Integrated Data Repositories Research Articles

The Mass General Brigham Biobank Portal: an i2b2-based data repository linking disparate and high-dimensional patient data to support multimodal analytics.

ObjectiveIntegrating and harmonizing disparate patient data sources into one consolidated data portal enables researchers to conduct analysis efficiently and effectively.Materials and MethodsWe describe an implementation of Informatics for Integrating Biology and the Bedside (i2b2) to create the Mass General Brigham (MGB) Biobank Portal data repository. The repository integrates data from primary and curated data sources and is updated weekly. The data are made readily available to investigators in a data portal where they can easily construct and export customized datasets for analysis.ResultsAs of July 2021, there are 125 645 consented patients enrolled in the MGB Biobank. 88 527 (70.5%) have a biospecimen, 55 121 (43.9%) have completed the health information survey, 43 552 (34.7%) have genomic data and 124 760 (99.3%) have EHR data. Twenty machine learning computed phenotypes are calculated on a weekly basis. There are currently 1220 active investigators who have run 58 793 patient queries and exported 10 257 analysis files.DiscussionThe Biobank Portal allows noninformatics researchers to conduct study feasibility by querying across many data sources and then extract data that are most useful to them for clinical studies. While institutions require substantial informatics resources to establish and maintain integrated data repositories, they yield significant research value to a wide range of investigators.ConclusionThe Biobank Portal and other patient data portals that integrate complex and simple datasets enable diverse research use cases. i2b2 tools to implement these registries and make the data interoperable are open source and freely available.

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions.

BackgroundIntegrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade.ObjectiveThe architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation.MethodsWe reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices.ResultsDespite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making).ConclusionsIDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

4549 Reproducible Informatics for Reproducible Translational Research

4549 Reproducible Informatics for Reproducible Translational Research

A Consensus-Based Approach for Harmonizing the OHDSI Common Data Model with HL7 FHIR.

A variety of data models have been developed to provide a standardized data interface that supports organizing clinical research data into a standard structure for building the integrated data repositories. HL7 Fast Healthcare Interoperability Resources (FHIR) is emerging as a next generation standards framework for facilitating health care and electronic health records-based data exchange. The objective of the study was to design and assess a consensus-based approach for harmonizing the OHDSI CDM with HL7 FHIR. We leverage a FHIR W5 (Who, What, When, Where, and Why) Classification System for designing the harmonization approaches and assess their utility in achieving the consensus among curators using a standard inter-rater agreement measure. Moderate agreement was achieved for the model-level harmonization (kappa = 0.50) whereas only fair agreement was achieved for the property-level harmonization (kappa = 0.21). FHIR W5 is a useful tool in designing the harmonization approaches between data models and FHIR, and facilitating the consensus achievement.

SPIRIT: Systematic Planning of Intelligent Reuse of Integrated Clinical Routine Data. A Conceptual Best-practice Framework and Procedure Model.

Secondary use of clinical routine data is receiving an increasing amount of attention in biomedicine and healthcare. However, building and analysing integrated clinical routine data repositories are nontrivial, challenging tasks. As in most evolving fields, recognized standards, well-proven methodological frameworks, or accurately described best-practice approaches for the systematic planning of solutions for secondary use of routine medical record data are missing. We propose a conceptual best-practice framework and procedure model for the systematic planning of intelligent reuse of integrated clinical routine data (SPIRIT). SPIRIT was developed based on a broad literature overview and further refined in two case studies with different kinds of clinical routine data, including process-oriented nursing data from a large hospital group and high-volume multimodal clinical data from a neurologic intensive care unit. SPIRIT aims at tailoring secondary use solutions to specific needs of single departments without losing sight of the institution as a whole. It provides a general conceptual best-practice framework consisting of three parts: First, a secondary use strategy for the whole organization is determined. Second, comprehensive analyses are conducted from two different viewpoints to define the requirements regarding a clinical routine data reuse solution at the system level from the data perspective (BOTTOM UP) and at the strategic level from the future users perspective (TOP DOWN). An obligatory clinical context analysis (IN BETWEEN) facilitates refinement, combination, and integration of the different requirements. The third part of SPIRIT is dedicated to implementation, which comprises design and realization of clinical data integration and management as well as data analysis solutions. The SPIRIT framework is intended to be used to systematically plan the intelligent reuse of clinical routine data for multiple purposes, which often was not intended when the primary clinical documentation systems were implemented. SPIRIT helps to overcome this gap. It can be applied in healthcare institutions of any size or specialization and allows a stepwise setup and evolution of holistic clinical routine data reuse solutions.

Using patient lists to add value to integrated data repositories

Patient lists are project-specific sets of patients that can be queried in integrated data repositories (IDR’s). By allowing a set of patients to be an addition to the qualifying conditions of a query, returned results will refer to, and only to, that set of patients. We report a variety of use cases for such lists, including: restricting retrospective chart review to a defined set of patients; following a set of patients for practice management purposes; distributing “honest-brokered” (deidentified) data; adding phenotypes to biosamples; and enhancing the content of study or registry data. Among the capabilities needed to implement patient lists in an IDR are: capture of patient identifiers from a query and feedback of these into the IDR; the existence of a permanent internal identifier in the IDR that is mappable to external identifiers; the ability to add queryable attributes to the IDR; the ability to merge data from multiple queries; and suitable control over user access and de-identification of results. We implemented patient lists in a custom IDR of our own design. We reviewed capabilities of other published IDRs for focusing on sets of patients. The widely used i2b2 IDR platform has various ways to address patient sets, and it could be modified to add the low-overhead version of patient lists that we describe.

Beyond the Clinic: ‘Direct-to-Consumer‘ Genomic Profiling Services and Pharmacogenomics

This article provides an overview of commercial pharmacogenetics and pharmacogenomics testing services offered online. The concept of 'beyond-the-clinic' (BTC) genomic testing is introduced to refer to the variety of formats in which these tests are offered and a typology of BTC models is developed. The authors review such models in relation to tests for individual drug response that are currently on offer. In conclusion, the authors argue that the evolving BTC domain provides opportunities for the pioneering of integrated data repositories, whose gatekeepers are patients or citizens. The authors anticipate that such developments will benefit pharmacogenomics sooner than other areas of medical practice.

Practices and perspectives on building integrated data repositories: results from a 2010 CTSA survey

Clinical integrated data repositories (IDRs) are poised to become a foundational element of biomedical and translational research by providing the coordinated data sources necessary to conduct retrospective analytic research and to identify and recruit prospective research subjects. The Clinical and Translational Science Award (CTSA) consortium's Informatics IDR Group conducted a survey of 2010 consortium members to evaluate recent trends in IDR implementation and use to support research between 2008 and 2010. A web-based survey based in part on a prior 2008 survey was developed and deployed to 46 national CTSA centers. A total of 35 separate organizations completed the survey (74%), representing 28 CTSAs and the National Institutes of Health Clinical Center. Survey results suggest that individual organizations are progressing in their approaches to the development, management, and use of IDRs as a means to support a broad array of research. We describe the major trends and emerging practices below.

Plenary III-01: A Methodology for Quantitative Measurement of Quality and Comprehensiveness of a Research Data Repository

Background and Aim: With the existence of research over federated repositories, it is desirable to utilize high quality integrated data repositories (IDRs). IDR can be defined as a data warehouse optimized for research purposes rather than clinical care, which contains clinical, administrative, trial, and -omics data. In this work, we focus on the quality of clinical and administrative components of IDRs. There is no standardized methodology which could quantitatively evaluate the quality of an IDR (e.g., ‘Does a given IDR have at least 2000 adult diabetic patients (type 1) with complete pediatric history?’). With the increased interest in research of existing data (such as comparative effectiveness research) and increasing number of institutions with an comprehensive IDRs, it is important to have a mechanism for selecting quality IDRs. Methods: Our poster will present a set of IDR quality measures which can compare IDRs in size and completeness. We considered the following criteria for a good measure: the measure is intuitive to interpret; facilitates monitoring improvement; and does not place any arbitrary value on individual measure components. Our methodology proposes a hierarchy of definitions of minimum EHR elements and uses a simple count of each level to quantitatively evaluate an IDR (e.g., count of patients with at least one diagnosis and one laboratory result). Results: We have applied our methodology to an IDR at Marshfield Clinic. Our poster will list all measures and results. Selected results were: 1.7M unique patients (level G1), 0.4M patients with at least one diagnosis, lab and prescription (level D3). To facilitate evaluations at other institutions, we have created an ANSI-SQL script which can compute all measures in a single execution. Conclusion: Our evaluation methodology provides a quick way to compare IDRs at different institutions. It can be applied to institutions contributing to a virtual warehouse. Our goal was to arrive at a pragmatic set of measures operating on an easy-to-implement event schema. The limitations are: focus on general research idea and event types and criteria included in some level definitions. We plan to conduct a Delphi study involving informatics experts to arrive at an improved consensus set of measures.