute of OntarioBruyèreUniversity of Ottawa
Fundersnot available
KeywordsSpecialtyMedicineObservational studyReferralFamily medicineWorkloadPediatricsMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Champlain BASE™ (Building Access to Specialists through eConsultation) is a web-based asynchronous electronic communication service that allows primary-care- practitioners (PCPs) to submit "elective" clinical questions to a specialist. For adults, PCPs have reported improved access and timeliness to specialist advice, averted face-to-face specialist referrals in up to 40% of cases and high provider satisfaction. OBJECTIVE: To determine whether the expansion of eConsult to a pediatric setting would result in similar measures of improved healthcare system process and high provider acceptance reported in adults. DESIGN: Prospective observational cohort study. SETTING: Single Canadian tertiary-care academic pediatric hospital (June 2014-16) servicing 1.2 million people. PARTICIPANTS: 1. PCPs already using eConsult. 2.Volunteer pediatric specialists provided services in addition to their regular workload. 3.Pediatric patients (< 18 years-old) referred for none-acute care conditions. MAIN OUTCOMES AND MEASURES: Specialty service utilization and access, impact on PCP course-of-action and referral-patterns and survey-based provider satisfaction data were collected. RESULTS: 1064 eConsult requests from 367 PCPs were answered by 23 pediatric specialists representing 14 specialty-services. The top three specialties represented were: General Pediatrics 393 cases (36.9%), Orthopedics 162 (15.2%) and Psychiatry 123 (11.6%). Median specialist response time was 0.9 days (range <1 hour-27 days), most consults (63.2%) required <10minutes to complete and 21/21(100%) specialist survey-respondents reported minimal workload burden. For 515/1064(48.4%) referrals, PCPs received advice for a new or additional course of action; 391/1064(36.7%) referrals resulted in an averted face-to-face specialist visit. In 9 specialties with complete data, the median wait-time was significantly less (p<0.001) for an eConsult (1 day, 95%CI:0.9-1.2) compared with a face-to-face referral (132 days; 95%CI:127-136). The majority (>93.3%) of PCPs rated eConsult as very good/excellent value for both patients and themselves. All specialist survey-respondents indicated eConsult should be a continued service. CONCLUSIONS AND RELEVANCE: Similar to adults, eConsult improves PCP access and timeliness to elective pediatric specialist advice and influences their care decisions, while reporting high end-user satisfaction. Further study is warranted to assess impact on resource utilization and clinical outcomes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.136
Threshold uncertainty score0.274

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.074
GPT teacher head0.276
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations44
Published2018
Admission routes2
Has abstractyes

Explore more

Same venuePLoS ONESame topicHealthcare Systems and TechnologyFrench-language works237,207