80 - Improving Problem List Accuracy in Pediatric Primary Care: A Standardized Workflow for 6 to 18 Month Well Visits

Publication Number: 4078.80

Lara Mattei, The Mount Sinai Kravis Children's Hospital, New York, NY, United States; Eric D. Acosta, Icahn School of Medicine at Mount Sinai, Bronx, NY, United States; Saranya Ramadurai, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Jessica Goldberg, Icahn School of Medicine at Mount Sinai Hospital - - New York, NY, New York, NY, United States; Sharon Arguello-angarita, Icahn School of Medicine at Mount Sinai, New york, NY, United States; Cynthia Katz, Icahn School of Medicine at Mount Sinai, New York, NY, United States

Background: The electronic medical record problem list (PL) often includes outdated or unnecessary information that can obscure relevant clinical data, leading to decreased efficiency and lower quality care. In our General Pediatrics practice, PL clean-up has not been prioritized, and therefore providers do not utilize it as a tool for efficient visits and documentation. To address this gap, we implemented a standardized workflow to ensure accurate, concise, and clinically relevant PLs.

Objective: SMARTIE

Aim: To increase the percentage of 6–18-month-old patients presenting for well child visits with an updated PL from 45 to 90% by May 2026 without disparities between primary English and non-English preferred language.

Design/Methods: A team of stakeholders, including physicians, staff, and patients, created a process map to capture current workflow of the utilization and modification of the PL. A fishbone diagram was used to identify reasons for failure to update the PL (Fig. 1), and a Key Driver Diagram aided in brainstorming interventions. For ease of data collection, the scope of our initial phase included only charts of 6–18-month-old patients at well visits. Measures were defined as: 3 process (% chart PLs without: 1- nursery elements, 2- duplicate diagnoses, and 3- acute problems), outcome (% charts with a fully updated PL, as defined by each process measure), and balancing (% charts with medication reconciliation done). Interventions included announcements at residency meetings, signage with a new standardized workflow, emails, and pre-clinic huddle reminders.

Results: Prior to our intervention, 8 weeks of baseline data established a median of 45% compliance for our outcome measure. Weekly data collection ran from August to October 2025 (Fig. 2). Iterative cycles of interventions showed sustained improvement with a 90% median, reaching our goal. Nursery elements present was our lowest performing process measure impacting the outcome. No disparity was found between preferred language. Medication reconciliation rates (balancing measure) has not changed throughout the project.

Conclusion(s): A standardized process to clean-up PLs streamlined provider workflow leading to improvement in our outcome to reach our goal. Introducing a workflow with new interns likely contributed to our success. Although we limited the target population in our initial phase, we anticipate that this workflow will seamlessly broaden the impact of our QI project to all patients and all PL elements. Our long-term goal is to utilize accurate PLs to aid in stratification of medical and social complexity to allow for higher quality care.

Figure 1. Fishbone Diagram
Fishbone Diagram.pdf

Figure 2. Run Chart
Problem List Run Chart.pdf