An Integrated Virtual Pharmacy Service Targeting Equitable and Safe Medication Access during an Acute COVID-19 Pandemic Response
Notice bibliographique
Résumé
Background: Paxlovid (nirmatrelvir/ritonavir) is a novel oral anti-viral therapy for COVID-19 infection. Although Paxlovid is an efficacious ambulatory treatment, it's prescribing and use required safety processes and assurance to avoid drug-drug interactions and a narrow therapeutic treatment window. Eligible patients for Paxlovid therapy tended to have multiple comorbidities and medications. Equally important, marginalized populations were impacted by COVID-19 disproportionately, putting them at increased risk for adverse drug events. The UHN Connected Care team built on a pre-existing integrated COVID-19 clinic model to target equitable and safe medication access for Paxlovid. The COVID-19 clinic virtual pharmacy expansion followed a co-design with essential care providers, patients, primary care, and pharmacists. The integrated virtual pharmacy pathway aimed to prescribe, dispense and follow up on needed Paxlovid treatment using a streamlined referral form, tiered medication review, and standardized follow-up plan. Objective: Herein, we report on the feasibility and impact of using an integrated care model for virtual pharmacy services to respond to acute pandemic needs for COVID-19 treatment. Methods: UHN Connected Care COVID-19 clinic (Toronto, Canada) is comprised of interdisciplinary (physicians, nurse practitioners, pharmacists) virtual care services targeting patients with acute COVID-19 infections eligible for Paxlovid treatment. We conducted a retrospective review of Paxlovid-referred prescriptions to analyze the types of referrals and outcomes to assess the feasibility and effectiveness of this model. Specifically, medication safety-related outcomes included the number, type, and severity of drug-drug interactions. In addition, feasibility was measured in access to treatment (time to treatment and number of patient interventions applied). Results: Between February 1 to June 30, 2022, prescriptions for Paxlovid to the COVID care clinic were analyzed. A total of 211 Paxlovid prescriptions were referred with an average treatment time of 24 hours from receipt of the referral, meeting the needed therapeutic window of 5 days from symptom onset. Patients were referred from complex specialty clinics (oncology, multi-organ transplant), primary care, and long-term care homes. On average, patients were 64 years old, had 2 to 3 pre-existing comorbidities (diabetes, cancer, transplant, kidney, and cardiac disease), and had 7 to 8 prescription medications per day. A total of 148 drug-drug interactions were identified from the referred prescriptions. 89% of the drug-drug interactions identified were classified as “moderate to severe”, where the potential for long-term adverse events, hospitalization or emergency room visits would have transpired if an appropriate therapeutic intervention was not applied. The UHN Connected Care team's interventions included: temporarily holding chronic medications, changing treatment doses, counseling patients to manage side effects, and recommending safer therapeutic alternatives. Conclusion: In summary, using an integrated care model targeting medication safety and equitable access is effective and addresses acute pandemic response needs. This collaborative model was feasible for providing timely access to COVID-19 treatment while maintaining high-quality and safe care.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».