OnetoMap Meta-Data: Healthcare Analytics Through Research
Introduction: Big Data has revolutionized healthcare research through the three Vs: volume, veracity, and variety. This study introduces the OnetoMap meta-data repository, a centralized inventory developed in collaboration with the University of South Florida's Department of Surgery.
Methods: The repository offers extensive details about each database, including its primary purpose, available variables, and examples of high-impact research utilizing these databases. It aims to create a centralized inventory, enabling researchers to locate and link relevant datasets efficiently. Each dataset is described using standardized criteria to ensure clarity and usability, such as data type, source, collection methods, and potential linkages to other datasets.
Results: Currently, the OnetoMap repository contains descriptions of 49 datasets, with ongoing updates to include new datasets and additional data years. These datasets include a range of data types, including cross-sectional and longitudinal, gathered through claims, registries, electronic health records, and surveys. The repository is hosted on GitHub, enabling version control, collaboration, and open access. Effective search functionalities and descriptive categorization enhance the findability of datasets.
Discussion: The data repository includes comprehensive records of patient health statuses, socioeconomic profiles, hospital structures, and physician practices, enabling nuanced interventions and addressing complex healthcare needs. It also promotes interdisciplinary research and accelerates novel discoveries by providing a centralized source of diverse data and facilitating collaboration among research teams.
Conclusion: The OnetoMap meta-data repository represents a significant advancement in healthcare research by providing a centralized, detailed, and easily accessible repository of clinical research databases. Future directions include implementing automatic annual updates of datasets, exploring automatic dataset linkage, providing monthly updates on published research, creating a user chat space for enhanced collaboration, and developing code applets for simplified data analysis. These efforts will ensure that the repository remains current, functional, and accessible, ultimately facilitating new discoveries and insights in healthcare outcomes research.
