Resident Weill Cornell Medicine New York, New York, United States
Background: Vaccination rates are facing a global decline leading to resurgence of once controlled and eradicated diseases. This decline is multifactorial in nature including access to care during the pandemic and a growing problem of vaccine hesitancy, defined as the state of being conflicted or opposed to vaccination. A similar trend has been noted in our clinic with the rates of immunizations for children ages 24-35 months declining from 84% to 74% since 2021. Objective: We aim to increase vaccination rates to 90% in children between ages 24-35 months by December 2024 in an urban publicly insured clinic using the Healthcare Effectiveness Data and Information Set vaccine combination 3(HC3) measure. Design/Methods: This is an observational Quality Improvement (QI) study with planned sequential experimentation at an urban publicly insured pediatric clinic affiliated with a tertiary academic medical center. A resident-led interdisciplinary team including frontline staff, faculty, parent representatives and QI specialist identified three key drivers including missed opportunities to vaccinate, workflow for vaccine immunization and vaccine hesitancy. We used the Model for Improvement to define measures including % of children 2 years of age with HC3 vaccines by 35 months of age (outcome), the % of patients with documented immunization hesitancy, % of medical providers trained in motivational interviewing and the % of missed opportunities for giving immunizations (process), number of well child visits and number of vaccination errors (balancing). HC3 vaccines include 4 doses DTaP, 3 doses IPV, 1 dose MMR, 3 doses HepB, 3 doses Hib, 1 dose VZV, and 4 doses PCV. We will test interventions such as staff education, electronic reminders/alerts using patient portal, use of presumptive language during patient encounter and standing orders to address the missed opportunities to vaccinate key driver. These interventions will be tested sequentially via PDSA cycles. Data analysis will use statistical process control charts with API rules to detect special cause variation.
