Mixed methods analysis of an automated email audit and feedback intervention for fostering (emergency) physician reflection
Notice bibliographique
Résumé
Background physician refelection requires personalized, timely and growth-oriented feedback. Iterative learning from multiple low-pressure events can be personalized to target areas of weakness and show sequential growth. Since emergency physicians typically work individually to deliver episodic care, opportunities for them to obtain iterative feedback on their clinical performace is often limited. Our study sought to evaluate whether physician reflection is facilitated through the 72hr re-admission alert received by emergency physicians in the Calgary zone. Implementation The 72-hr readmission alert is already part of feedback received in the Calgary Zone. Our study was specifically looking at understanding the utility of these alerts to emergency physicians through qualitative interviews. Our team of two interviewers (DA and CP) collected and banked the data through anonymized one-on-one interviews. Themes from these interviews will be used to guide future adjustments made to the alert and dictate it’s future role in emergency physician feedback. Current changes based on preliminary data have included the ability to customize re-admission alert time-frames based on personal preference. We are currently in the process of analyzing the themes that will shape further improvements made to the alert. Evaluation Methods This mixed methods realist evaluation consisted of two sequential phases: an initial quantitative phase examining the general features of 72-hr readmission alerts sent over a 1-year period (4024 alerts from May 2017-2018) and a subsequent qualitative phase involving 17 semi-structured interviews to generate “context-mechanism-outcome” (CMO) statements to guide refinement of our program theory. Results CMO statements revealed emergency physician stakeholders were concerned that the alert impacted personnel decisions, changed patient return expectations and didn’t involve consulting services. Physicians, who didn’t believe alerts were involved in personnel decisions, were more likely to pursue balanced reflection/acquisition after each alert when receiving illness related returns. Conversely, physicians, who believed alerts were involved in performance assessment/hiring decisions, were more likely to defensively change their practice. Commonly cited areas of improvement were the ability to personally adjust time criteria for alerts and involving consulting services in feedback. Advice and Lessons Learned It is essential to partner with local departments who can use formal (newsletters) and informal (word of mouth) avenues to encourage participation in the study. Participant anonymity must be emphasized when recruiting for qualitative interviews in order to receive the full scope of perspectives. Clear and concise scripts highlighting the objective of each question can ensure the quality of responses received and help interviewers probe further into the topic when necessary. When performing quality improvement studies on formal feedback mechanisms, faculty leadership buy-in is essential in order to ensure a safe environment for all participants.
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 | 0,073 | 0,128 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».