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Best Practices for Data Modernization across the United States Public Health: A Scoping Review

Zebunnesa Zeba¹*, Stella T. Lartey¹, Polina Durneva³, Shongkour Roy¹, Nichole Saulsberry-Scarboro¹, Michelle Taylor², Ashish Joshi¹

Affiliation

1. University of Memphis School of Public Health, Memphis, TN
2. Shelby County Health Department, Memphis, TN
3. University of Memphis, Fogelman College of Business & Economics, Memphis, TN

*Corresponding author: Zebunnesa Zeba (email: zzeba@memphis.edu)

ABSTRACT

Background: The adoption of new technologies and data modernization approaches in public health aims to enhance the use of health data to inform decision-making and improve population health. However, public health departments struggle with legacy systems, siloed data, and privacy concerns, hampering new technology adoption and data sharing with stakeholders. This paper maps how to address these shortcomings by identifying data modernization challenges, initiatives, and progress.

Objective: To characterize the evidence for data modernization associated gaps and best practices in public health.

Methods: This study conducts a scoping review to characterize recent initiatives on data modernization to optimize health data best practices across the United States (U.S.) public health agencies. We conducted a search of papers across PubMed, Scopus, Google Scholars, and grey literature that were published between 2019 and 2024 and that focused on data modernization within local, state, and the U.S. federal public health departments. Data were extracted on modernization approaches, data sources, best practices, challenges, and impacts.

Results: Our final sample included 22 papers. In this review, we discuss about common data modernization components including migrating data to the cloud, integrating disparate data sources into unified systems, existing governance policies, and adopting analytics platforms. Major data sources were electronic health records, insurance claims, and disease registries. The common challenges were poor data quality, limited system interoperability, and resource constraints. We also reflect the benefits include timely integrated data, new insights enhancing programs when aligned with data policies and standards.

Conclusion: This study identifies initiatives aiming to facilitate data-driven policy and decision-making. Findings show that there remain opportunities to implement best practices, evaluate impact, address technological gaps, and prioritize strategic sustainable investments to further improve data infrastructure and modernization.