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Record W2980015356 · doi:10.1182/blood.v126.23.42.42

Content, Utilization and Impact of a Hematology e-Consultation Service

2015· article· en· W2980015356 on OpenAlexaffabout
Karima Khamisa, Adam Fogel, Clare Liddy, Amir Afkham

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsOttawa HospitalChamplain Regional CollegeBruyèreUniversity of Ottawa
Fundersnot available
KeywordsMedicineSpecialtyReferralFamily medicineService (business)HematologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Limited access to specialist care remains a major barrier to health care in Canada. The Champlain BASE (Building Access to Specialists through eConsultation) eConsult service is a secure web-based tool that gives primary care providers (PCPs) expedited access to specialist advice for their patients in Ontario, Canada. Hematology is the third most commonly consulted specialty in the eConsult service, accounting for 8% of all cases. The purpose of this study is to perform an in-depth analysis to describe the types of questions, content, utilization, and impact of hematology eConsults submitted by PCPs. Additionally, the results will inform future continuing medical education activities for PCPs. Methods All Hematology eConsults completed between April 1, 2011 and January 31, 2015 were included. We analyzed and categorized each consultation by: (1) clinical content (up to two per case) using a modification of the International Classification for Primary Care (ICPC-2); and (2) type of questions asked by the PCP based on a validated taxonomy. Other data including PCP designation, time for specialist to complete the eConsult, specialist response time, perceived value of the eConsult by the PCP, and the need for a face-to-face referral following the eConsult was collected in real time via the eConsult service and a survey completed by the initiating PCP at the closure of each eConsult. Results There were a total of 436 Hematology eConsults submitted, 87% from physicians and 13% from Nurse Practitioners. Most cases were answered within 3 days. The most common types of questions being asked pertained to management of hematologic disorders (25%), interpretation of a laboratory test (22%) and appropriate further investigative tests (18%). Common clinical content categories were anemia (22%), neutropenia (13%), high ferritin (11%), monoclonal gammopathy of undetermined significance or an abnormal protein electrophoresis (10%) and thrombocytopenia (10%). Two clinical content categories were included in 19% of cases. Self-reported response time by hematologists was under 10 minutes in >75% of cases. Over 66% of cases did not require a face-to-face visit with the specialist following an eConsult; in fact, in 46% of cases an unnecessary referral was avoided. In 4% of cases, a face-to-face consultation was initiated where one was not originally contemplated. PCPs gained new or additional advice for a course of action in 58% of eConsults, and were able to confirm their original course of action in 39% of cases. PCPs rated the value of the eConsult service as ≥4/5 for both themselves and patients in >90% of cases. Impact The hematology eConsult service has significantly increased access to specialist care in a timely manner compared to traditional face-to-face consultations. The service allowed a significant proportion of patients to avoid traditional consultations leading to the potential of cost savings and increased patient safety. Identifying the most common questions and content being asked via the eConsult service will allow for more informed continuing medical education programs for PCPs so that patients can be better served in the primary care setting. Disclosures Khamisa: Amgen: Speakers Bureau.

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)0.000
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 score0.091
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.114
GPT teacher head0.306
Teacher spread0.191 · 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".

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Citations5
Published2015
Admission routes2
Has abstractyes

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