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Record W4400581697 · doi:10.1093/ajhp/zxae184

Using quality improvement frameworks to develop, implement, and evaluate a novel ambulatory oncology pharmacy practice model: A descriptive example

2024· article· en· W4400581697 on OpenAlexaff
Hayley Underhill, Michael LeBlanc, Robyn Jane Macfarlane, Lauren Hutton

Bibliographic record

VenueAmerican Journal of Health-System Pharmacy · 2024
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsHorizon Health NetworkMoncton HospitalQueen Elizabeth II Health Sciences CentreNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsPharmacyAmbulatoryQuality (philosophy)Descriptive statisticsMedicineMedical physicsPharmacy practiceOncologyInternal medicineFamily medicineStatisticsMathematics

Abstract

fetched live from OpenAlex

PURPOSE: To describe the application of the Plan-Do-Study-Act quality improvement framework in the development, implementation, and evaluation of a novel pharmacy practice model in ambulatory oncology. SUMMARY: Four iterations of the Plan-Do-Study-Act framework were completed to develop a patient-facing, pharmacist-led ambulatory oncology clinic program. The clinic provided care to patients with prostate cancer on oral anticancer therapy. Metrics were collected throughout all stages of development to inform target processes for improvement. The pharmacist saw 136 patients between July 2019 and January 2023, resulting in 464 total encounters. The pharmacist provided clinical interventions and counseling to patients newly starting on oral anticancer therapy and those established on therapy using a longitudinal model of care. CONCLUSION: Application of the Plan-Do-Study-Act quality improvement framework to a novel pharmacy practice model supported the development, evaluation, and sustainability of a pharmacist-led ambulatory oncology clinic providing care to patients with prostate cancer on oral anticancer therapy.

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)0.006
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.295
GPT teacher head0.536
Teacher spread0.241 · 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 designOther design
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

Citations2
Published2024
Admission routes1
Has abstractyes

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