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Record W2144362933 · doi:10.4212/cjhp.v58i3.307

Medication Safety Huddles: Teaming Up to Improve Patient Safety

2005· article· en· W2144362933 on OpenAlexaffvenueabout
Kerry Wilbur, Kathy Scarborough

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2005
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsPatient safetyMedicineHarmHealth careAdverse effectMedical emergencyGovernment (linguistics)Incident reportFamily medicineEmergency medicinePsychology

Abstract

fetched live from OpenAlex

INTRODUCTION The focus on patient safety in health care has intensified over the past 5 years. The 1999 Institute of Medicine report To Err is Human,1 which outlined the alarmingly high rate of medical errors in the United States, mirrored recognition of iatrogenic injury in Australia2 and the United Kingdom3 and generated an unprecedented response in health care policy. Many health care organizations launched initiatives to promote patient safety and, in December 2003, the Canadian government funded establishment of the Canadian Patient Safety Institute.4 Recently, much anticipated data for a national estimate of hospital-based adverse events has been published.5 In this review of hospital records for 3745 randomly selected patients from across the country, it was estimated that 7.5% of patients admitted to acute care hospitals experienced one or more adverse events. Although drug-related hospital admissions and adverse drug reactions represent major contributors to negative patient outcomes, US data indicate that adverse events specific to medication errors account for 7000 deaths annually.6-9 Medication use in hospitals is complex and susceptible to error at multiple points including prescribing, transcribing, dispensing, administration, and monitoring. Medication errors, either potential or actual,10 are considered preventable events that may cause or lead to inappropriate medication use or patient harm. Although proposed safeguards that hinge principally on technological advances (computerized physician order entry, point-of-care unit-dose dispensing cabinets, bar-code technology) may minimize risk, they will likely never entirely overcome the human element in medication error.11-13 The original concept of using safety briefings or “medication safety huddles” as a strategy to promote a culture of safety in health care settings has been credited to the Institute for Healthcare Improvement.14 A simple and efficient tool for front-line staff, these small briefings represent an opportunity to share information about actual or potential medication safety problems and concerns on a regular basis. Brainstorming leads to suggestions for interventions that are implemented in a timely fashion. Medication safety huddles can be used to identify and address factors contributing to medication errors, educate nursing staff about medications, and promote a culture of change among participants. Ultimately, the goal of medication safety huddles is to reduce the risk of medication errors and improve the quality of patient care. Activities that foster a “culture of safety” are acknowledged as fundamental in enhancing patient safety in any organization. In a recent survey of nurses, more than one-third of respondents said that they had failed to report one or more medication errors during their career for fear of personal or professional repercussions.15 Clearly a workplace environment that focuses on finding fault can suppress medication error reporting and may lead to dangerous situations. It may also take its toll on productivity and morale as staff are less inclined to be creative, courageous, and even ethical in a workplace where energies are invested in blame.16 First employed in aviation and construction, safety briefings have now been adopted in health care settings and patient care facilities.17,18 We describe our experience in initiating medication safety huddles on the acute adult medicine unit at our hospital.

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)0.006
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0030.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0230.004

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 head0.033
GPT teacher head0.370
Teacher spread0.337 · 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 designNot applicable
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

Citations13
Published2005
Admission routes3
Has abstractyes

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