Public health reports (Washington, D.C. : 1974) · 2026
Quality Assessment of Public Health Datasets and Their Alignment With the United States Core Data for Interoperability (USCDI) Data Elements.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Chehab Chirine, Jaafa Noah, Arhin-Wiredu Kingsley, Usifoh Nnamdi, Michaels Maria, Sorungbe Temilade
Аннотация
OBJECTIVES: Numerous nonclinical health factors such as housing and access to services shape public health outcomes and are essential for understanding and addressing population needs. National initiatives, including the United States Core Data for Interoperability (USCDI), aim to standardize data collection and improve data sharing across health care and public health systems. Dataset quality is critical for reliable decision-making and patient care. However, challenges in limited validation, fragmentation, and integration issues remain. This study assessed the quality of national public health datasets and evaluated their alignment with USCDI.
METHODS: An interdisciplinary public health informatics team analyzed 5 datasets across social, economic, education, physical, and health services domains during 2024-2025. The research team examined data quality using 6 metrics: completeness, uniqueness, accuracy, timeliness, consistency, and conformity.
RESULTS: The analysis revealed high levels of completeness (100%) and uniqueness (100%) for a subset of USCDI-mapped elements across most datasets; however, other data quality metrics (accuracy, timeliness, and conformity) could not be uniformly assessed because of dataset characteristics and limited gold-standard reference values. We developed a publicly accessible toolkit to support informaticists and data scientists in creating dataset quality metrics and advancing alignment.
CONCLUSION: This study underscores the role of data standards in strengthening public health data quality through improved alignment. The toolkit may serve as a practical resource for aligning datasets with USCDI and enhancing reliability for public health analysis. Future research linking public health and clinical data could advance interoperability and promote health equity.
METHODS: An interdisciplinary public health informatics team analyzed 5 datasets across social, economic, education, physical, and health services domains during 2024-2025. The research team examined data quality using 6 metrics: completeness, uniqueness, accuracy, timeliness, consistency, and conformity.
RESULTS: The analysis revealed high levels of completeness (100%) and uniqueness (100%) for a subset of USCDI-mapped elements across most datasets; however, other data quality metrics (accuracy, timeliness, and conformity) could not be uniformly assessed because of dataset characteristics and limited gold-standard reference values. We developed a publicly accessible toolkit to support informaticists and data scientists in creating dataset quality metrics and advancing alignment.
CONCLUSION: This study underscores the role of data standards in strengthening public health data quality through improved alignment. The toolkit may serve as a practical resource for aligning datasets with USCDI and enhancing reliability for public health analysis. Future research linking public health and clinical data could advance interoperability and promote health equity.