In the 19th century, Florence Nightingale pointed to the importance of nursing documentation for the care of patients and the necessity of data-based statistics for quality improvement. The same century, John Snow projected his observations about patients with Cholera on a street map, laying the ground for modern epidemiological science. The historical examples demonstrate that proper data are the foundation of relevant information about individuals and of new scientific evidence. In the ideal case of Ackoff’s pyramid, information, knowledge, understanding, and wisdom arise from data.

Metadata management is an essential condition to follow the FAIR principles. Therefore, metadata management was one asset of an accompanying project within a funding scheme for registries in health services research. The metadata of the funded projects were acquired, combined in a database compatible with the metamodel of ISO/IEC 11179 “Information technology – Metadata registries” third edition (ISO/IEC 11179-3), and analyzed in order to support the development and the operation of the registries. In the second phase of the funding scheme, six registries delivered a complete update of their metadata. The mean number of data elements increased from 245.7 to 473.5 and the mean number of values from 569.5 to 1,306.0. The conceptual core of the database had to be extended by one third to cover the new elements. The reason for this increase remained unclear. Constraints from the grant might be causal, a deviation from an evidence-based development process as well. It is questionable, whether the revealed quality of the metadata is sufficient to fulfill the FAIR principles. The extension of the metamodel of ISO/IEC 11179-3 is in agreement with the literature. However, further research is needed to find workable solutions for metadata management.

Background Patient registries are an established methodology in health services research. Since more than 150 years, registries collect information concerning groups of similar patients to answer research questions. Elaborated recommendations about an appropriate development and an efficient operation of registries are available. However, the scene changes rapidly. Objectives The aim of the study is to describe current trends in registry research for health services research. Methods Registries developed within a German funding scheme for model registries in health services research were analyzed. The observations were compared with recent recommendations of the Agency for Healthcare Research and Quality (AHRQ) on registries in the 21st century. Results Analyzing six registries from the funding scheme revealed the following trends: recruiting healthy individuals, representing familial or other interpersonal relationships, recording of patient-reported experiences or outcomes, accepting participants as study sites, active informing of participants, integrating the registry with other data collections, and transferring data from the registry to electronic patient records. This list partly complies with the issues discussed by the AHRQ. The AHRQ structured its ideas in five chapters, increasing focus on the patient, engaging patients as partners, digital health and patient registries, direct-to-patient registry, and registry networks. Conclusion For the near future, it can be said that the concept and the design of a registry should place the patient in the center. Registries will be increasingly linked together and interconnected with other data collections. New challenges arise regarding the management of data quality and the interpretation of results from less controlled settings. Here, further research related to the methodology of registries is needed.

The FAIR Guiding Principles do not address the quality of data and metadata. Therefore, data collections could be FAIR but useless. In a funding initiative of registries for health services research, trueness of data received special attention. Completeness in the definition of recall was selected to represent this dimension in a cross-registry benchmarking. The first analyses of completeness revealed a diversity of its implementation. No registry was able to present results exactly as requested in a guideline on data quality. Two registries switched to a source data verification as alternative, the three others downsized to the dimension integrity. The experiences underline that the achievement of appropriate data quality is a matter of costs and resources, whereas the current Guiding Principles quote for a transparent culture regarding data and metadata. We propose the extension to FAIR-Q, data collections should not only be findable, accessible, interoperable, and reusable, but also quality assured.