Medication Safety Huddles: Teaming Up to Improve Patient Safety
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».