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Record W4402391084 · doi:10.23889/ijpds.v9i5.2585

Timely data on the dynamic drug supply is crucial in addressing the ongoing drug poisoning crisis. The team is exploring ways to share information with emergency responders, healthcare providers, government officials, and pwSUD.

2024· article· en· W4402391084 on OpenAlexaffabout
Tanmay Patil, Robin L. Walker, Congshi Shi, Wanning Song, Judy Seidel, Scott Oddie

Bibliographic record

VenueInternational Journal for Population Data Science · 2024
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsRed Deer PolytechnicAlberta Health Services
Fundersnot available
KeywordsGovernment (linguistics)DrugBusinessHealth carePublic relationsMedical emergencyMedicineKnowledge managementInternet privacyPolitical scienceComputer sciencePharmacologyEconomicsEconomic growth

Abstract

fetched live from OpenAlex

ObjectiveTransitioning from hospitals to primary care brings significant challenges, including increased readmission rates, mortality, and costs due to information gaps. Alberta, Canada, addressed this by integrating 1000+ health systems and implementing Connect Care (CC), a Clinical Information System (CIS), to enhance patient safety and care coordination. In 2020, the Primary Health Care Integration Network (PHCIN) released the Home to Hospital to Home (H2H2H) Transitions Guideline and metrics to improve patient outcomes and system integration. ApproachThis study examines provincial data on H2H2H transitions measures from acute care hospitals using CC between April 1, 2022, and October 31, 2023, to assess transitions and support improvement efforts. CC data assesses transition components, including confirming primary care providers at hospital discharge, utilizing the LACE Readmission Risk Index, and ensuring timely discharge summary (DS) signoffs. It links with administrative data to evaluate post-discharge outcomes like primary care physician follow-up, unplanned readmissions, and Emergency Department (ED) visits. Results47 CC-implemented sites show nearly 80% discharges identified a primary care provider; less than 5% incorporated the LACE index in DS. About 90-93% of DS were signed within 24-72 hours. Approximately 62% of moderate-risk and 55% of high-risk discharges received timely follow-up. Readmission and ED visit rates within 7-30 days varied from 2-8% and 5-11% respectively. ConclusionIn conclusion, adopting CC and H2H2H transition metrics facilitates integrated care measurement, highlighting the need for risk index inclusion and high-risk discharge follow-up for improved patient transitions. Sustained initiatives are vital for optimal outcomes and system integration in Alberta.

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)0.011
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.427

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.010
Science and technology studies0.0030.001
Scholarly communication0.0060.008
Open science0.0020.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0160.005

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 head0.335
GPT teacher head0.469
Teacher spread0.134 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

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