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Record W2792368713 · doi:10.12927/hcq.2018.25420

A Technology-Enabled Solution to Manage Referrals to Hospice and Palliative Care Beds: The Ottawa SMART System as a Case Study

2018· article· en· W2792368713 on OpenAlexaffvenueabout
José Pereira, Kathy Greene, Lisa Sullivan, Nicole Rutkowski, Peter G. Lawlor, Pamela Grassau

Bibliographic record

VenueHealthcare Quarterly · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsCarleton UniversityCanadian Hospice Palliative Care AssociationBruyèreCollege of Family Physicians of Canada
Fundersnot available
KeywordsPalliative careBest practiceMedicineNursingBusinessManagement

Abstract

fetched live from OpenAlex

Ottawa has a 31-bed palliative care unit (PCU) and two residential adult hospices (total 19 beds). In 2013, we initiated a project to improve the referral and triage processes to these beds. Previously, there were two separate paper-based systems with duplication, inefficiencies, delays and inappropriate patient placements. The multipronged approach included clarifying the respective roles of the PCU and hospices, creating a single referral and triage office and developing an e-platform. We leveraged technology that was available in the public-funded system. This paper describes the development processes, lessons learned, and the final system, referred to as System to Manage Access, Referrals and Triage (SMART).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.028
GPT teacher head0.309
Teacher spread0.281 · 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 designQualitative
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

Citations1
Published2018
Admission routes3
Has abstractyes

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