Traits and types of health data repositories

Abstract

We review traits of reusable clinical data and offer a typology of clinical repositories with a range of known examples. Sources of clinical data suitable for research can be classified into types reflecting the data’s institutional origin, original purpose, level of integration and governance. Primary data nearly always come from research studies and electronic medical records. Registries collect data on focused populations primarily to track outcomes, often using observational research methods. Warehouses are institutional information utilities repackaging clinical care data. Collections organize data from more organizations than a data warehouse, and more original data sources than a registry. Therefore even if they are heavily curated, their level of internal integration, and thus ease of use, can be less than other types. Federations are like collections except that physical control over data is distributed among donor organizations. Federations sometimes federate, giving a second level of organization. While the size, in number of patients, varies widely within each type of data source, populations over 10 K are relatively numerous, and much larger populations can be seen in warehouses and federations. One imagined ideal structure for research progress has been called an “Information Commons”. It would have longitudinal, multi-leveled (environmental through molecular) data on a large population of identified, consenting individuals. These are qualities whose achievement would require long term commitment on the part of many data donors, including a willingness to make their data public.

Making sense of big data in health research: Towards an EU action plan

“…Patient registries have for decades served as a key tool for assessing clinical outcome and clinical and health technology performance [ 13 – 15 ]. Rare disease registries pool data to achieve a sufficient sample size for epidemiological and/or clinical research [ 16 , 17 ].…”

Towards a European health research and innovation cloud (HRIC)

“…It therefore seems more promising to drive standardization within research communities while looking out for opportunities for overall standardization. Thus, the workshop envisioned the HRIC as a distributed collection of data repositories, people, and services, which together make up a framework for sharing and operating as a federated data commons, with reproducible software, standards, and expertise based on joint policies and guidelines on conducting health research, much like the smaller frameworks used successfully in previous initiatives [2,[16][17][18][19]. The need for federation is also highlighted in the proposed EU action plan for 'Making sense of big data in health research' [3].…”

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

“…A combination of clinical * and high-throughput molecular profiles (“omics”) † creates a very variable heterogeneous data set, where dimensionalities of different data types span several orders of magnitude. 12 Moreover, ensuring veracity, that is, quality, to clinical data is a challenging and time-consuming task. 13 , 14 This stems from a variety of collection methods, featuring manual data input, nondigital data capture, and nonstandard formats.…”

“…The emergence of big biomedical data sets, covering dozens of thousands of patients, 12 raises questions on infrastructure necessary to host and analyze them. Especially genomic data, generated rapidly due to dropping sequencing costs, pose a problem in terms of storage and analytics.…”