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Vast amounts of data are created during routine patient care which are stored in unstructured digital and hardcopy formats in healthcare institutions. Analysis of large databases help to define the healthcare needs of the population and to organize healthcare services for specific diseases. As a model, we selected multiple sclerosis (MS), a disease with well-defined diagnostic criteria, a usually inpatient initial diagnosis, and a need for regular outpatient check-up. Using multiple sclerosis as an example, we set forth to screen and analyze international and Hungarian databases. In the framework of the initiation of the data lake system of Semmelweis University, we aim to define features of the data system needed for disease-specific databases for future applications. To determine essential data-entry criteria for such a database, we review the most important multiple sclerosis registries. We evaluate the type of registered data, structure of database, privacy issues, the availability and ways of application of the databases. Initially, the MS databases were created locally, aiming for better care of patients. As a further step, data were collected for scientific research by national and international co-operations. Disease-specific databases have become of high priority for national healthcare providers, and long-term information on a population (“real-world” data) is extremely important to assess the effectivity and safety of a treatment at the population level. Our analysis contributes to a project which focuses on the aspects of developing a data lake at a service provider level including clinical, diagnostic and digital healthcare departments of Semmelweis University, Budapest, Hungary. Orv Hetil. 2019; 160(4): 123–130.

Nagy adatbázisok neurológiai kórképekben: nemzetközi áttekintés a sclerosis multiplex példáján