Overcoming Decisional Gaps in High-Risk Prescribing by Junior Physicians Using Simulation-Based Training: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Gaps between rational thought and actual decisions are increasingly recognized as a reason why people make suboptimal choices in states of heightened emotion, such as stress. These observations may help explain why high-risk medications continue to be prescribed to acutely ill hospitalized older adults despite widely accepted recommendations against these practices. Role playing and other efforts, such as simulation training, have demonstrated benefits to help people avoid decisional gaps but have not been tested to reduce overprescribing of high-risk medications. OBJECTIVE: This study aims to evaluate the impact of a simulation-based training program designed to address decisional gaps on prescribing of high-risk medications compared with control. METHODS: In this 2-arm pragmatic trial, we are randomizing at least 36 first-year medical resident physicians (ie, interns) who provide care on inpatient general medicine services at a large academic medical center to either intervention (simulation-based training) or control (online educational training). The intervention comprises a 40-minute immersive individual simulation training consisting of a reality-based patient care scenario in a simulated environment at the beginning of their inpatient service rotation. The simulation focuses on 3 types of high-risk medications, including benzodiazepines, antipsychotics, and sedative hypnotics (Z-drugs), in older adults, and is specifically designed to help the physicians identify their reactions and prescribing decisions in stressful situations that are common in the inpatient setting. The simulation scenario is followed by a semistructured debriefing with an expert facilitator. The trial's primary outcome is the number of medication doses for any of the high-risk medications prescribed by the interns to patients aged 65 years or older who were not taking one of the medications upon admission. Secondary outcomes include prescribing by all providers on the care team, being discharged on 1 of the medications, and prescribing of related medications (eg, melatonin, trazodone), or the medications of interest for the control intervention. These outcomes will be measured using electronic health record data. RESULTS: Recruitment of interns began on March 29, 2021. Recruitment for the trial ended in Q42021, with follow-up completed by Q12022. CONCLUSIONS: This trial will evaluate the impact of a simulation-based training program designed using behavioral science principles on prescribing of high-risk medications by junior physicians. If the intervention is shown to be effective, this approach could potentially be reproducible by others and for a broader set of behaviors. TRIAL REGISTRATION: ClinicalTrials.gov NCT04668248; https://clinicaltrials.gov/ct2/show/NCT04668248. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/31464.
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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 | 0,044 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,008 | 0,003 |
| Méta-épidémiologie (sens large) | 0,015 | 0,008 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,008 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,072 | 0,009 |
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 ».