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Record W4390957047 · doi:10.5334/ijic.icic23601

An Integrated Virtual Pharmacy Service Targeting Equitable and Safe Medication Access during an Acute COVID-19 Pandemic Response

2023· article· en· W4390957047 on OpenAlexaffabout
Tsoleen Ayanian, Stephanie W. Ong, Melissa Chang, Tori Edgar, Christopher T. Chan

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicinePharmacyPandemicMedication therapy managementPharmaceutical carePolypharmacyAmbulatory careReferralMedical prescriptionClinical pharmacyTelemedicinePharmacistMedical emergencyFamily medicineCoronavirus disease 2019 (COVID-19)Intensive care medicineHealth careNursingInternal medicine

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.055
GPT teacher head0.428
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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