An Integrated Virtual Pharmacy Service Targeting Equitable and Safe Medication Access during an Acute COVID-19 Pandemic Response
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".