Registries: Big data, bigger problems?

Patient registries have grown in size and number along with general computing power and digitization of the healthcare world. In contrast to databases, registries are typically patient data systematically created and collected for the express purpose of answering health-related questions. Registries can be disease-, procedure-, pathology-, or product-based in nature. Registry-based studies typically fit into Level II or III in the hierarchy of evidence-based medicine. However, a recent advent in the use of registry data has been the development and execution of registry-based trials, such as the TASTE trial, which may elevate registry-based studies into the realm of Level I evidence. Some strengths of registries include the sheer volume of data, the inclusion of a diverse set of participants, and their ability to be linked to other registries and databases. Limitations of registries include variable quality of the collected data, and a lack of active follow-up (which may underestimate rates of adverse events). As with any study type, the intended design does not automatically lead to a study of a certain quality. While no specific tool exists for assessing the quality of a registry-based study, some important considerations include ensuring the registry is appropriate for the question being asked, whether the patient population is representative, the presence of an appropriate comparison group, and the validity and generalizability of the registry in question. The future of clinical registries remains to be seen, but the incorporation of big data and machine learning algorithms will certainly play an important role. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Patient registries have become larger and more numerous with the advancement of computing power and digitization in healthcare. These registries are specifically designed to collect patient data in order to answer health-related questions. Registry-based studies can provide valuable evidence in medical research, and the development of registry-based trials has the potential to elevate these studies to the highest level of evidence. While registries have strengths in terms of data volume, diversity of participants, and linkage to other databases, they also have limitations in terms of data quality and follow-up. Assessing the quality of a registry-based study involves important considerations such as appropriateness for the research question, representative patient population, comparison group, and validity and generalizability of the registry. The future of clinical registries is likely to involve the incorporation of big data and machine learning algorithms.