Shared Decision-Making for Drug-Drug Interactions: Formative Evaluation of an Anticoagulant Drug Interaction
Notice bibliographique
Résumé
BACKGROUND: Warnings about drug-drug interactions (DDIs) between warfarin and nonsteroidal anti-inflammatory drugs (NSAIDs) within electronic health records indicate potential harm but fail to account for contextual factors and preferences. We developed a tool called DDInteract to enhance and support shared decision-making (SDM) between patients and physicians when both warfarin and NSAIDs are used concurrently. DDInteract was designed to be integrated into electronic health records using interoperability standards. OBJECTIVE: The purpose of this study was to conduct a formative evaluation of a DDInteract that incorporates patient and product contextual factors to estimate the risk of bleeding. METHODS: A randomized formative evaluation was conducted to compare DDInteract to usual care (UC) using physician-patient dyads. Using case vignettes, physicians and patients on warfarin participated in simulated virtual clinical encounters where they discussed the use of taking ibuprofen and warfarin concurrently and determined an appropriate therapeutic plan based on the patient's individualized risk. Dyads were randomized to either DDInteract or UC. Participants completed a postsession interview and survey of the SDM process. This included the 9-item Shared Decision-Making Questionnaire (SDM-Q-9), tool usability and workload National Aeronautics and Space Administration (NASA) Task Load Index, Unified Theory of Acceptance and Use of Technology (UTAUT), Perceived Behavioral Control (PBC) scale, System Usability Scale (SUS), and Decision Conflict Scale (DCS). They also were interviewed after the session to obtain perceptions on DDInteract and UC resources for DDIs. RESULTS: Twelve dyad encounters were performed using virtual software. Most (n=11, 91.7%) patients were over 50 years of age, and 9 (75%) had been taking warfarin for more than 2 years (75%). Regarding scores on the SDM-Q-9, participants rated DDInteract higher than UC for questions pertaining to helping patients clarify the decision (P=.03), involving patients in the decision (P=.01), displaying treatment options (P<.001), identifying advantages and disadvantages (P=.01), and facilitating patient understanding (P=.01) and discussion of preferences (P=.01). Five of the 8 UTAUT constructs showed differences between the 2 groups, favoring DDInteract (P<.05). Usability ratings from the SUS were significantly higher (P<.05) for physicians using DDInteract compared to those in the UC group but showed no differences from the patient's perspective. No differences in patient responses were observed between groups using the DCS. During the session debrief, physicians indicated little concern for the additional time or workload entailed by DDInteract use. Both clinicians and patients indicated that the tool was beneficial in simulated encounters to understand and mitigate the risk of harm from this DDI. CONCLUSIONS: Overall, DDInteract may improve encounters where there is a risk of bleeding due to a potential drug-drug interaction involving anticoagulants. Participants rated DDInteract as logical and useful for enhancing SDM. They reported that they would be willing to use the tool for an interaction involving warfarin and NSAIDs.
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 | 0,040 | 0,112 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 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) | 0,003 | 0,000 |
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.
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 ».