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Enregistrement W4241319293 · doi:10.1097/01258363-200812000-00008

The epistemology of patient safety research

2008· article· en· W4241319293 sur OpenAlex
W. B. Runciman, G. Ross Baker, Philippe Michel, Itziar Larizgoitia Jauregui, Richard Lilford, Anne Andermann, Rhona Flin, William B. Weeks

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevueInternational Journal of Evidence-Based Healthcare · 2008
Typearticle
Langueen
DomaineHealth Professions
ThématiquePatient Safety and Medication Errors
Établissements canadiensMcGill UniversityUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésPatient safetyAuditRisk analysis (engineering)Context (archaeology)Scope (computer science)Risk managementProcess (computing)Health careQuality (philosophy)Observational studyBusinessKnowledge managementProcess managementComputer scienceMedicinePolitical science

Résumé

récupéré en direct d'OpenAlex

Patient safety has only recently been subjected to wide-spread systematic study. Healthcare differs from other high risk industries in being more diverse and multi-contextual, and less certain and regulated. Also many patient safety problems are low-frequency events associated with many, varied contributing factors. The subject of this paper is the epistemology of patient safety (the science of the method of finding out about patient safety). Patient safety research is considered here on the background of a risk management framework which requires researchers to: Understand the context – as a subset of healthcare quality, services and systems research, with technical and human behavioural (cultural) components and a range of external and internal organisational influences, a wide range of research disciplines is necessary Identify the risks – identify the things that go wrong and the frequency and nature of different types of incidents from sources such as medical record review, observational studies, audit, incident and medico-legal reports Analyse the risks – deconstruct the things that go wrong, identifying contributing factors and trying to detect trends and patterns in contributing factors, detection, mitigation factors, ameliorating factors and actions taken to reduce risk Evaluate the risks – decide on priorities, identifying preventive and corrective strategies and judging the risk- and cost-benefit of potential corrective strategies such as standardisation or simplification of a process or device Manage the risk – evaluate and scope preventive and/or corrective strategies and then implement these, or place the problem on a risk register pending solution, or accept that what is needed is unaffordable Communicate and consult – use interactive sessions, audit, on-going feedback, reminders and patient mediated prompts Monitor and review the state of the problem – get baseline trends and patterns so that changes can be tracked and properly attributed to an intervention A hierarchy of levels of evidence has been proposed for clinical research and we argue that insufficient weighting has been given to lower ranked levels of research and to qualitative research, although critical interpretive synthesis is now gaining acceptance in mainstream thinking (e.g. by the Cochrane Collaboration). Fundamental challenges remain including how to grasp the elusive concept of patient safety, how to quantify, characterise and cost the problems, how to judge the extent to which harm can be attributed to errors, violations or system failures, how to identify contributing factors and the extent to which they can be implicated, how to judge whether incidents or their precursors are preventable, how to generate strong evidence to make healthcare safer and how to translate research into practice. Future directions include addressing the mundane as well as rare, dramatic events, and developing further research in non-hospital settings and in developing countries. In summary, a mixture of qualitative and quantitative methods, using information from all available data sources and combining retrospective, real time and prospective study designs, is necessary to address some of the more difficult patient safety problems.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,007
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,623
Score d'incertitude au seuil0,839

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,560
Tête enseignante GPT0,571
Écart entre enseignants0,012 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle