Improving Patient Access to Hospital Pharmacists Using eConsults: Retrospective Descriptive Study
Notice bibliographique
Résumé
BACKGROUND: eConsults are increasingly used worldwide to reduce specialist referrals and increase access to medical care. An additional benefit of using an eConsult tool is a reduction of health care costs while improving the quality of health care and patient participation. Currently, shared decision making is increasingly implemented and preferred by patients. eConsults are also a promising tool to improve access to the hospital pharmacist. Patients often have questions about their medication. When medication is started during a hospital admission or outpatient visit, community pharmacists are not always sufficiently informed to answer patient questions. Direct contact with hospital pharmacists may be more appropriate and efficient. This contact is facilitated through the eConsult feature in the hospital's patient portal. OBJECTIVE: This study aims to evaluate the prevalence and contents of the eConsults sent by patients to hospital pharmacists. METHODS: A first retrospective descriptive study was conducted at the Leiden University Medical Center in the Netherlands. Patients who sent at least one eConsult to a hospital pharmacist between March 2017 and December 2021 were included. Patient characteristics and the number of medications taken were extracted from electronic health records. The content of eConsults was analyzed and grouped into different subjects. Time of sending of the eConsults was analyzed. A comparison was made between the number of eConsults sent to the hospital pharmacy and the number sent to the medical center. Finally, the appropriateness for evaluation by the hospital pharmacist was assessed in all eConsults. RESULTS: During the study period, 983 eConsults (from 808 patients) were sent to the hospital pharmacist. The average patient age was 56 (SD 15.9) years, and 51.4% (415/808) were male; 47.8% (386/808) of the patients used 0 to 4 medications, 33.0% (267/808) used 5 to 9 medications, and 19.2% (155/808) used ≥10 medications. Of the eConsults, 10.9% (107/983) were excluded due to not being medication-related or not intended for the hospital pharmacist. Patients being treated in 31 medical specialties sent eConsults to the hospital pharmacist. The most common medical specialty was cardiology with 22.5% (197/876) of the eConsults. Most eConsults were sent during office hours (614/876, 70.2%). eConsult subjects were medication verification (372/876, 42.5%), logistics (243/876, 27.7%), therapeutic effect and adverse events (100/876, 11.4%), use of medication (87/876, 9.9%), and other subjects (74/876, 8.4%). CONCLUSIONS: Introducing eConsults allows patients to ask medication-related questions directly to hospital pharmacists. Our study shows that patients send medication reconciliation-related eConsults most often. Use of the eConsult tool leads to fast, direct, and documented communication between patient and hospital pharmacist. This can reduce medication-related errors, improve patient empowerment, and increase access to the hospital pharmacist.
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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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».