Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation

Abstract

Background Modern data-driven medical research provides new insights into the development and course of diseases and enables novel methods of clinical decision support. Clinical and translational data warehouses, such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART, are important infrastructure components that provide users with unified access to the large heterogeneous data sets needed to realize this and support use cases such as cohort selection, hypothesis generation, and ad hoc data analysis. Objective Often, different warehousing platforms are needed to support different use cases and different types of data. Moreover, to achieve an optimal data representation within the target systems, specific domain knowledge is needed when designing data-loading processes. Consequently, informaticians need to work closely with clinicians and researchers in short iterations. This is a challenging task as installing and maintaining warehousing platforms can be complex and time consuming. Furthermore, data loading typically requires significant effort in terms of data preprocessing, cleansing, and restructuring. The platform described in this study aims to address these challenges. Methods We formulated system requirements to achieve agility in terms of platform management and data loading. The derived system architecture includes a cloud infrastructure with unified management interfaces for multiple warehouse platforms and a data-loading pipeline with a declarative configuration paradigm and meta-loading approach. The latter compiles data and configuration files into forms required by existing loading tools, thereby automating a wide range of data restructuring and cleansing tasks. We demonstrated the fulfillment of the requirements and the originality of our approach by an experimental evaluation and a comparison with previous work. Results The platform supports both i2b2 and tranSMART with built-in security. Our experiments showed that the loading pipeline accepts input data that cannot be loaded with existing tools without preprocessing. Moreover, it lowered efforts significantly, reducing the size of configuration files required by factors of up to 22 for tranSMART and 1135 for i2b2. The time required to perform the compilation process was roughly equivalent to the time required for actual data loading. Comparison with other tools showed that our solution was the only tool fulfilling all requirements. Conclusions Our platform significantly reduces the efforts required for managing clinical and translational warehouses and for loading data in various formats and structures, such as complex entity-attribute-value structures often found in laboratory data. Moreover, it facilitates the iterative refinement of data representations in the target platforms, as the required configuration files are very compact. The quantitative measurements presented are consistent with our experiences of significantly reduced efforts for building warehousing platforms in close cooperation with medical researchers. Both the cloud-based hosting infrastructure and the data-loading pipeline are available to the community as open source software with comprehensive documentation.

Towards Harmonized Data Quality in the Medical Informatics Initiative – Current State and Future Directions

“…Since the underlying grant program was competitively bid, the information architecture is the same within consortium sites, but different between the consortia. The technical architecture of the ETL pipeline in DIFUTURE integrates comprehensive event logging through which data quality is viewed in a structured and detailed manner [5]. An audit service allows flexibly configurable quality analyses, which are executed on a SQLbased data mart but are independent of concrete schemas.…”

Data integration and analysis for circadian medicine

“…A common approach for integrating data for research purposes is to establish data warehouses, which are specific types of databases designed for analytical processing of heterogeneous data. To reduce the efforts required to transform data into common representations, pay‐as‐you‐go approaches can be utilized 43 . These approaches are based on the principle of not aiming for a fully integrated dataset from the beginning, but instead incrementally harmonizing it into a common representation as it is needed, for example, for a circadian medicine research project together with domain experts.…”

“…To reduce the efforts required to transform data into common representations, pay-as-you-go approaches can be utilized. 43 These approaches are based on the principle of not aiming for a fully integrated dataset from the beginning, but instead incrementally harmonizing it into a common representation as it is needed, for example, for a circadian medicine research project together with domain experts. This implies that efforts are invested when needed, leading to a more efficient integration process.…”

A Comprehensive Portal for Clinical and Translational Data Warehouses

“…A single web server (i.e. Apache 2) is placed in a dedicated gateway container, which manages ingoing and outgoing communication using proxy and reverse proxy configurations for each instance's i2b2 and tranSMART installation [3].…”

“…The infrastructure is based on container technology and supports setting up new instances and terminating old instances in a scalable manner. For this purpose, we have adopted the open source approach presented by Spengler et al [3], which makes use of the Docker software stack for containerization and management of multiple instances. Another advantage of the approach is that it supports agile processes for data loading based on close feedback cycles between informaticians and medical researchers.…”