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Record W2164821486 · doi:10.4212/cjhp.v59i5.268

Medication Error Events in Ontario Acute Care Hospitals

2006· article· en· W2164821486 on OpenAlexaffvenueabout
Joan A. Marshman, K U David, Robert Lam, Sylvia Hyland

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2006
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedication errorMedicineHarmEmergency medicineMedical emergencyDrug classPatient safetyPediatricsFamily medicineHealth careDrugPsychologyPsychiatry

Abstract

fetched live from OpenAlex

Background and Objective: In 2002, the Institute for Safe Medication Practices Canada (ISMP Canada) collaborated with several hospitals to determine the feasibility of using an electronic system to document and report medication error events and assess medication-use processes. This article provides an overview of the events reported and makes limited comparisons with similar data from US studies. Methods: A standard electronic submission system for documenting medication error events was made available to 14 acute care hospitals in Ontario. The hospitals collected data on medication error events identified by usual criteria and procedures over a 12-month period and submitted the data to ISMP Canada electronically. Analysis of the data focused on the frequency of errors by severity of consequence to the patient, type of outcome, therapeutic class of drugs involved, stage of the medication-use process at which the error occurred, types of error, and hospital-identified cause(s). Parallel analyses were undertaken for the subsets of reported errors classified as adverse drug events (ADEs) and potential ADEs. Results: The 4243 errors examined represent 0.86 errors per bed and 0.25 errors per 1000 doses of medication dispensed. Only 120 (2.8%) of the errors resulted in or possibly contributed to patient harm and were classified as ADEs. No error resulted in death. The 685 errors (16.1%) that reached patients and for which monitoring or intervention were required, but that were not implicated in patient harm, were classified as potential ADEs. The most commonly involved drug classes were central nervous system agents (including analgesics, sedatives, and antipsychotic drugs) (25.6%), blood formation and coagulation agents (12.7%), anti-infective drugs (12.3%), cardiovascular drugs ((12.0%), and hormones and synthetic substitutes (9.9%). The most frequently involved individual drugs were insulin, warfarin, heparin, morphine, furosemide, potassium chloride, epoetin, electrolyte solutions, and cefazolin. Errors occurred most frequently in the medication administration process (56.6%) and the order entry and transcription stages (32.6%) of the drug-use process. Contributing factors most frequently identified included miscommunication of a drug order; environmental, staffing or workload problems; lack of staff education; and lack of quality control or independent check systems. Conclusions: The participating hospitals were willing to submit medication error reports electronically, and compilation of the resulting data provided a snapshot of medication error events detected and documented using usual practices. A very small proportion of the events resulted in harm to patients, but a

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)0.003
metaresearch head score (Gemma)0.031
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.801

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.007
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.374
Teacher spread0.338 · 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".

Quick stats

Citations6
Published2006
Admission routes3
Has abstractyes

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