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Record W3082281817

GIMRAC development, model of care, support of admission avoidance and decreased length of stay

2020· article· en· W3082281817 on OpenAlexaboutno aff
Nada Elmazariky

Bibliographic record

VenueJournal of medical research/˜The œjournal of medical research · 2020
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsnot available
Fundersnot available
KeywordsSpecialtyAccountabilityMedicineAmbulatory careTransparency (behavior)Health careHospital medicineNursingFamily medicineMedical emergencyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Abstract Hallway medicine has taken a forefront in Ontario Healthcare and is influencing patient care. The Conservative party is focused on increasing fiscal responsibility.  Joseph Brant Hospital’s General Internal Medicine Rapid Assessment Clinic (GIMRAC) was initiated in 2016 in an effort to decrease hallway medicine and assume that fiscal responsibility by having the right patient in the right place at the right time. Appropriate patients are referred from the Emergency department and Inpatient units and provided with a timely assessment by an internal medicine physician in a clinic setting close to home. The GIMRAC has prevented over 3500 hospital admissions and allowed for over 800 early discharges since its inception. Volumes have increased by greater than 350% to date. Specialty medical care, collaboration between departments, and an intense focus on client centered care, have supported these numbers. The governance of an Ambulatory Care Steering Committee and consistent monitoring of metrics amongst staff, leadership, and finance, has provided the transparency and accountability needed to further grow. A review of community needs allows for proper alignment of care within the clinics. A realignment of resources and co-location of clinics, allows for visibility, cross training of staff, and collaboration of expert care. The aim of this presentation is to inform conference attendees about the type of patient optimal for this model of care, challenges surrounding referrals, follow up, limited resource utilization, and barriers to further growth. Strategies related to the daily dynamics   between frontline providers (ie., physicians, nurses, and hospital administration) will be outlined.   Biography Dr. Nada Elmazariky Dr. Elmazariky is a graduate of McMaster University where she studied Internal Medicine and was nominated for Chief Resident at Juravinski Hospital. Dr. Elmazariky is a Fellow of the Royal College of Physicians and Surgeons of Canada where she became an academic representative for the Internal Medicine Fellows and a Certified Specialist in Internal Medicine. Dr. Elmazariky has been working at Joseph Brant Hospital (JBH) since 2016. She has led the development of the GIMRAC Clinic and been the physician lead for the Preoperative Medicine Clinic. Dr. Elmazariky has been instrumental in designing and teaching internal medicine electives for residents. Presenting author details Full name: Dr. Nada Elmazariky Contact number: 1-647-808-6434 Session name/ number: Category: Oral presentation   Biography Tracy A. Fazzari Tracy Fazzari is the Manager of Oncology, Endoscopy and Ambulatory Care at Joseph Brant Hospital (JBH), board member of the Canadian Association of Ambulatory Care and member of the Canadian College of Health Leaders. She has a Master of Science in Speech Pathology with over 18 years clinical experience and 12 years lecturing within the university which has supported her interest in patient centred care and provided the foundation for leadership. Tracy participated in the redevelopment project at JBH gaining operational skills necessary to develop the Ambulatory care clinics. Tracy has an interest in leadership, mentorship, collaboration and clinic development.

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)0.008
metaresearch head score (Gemma)0.024
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.002
Open science0.0030.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.002

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 head0.544
GPT teacher head0.615
Teacher spread0.071 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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