Interventions to optimise the outputs of national clinical audits to improve the quality of health care: a multi-method study including RCT
Notice bibliographique
Résumé
Background National clinical audit programmes aim to improve patient care by reviewing performance against explicit standards and directing action towards areas not meeting those standards. Their impact can be improved by (1) optimising feedback content and format, (2) strengthening audit cycles and (3) embedding randomised trials evaluating different ways of delivering feedback. Objectives The objectives were to (1) develop and evaluate the effects of modifications to feedback on recipient responses, (2) identify ways of strengthening feedback cycles for two national audits and (3) explore opportunities, costs and benefits of national audit participation in a programme of trials. Design An online fractional factorial screening experiment (objective 1) and qualitative interviews (objectives 2 and 3). Setting and participants Participants were clinicians and managers involved in five national clinical audits – the National Comparative Audit of Blood Transfusions, the Paediatric Intensive Care Audit Network, the Myocardial Ischaemia National Audit Project, the Trauma Audit & Research Network and the National Diabetes Audit – (objective 1); and clinicians, members of the public and researchers (objectives 2 and 3). Interventions We selected and developed six online feedback modifications through three rounds of user testing. We randomised participants to one of 32 combinations of the following recommended specific actions: comparators reinforcing desired behaviour change; multimodal feedback; minimised extraneous cognitive load for feedback recipients; short, actionable messages followed by optional detail; and incorporating ‘the patient voice’ (objective 1). Main outcome measures The outcomes were intended actions, including enactment of audit standards (primary outcome), comprehension, user experience and engagement (objective 1). Results For objective 1, the primary analysis included 638 randomised participants, of whom 566 completed the outcome questionnaire. No modification independently increased intended enactment of audit standards. Minimised cognitive load improved comprehension (+0.1; p = 0.014) and plans to bring audit findings to colleagues’ attention (+0.13, on a –3 to +3 scale; p = 0.016). We observed important cumulative synergistic and antagonistic interactions between modifications, participant role and national audit. The analysis in objective 2 included 19 interviews assessing the Trauma Audit Research Network and the National Diabetes Audit. The identified ways of strengthening audit cycles included making performance data easier to understand and guiding action planning. The analysis in objective 3 identified four conditions for effective collaboration from 31 interviews: compromise – recognising capacity and constraints; logistics – enabling data sharing, audit quality and funding; leadership – engaging local stakeholders; and relationships – agreeing shared priorities and needs. The perceived benefits of collaboration outweighed the risks. Limitations The online experiment assessed intended enactment as a predictor of actual clinical behaviour. Interviews and surveys were subject to social desirability bias. Conclusions National audit impacts may be enhanced by strengthening all aspects of feedback cycles, particularly effective feedback, and considering how different ways of reinforcing feedback act together. Future work Embedded randomised trials evaluating different ways of delivering feedback within national clinical audits are acceptable and may offer efficient, evidence-based and cumulative improvements in outcomes. Trial registration This trial is registered as ISRCTN41584028. Funding details This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research; Vol. 10, No. 15. See the NIHR Journals Library website for further project information.
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 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 | 0,077 | 0,093 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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.
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 ».