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Enregistrement W2523586271 · doi:10.3310/hsdr04270

Characterising the nature of primary care patient safety incident reports in the England and Wales National Reporting and Learning System: a mixed-methods agenda-setting study for general practice

2016· article· en· W2523586271 sur OpenAlexaff
Andrew Carson‐Stevens, Peter Hibbert, Huw Williams, H. P. Evans, Alison Cooper, Philippa Rees, Anita Deakin, Emma Shiels, Russell Gibson, Amy Butlin, Ben Carter, Donna Luff, Gareth Parry, Meredith Makeham, Paul McEnhill, Hope Olivia Ward, Ray Samuriwo, Anthony Avery, Antony Chuter, Liam Donaldson, Sharon Mayor, Sukhmeet S. Panesar, Aziz Sheikh, Fiona Wood, Adrian Edwards

Notice bibliographique

RevueHealth Services and Delivery Research · 2016
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMedical Malpractice and Liability Issues
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesCardiff UniversityImperial College LondonUniversity of SouthamptonHealth Services and Delivery Research ProgrammeNational Institute for Health and Care ResearchNational Institutes of HealthAustralian Commission on Safety and Quality in Health CareWorld Health Organization
Mots-clésPatient safetyThematic analysisMedicineIncident reportVignetteHarmNear missQualitative researchNursingHealth careFamily medicineMedical emergencyPsychologyComputer security

Résumé

récupéré en direct d'OpenAlex

Background There is an emerging interest in the inadvertent harm caused to patients by the provision of primary health-care services. To date (up to 2015), there has been limited research interest and few policy directives focused on patient safety in primary care. In 2003, a major investment was made in the National Reporting and Learning System to better understand patient safety incidents occurring in England and Wales. This is now the largest repository of patient safety incidents in the world. Over 40,000 safety incident reports have arisen from general practice. These have never been systematically analysed, and a key challenge to exploiting these data has been the largely unstructured, free-text data. Aims To characterise the nature and range of incidents reported from general practice in England and Wales (2005–13) in order to identify the most frequent and most harmful patient safety incidents, and relevant contributory issues, to inform recommendations for improving the safety of primary care provision in key strategic areas. Methods We undertook a cross-sectional mixed-methods evaluation of general practice patient safety incident reports. We developed our own classification (coding) system using an iterative approach to describe the incident, contributory factors and incident outcomes. Exploratory data analysis methods with subsequent thematic analysis was undertaken to identify the most harmful and most frequent incident types, and the underlying contributory themes. The study team discussed quantitative and qualitative analyses, and vignette examples, to propose recommendations for practice. Main findings We have identified considerable variation in reporting culture across England and Wales between organisations. Two-thirds of all reports did not describe explicit reasons about why an incident occurred. Diagnosis- and assessment-related incidents described the highest proportion of harm to patients; over three-quarters of these reports (79%) described a harmful outcome, and half of the total reports described serious harm or death (n = 366, 50%). Nine hundred and ninety-six reports described serious harm or death of a patient. Four main contributory themes underpinned serious harm- and death-related incidents: (1) communication errors in the referral and discharge of patients; (2) physician decision-making; (3) unfamiliar symptom presentation and inadequate administration delaying cancer diagnoses; and (4) delayed management or mismanagement following failures to recognise signs of clinical (medical, surgical and mental health) deterioration. Conclusions Although there are recognised limitations of safety-reporting system data, this study has generated hypotheses, through an inductive process, that now require development and testing through future research and improvement efforts in clinical practice. Cross-cutting priority recommendations include maximising opportunities to learn from patient safety incidents; building information technology infrastructure to enable details of all health-care encounters to be recorded in one system; developing and testing methods to identify and manage vulnerable patients at risk of deterioration, unscheduled hospital admission or readmission following discharge from hospital; and identifying ways patients, parents and carers can help prevent safety incidents. Further work must now involve a wider characterisation of reports contributed by the rest of the primary care disciplines (pharmacy, midwifery, health visiting, nursing and dentistry), include scoping reviews to identify interventions and improvement initiatives that address priority recommendations, and continue to advance the methods used to generate learning from safety reports. Funding The National Institute for Health Research Health Services and Delivery Research programme.

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.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,090
score de la tête « metaresearch » (Gemma)0,105
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,090
Score d'incertitude au seuil0,474

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0900,105
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0050,004
Études des sciences et des technologies0,0040,003
Communication savante0,0030,004
Science ouverte0,0020,006
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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 Opus0,102
Tête enseignante GPT0,518
Écart entre enseignants0,415 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations79
Publié2016
Routes d'admission1
Résumé présentoui

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