Best Practices for Data Modernization Across the United States Public Health System: Scoping Review

Area of Science:

• Public Health Informatics
• Health Data Management
• Health Systems Research

Background:

• Public health aims to leverage new technologies and data modernization for better decision-making and population health.
• Challenges include legacy systems, data silos, and privacy concerns hindering technology adoption and data sharing.
• This paper identifies data modernization challenges, initiatives, and progress in public health.

Purpose of the Study:

• To characterize evidence for data modernization-associated gaps and best practices in public health.
• To provide a comprehensive overview of the current landscape of public health data modernization.

Main Methods:

• A scoping review was conducted following Arksey and O'Malley's framework and PRISMA-ScR guidelines.
• Searches were performed in PubMed, Scopus, CINAHL, PsycINFO, and Google Scholar (Jan 2019–Apr 2024).
• Eligible studies focused on US public health data modernization, reporting best practices, challenges, and outcomes; data were thematically analyzed.

Main Results:

• The review analyzed 21 studies on public health data modernization, identifying common components like cloud migration, data consolidation, governance frameworks, and analytics.
• Primary data sources included EHRs, insurance claims, and disease surveillance.
• Key challenges were data quality, interoperability, and limited resources, while benefits included timely data access, improved integration, and enhanced analytics.

Conclusions:

• Progress requires balancing local needs with national coordination and improving data governance.
• Enhanced collaboration across institutions is vital for effective public health efforts.
• Ensuring timely, accurate, and actionable information is crucial for public health systems.