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Record W2133669977 · doi:10.4212/cjhp.v65i4.1161

Potential Risks Associated with Medication Administration, as Identified by Simple Tools and Observations

2012· article· en· W2133669977 on OpenAlexaffvenueabout
Adrian E. Ghenadenik, Élise Rochais, Suzanne Atkinson, Jean‐François Bussières

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2012
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsSimple (philosophy)Administration (probate law)Computer sciencePolitical scienceEpistemologyPhilosophyLaw

Abstract

fetched live from OpenAlex

Adverse events are common in health care institutions. In a study published in 2007, the Canadian Institute for Health Information reported that “1 in 13 adult medical and surgical patients admitted to acute care hospitals in Canada in 2000 experienced an adverse event”. Medication errors are among the most frequent adverse events. In an international survey of adults with health problems, administered by The Commonwealth Fund, about 10% of Canadian respondents reported having received the wrong medication or dose from a health care provider in the previous 2 years. These errors may result in morbidity, mortality, increases in monitoring and costs of care, and delays in hospital discharge. A prospective cohort study analyzing the incidence of drug-related adverse events in 2 tertiary care hospitals showed that 34% of preventable adverse medication-related events were at the administration stage, making this category the second most frequent cause (after errors at the ordering stage, which accounted for 56% of preventable adverse medication events). Given this reality, the management of medication-related risks is a priority for hospitals. The medication-use system is complex, with a total of 54 identified phases, for which many activities, tools, equipment, and information systems are needed and for which several interfaces are typically required. Many of these phases, particularly the medication administration process, carry high risks. Typically, nurses are responsible for the critical stages of the medication-use system, with a risk of error at each stage. Importantly, there seems to be a link between the way nurses’ work is organized and the occurrence of errors during the administration of medications. According to a study on nurses’ perceptions of medication errors, “a single hospital patient can receive up to 18 medications per day, and a nurse can administer as many as 50 medications per working shift”. A study of the delivery of nursing care in acute care settings showed that nurses spent 16% of their time preparing or administering medications. In addition, 22% of interruptions occurred during the medication preparation process. A high number of interruptions can lead to medication errors. At the authors’ centre, medication errors were an important cause of incidents and accidents from 2004 to 2010. More specifically, medication errors represented 74% of incidents and accidents in 2004/2005, although this proportion was reduced to 39% in 2010/2011. Errors related to drug administration represented 66.3% of these medication errors. Various preventive strategies are used to manage risk within the medication-use system, including training and use of daily unit-dose medication distribution systems, with medication carts containing individual drawers designated for specific patients (identified by bed numbers). Nonetheless, errors still occur frequently in health care institutions.

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)0.004
metaresearch head score (Gemma)0.044
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.044
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.125
GPT teacher head0.414
Teacher spread0.289 · 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

Citations10
Published2012
Admission routes3
Has abstractyes

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