Advancing Newborn Screening Long-Term Follow-Up: Integration of Epic-Based Registries, Dashboards, and Efficient...

Area of Science:

• Public Health
• Health Informatics
• Pediatric Care

Background:

• Newborn Screening (NBS) identifies infants with serious disorders.
• Effective long-term follow-up (LTFU) care is crucial for improving health outcomes in NBS patients.
• Challenges exist in adopting population health tools for tracking NBS patient care within electronic health records (EHRs).

Purpose of the Study:

• To implement and evaluate an LTFU registry model within the Epic EHR system for NBS patients.
• To improve the adoption of population health tools by specialty care teams.
• To enhance care delivery and track outcomes for infants diagnosed through NBS.

Main Methods:

• Utilized the Epic EHR to establish and optimize LTFU registries.
• Implemented an LTFU model for an early childhood cohort at Connecticut Children's.
• Captured external data via Health Information Exchanges and incorporated evidence-based guidelines.
• Conducted qualitative and quantitative evaluations of the intervention.

Main Results:

• The early childhood cohort showed significant and sustainable improvements in percentage of visits up-to-date (%UTD).
• Positive trends were observed in %UTD for visits and condition-specific performance metrics.
• Qualitative evaluation confirmed the achievability of practice behavior changes with dedicated support.

Conclusions:

• The Connecticut NBS Network's model demonstrates successful adoption of EHR-based population health tools.
• This approach effectively tracks care delivery and addresses gaps for NBS patients.
• The model shows promise for improving long-term health outcomes for individuals identified through newborn screening.