Experiences and Outcomes of Using e-Prescribing for Opioids: Rapid Scoping Review
Notice bibliographique
Résumé
BACKGROUND: e-Prescribing is designed to assist in facilitating safe and appropriate prescriptions for patients. Currently, it is unknown to what extent e-prescribing for opioids influences experiences and outcomes. To address this gap, a rapid scoping review was conducted. OBJECTIVE: This rapid scoping review aims to (1) explore how e-prescribing has been used clinically; (2) examine the effects of e-prescribing on clinical outcomes, the patient or clinician experience, service delivery, and policy; and (3) identify current gaps in the present literature to inform future studies and recommendations. METHODS: A rapid scoping review was conducted following the guidance of the JBI 2020 scoping review methodology and the World Health Organization guide to rapid reviews. A comprehensive literature search was completed by an expert librarian from inception until November 16, 2022. Three databases were electronically searched: MEDLINE (Ovid), Embase (Ovid), and Scopus (Elsevier). The search criteria were as follows: (1) e-prescribing programs targeted to the use or misuse of opioids, including those that were complemented or accompanied by clinically focused initiatives, and (2) a primary research study of experimental, quasi-experimental, observational, qualitative, or mixed methods design. An additional criterion of an ambulatory component of e-prescribing (eg, e-prescribing occurred upon discharge from acute care) was added at the full-text stage. No language limitations or filters were applied. All articles were double screened by trained reviewers. Gray literature was manually searched by a single reviewer. Data were synthesized using a descriptive approach. RESULTS: Upon completing screening, 34 articles met the inclusion criteria: 32 (94%) peer-reviewed studies and 2 (6%) gray literature documents (1 thesis study and 1 report). All 33 studies had a quantitative component, with most highlighting e-prescribing from acute care settings to community settings (n=12, 36%). Only 1 (3%) of the 34 articles provided evidence on e-prescribing in a primary care setting. Minimal prescriber, pharmacist, and clinical population characteristics were reported. The main outcomes identified were related to opioid prescribing rates, alerts (eg, adverse drug events and drug-drug interactions), the quantity and duration of opioid prescriptions, the adoption of e-prescribing technology, attitudes toward e-prescribing, and potential challenges with the implementation of e-prescribing into clinical practice. e-Prescribing, including key features such as alerts and dose order sets, may reduce prescribing errors. CONCLUSIONS: This rapid scoping review highlights initial promising results with e-prescribing and opioid therapy management. It is important that future work explores the experience of prescribers, pharmacists, and patients using e-prescribing for opioid therapy management with an emphasis on prescribers in the community and primary care. Developing a common set of quality indicators for e-prescribing of opioids will help build a stronger evidence base. Understanding implementation considerations will be of importance as the technology is integrated into clinical practice and health systems.
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 | 0,032 | 0,120 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,024 | 0,024 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».