I2b2-etl: Python application for importing electronic health data into the informatics for integrating biology and the bedside platform.

MOTIVATION: The i2b2 platform is used at major academic health institutions and research consortia for querying for electronic health data. However, a major obstacle for wider utilization of the platform is the complexity of data loading that entails a steep curve of learning the platform's complex data schemas. To address this problem, we have developed the i2b2-etl package that simplifies the data loading process, which will facilitate wider deployment and utilization of the platform. RESULTS: We have implemented i2b2-etl as a Python application that imports ontology and patient data using simplified input file schemas and provides inbuilt record number de-identification and data validation. We describe a real-world deployment of i2b2-etl for a population-management initiative at MassGeneral Brigham. AVAILABILITY AND IMPLEMENTATION: i2b2-etl is a free, open-source application implemented in Python available under the Mozilla 2 license. The application can be downloaded as compiled docker images. A live demo is available at https://i2b2clinical.org/demo-i2b2etl/ (username: demo, password: Etl@2021). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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

OBJECTIVE: Integrating and harmonizing disparate patient data sources into one consolidated data portal enables researchers to conduct analysis efficiently and effectively. MATERIALS AND METHODS: We 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. RESULTS: As 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. DISCUSSION: The 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. CONCLUSION: The 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.

Implementation of informatics for integrating biology and the bedside (i2b2) platform as Docker containers.

BACKGROUND: Informatics for Integrating Biology and the Bedside (i2b2) is an open source clinical data analytics platform used at over 200 healthcare institutions for querying patient data. The i2b2 platform has several components with numerous dependencies and configuration parameters, which renders the task of installing or upgrading i2b2 a challenging one. Even with the availability of extensive documentation and tutorials, new users often require several weeks to correctly install a functional i2b2 platform. The goal of this work is to simplify the installation and upgrade process for i2b2. Specifically, we have containerized the core components of the platform, and evaluated the containers for ease of installation. RESULTS: We developed three Docker container images: WildFly, database, and web, to encapsulate the three major deployment components of i2b2. These containers isolate the core functionalities of the i2b2 platform, and work in unison to provide its functionalities. Our evaluations indicate that i2b2 containers function successfully on the Linux platform. Our results demonstrate that the containerized components work out-of-the-box, with minimal configuration. CONCLUSIONS: Containerization offers the potential to package the i2b2 platform components into standalone executable packages that are agnostic to the underlying host operating system. By releasing i2b2 as a Docker container, we anticipate that users will be able to create a working i2b2 hive installation without the need to download, compile, and configure individual components that constitute the i2b2 cells, thus making this platform accessible to a greater number of institutions.

Automating Installation of the Integrating Biology and the Bedside (i2b2) Platform.

Informatics for Integrating Biology and the Bedside (i2b2) is an open source clinical data analytics platform used at more than 150 institutions for querying patient data. An i2b2 installation (called hive) comprises several i2b2 cells that provide different functionalities. Given the complex architecture of i2b2 installation, creating a working installation of the platform is challenging for new users. This is despite the availability of extensive documentation for i2b2 and access to a large and active mailing list community of i2b2 users. To address this problem, we have created an automated installation package, called i2b2-quickstart, which automatically downloads the latest i2b2 source code and dependencies, and compiles and configures the i2b2 cells to create a functional i2b2 hive installation. This package will serve as a convenient starting point and reference implementation that will facilitate researchers in the installation and exploration of the i2b2 platform.

i2b2 implemented over SMART-on-FHIR.

Integrating Biology and the Bedside (i2b2) is the de-facto open-source medical tool for cohort discovery. Fast Healthcare Interoperability Resources (FHIR) is a new standard for exchanging health care information electronically. Substitutable Modular third-party Applications (SMART) defines the SMART-on-FHIR specification on how applications shall interface with Electronic Health Records (EHR) through FHIR. Related work made it possible to produce FHIR from an i2b2 instance or made i2b2 able to store FHIR datasets. In this paper, we extend i2b2 to search remotely into one or multiple SMART-on-FHIR Application Programming Interfaces (APIs). This enables the federation of queries, security, terminology mapping, and also bridges the gap between i2b2 and modern big-data technologies.

SMART-on-FHIR implemented over i2b2.

We have developed an interface to serve patient data from Informatics for Integrating Biology and the Bedside (i2b2) repositories in the Fast Healthcare Interoperability Resources (FHIR) format, referred to as a SMART-on-FHIR cell. The cell serves FHIR resources on a per-patient basis, and supports the "substitutable" modular third-party applications (SMART) OAuth2 specification for authorization of client applications. It is implemented as an i2b2 server plug-in, consisting of 6 modules: authentication, REST, i2b2-to-FHIR converter, resource enrichment, query engine, and cache. The source code is freely available as open source. We tested the cell by accessing resources from a test i2b2 installation, demonstrating that a SMART app can be launched from the cell that accesses patient data stored in i2b2. We successfully retrieved demographics, medications, labs, and diagnoses for test patients. The SMART-on-FHIR cell will enable i2b2 sites to provide simplified but secure data access in FHIR format, and will spur innovation and interoperability. Further, it transforms i2b2 into an apps platform.

Taking advantage of continuity of care documents to populate a research repository.

OBJECTIVE: Clinical data warehouses have accelerated clinical research, but even with available open source tools, there is a high barrier to entry due to the complexity of normalizing and importing data. The Office of the National Coordinator for Health Information Technology's Meaningful Use Incentive Program now requires that electronic health record systems produce standardized consolidated clinical document architecture (C-CDA) documents. Here, we leverage this data source to create a low volume standards based import pipeline for the Informatics for Integrating Biology and the Bedside (i2b2) clinical research platform. We validate this approach by creating a small repository at Partners Healthcare automatically from C-CDA documents. MATERIALS AND METHODS: We designed an i2b2 extension to import C-CDAs into i2b2. It is extensible to other sites with variances in C-CDA format without requiring custom code. We also designed new ontology structures for querying the imported data. RESULTS: We implemented our methodology at Partners Healthcare, where we developed an adapter to retrieve C-CDAs from Enterprise Services. Our current implementation supports demographics, encounters, problems, and medications. We imported approximately 17 000 clinical observations on 145 patients into i2b2 in about 24 min. We were able to perform i2b2 cohort finding queries and view patient information through SMART apps on the imported data. DISCUSSION: This low volume import approach can serve small practices with local access to C-CDAs and will allow patient registries to import patient supplied C-CDAs. These components will soon be available open source on the i2b2 wiki. CONCLUSIONS: Our approach will lower barriers to entry in implementing i2b2 where informatics expertise or data access are limited.

Apps to display patient data, making SMART available in the i2b2 platform.

The Substitutable Medical Apps, Reusable Technologies (SMART) project provides a framework of core services to facilitate the use of substitutable health-related web applications. The platform offers a common interface used to "SMART-ready" health IT systems allowing any SMART application to be able to interact with those systems. At Partners Healthcare, we have SMART-enabled the Informatics for Integrating Biology and the Bedside (i2b2) open source analytical platform, enabling the use of SMART applications directly within the i2b2 web client. In i2b2, viewing the patient in an EMR-like view enables a natural-feeling medical review process for each patient.

Strategies for maintaining patient privacy in i2b2.

BACKGROUND: The re-use of patient data from electronic healthcare record systems can provide tremendous benefits for clinical research, but measures to protect patient privacy while utilizing these records have many challenges. Some of these challenges arise from a misperception that the problem should be solved technically when actually the problem needs a holistic solution. OBJECTIVE: The authors' experience with informatics for integrating biology and the bedside (i2b2) use cases indicates that the privacy of the patient should be considered on three fronts: technical de-identification of the data, trust in the researcher and the research, and the security of the underlying technical platforms. METHODS: The security structure of i2b2 is implemented based on consideration of all three fronts. It has been supported with several use cases across the USA, resulting in five privacy categories of users that serve to protect the data while supporting the use cases. RESULTS: The i2b2 architecture is designed to provide consistency and faithfully implement these user privacy categories. These privacy categories help reflect the policy of both the Health Insurance Portability and Accountability Act and the provisions of the National Research Act of 1974, as embodied by current institutional review boards. CONCLUSION: By implementing a holistic approach to patient privacy solutions, i2b2 is able to help close the gap between principle and practice.

Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2).

Informatics for Integrating Biology and the Bedside (i2b2) is one of seven projects sponsored by the NIH Roadmap National Centers for Biomedical Computing (http://www.ncbcs.org). Its mission is to provide clinical investigators with the tools necessary to integrate medical record and clinical research data in the genomics age, a software suite to construct and integrate the modern clinical research chart. i2b2 software may be used by an enterprise's research community to find sets of interesting patients from electronic patient medical record data, while preserving patient privacy through a query tool interface. Project-specific mini-databases ("data marts") can be created from these sets to make highly detailed data available on these specific patients to the investigators on the i2b2 platform, as reviewed and restricted by the Institutional Review Board. The current version of this software has been released into the public domain and is available at the URL: http://www.i2b2.org/software.

Instrumenting the health care enterprise for discovery research in the genomic era.

Tens of thousands of subjects may be required to obtain reliable evidence relating disease characteristics to the weak effects typically reported from common genetic variants. The costs of assembling, phenotyping, and studying these large populations are substantial, recently estimated at three billion dollars for 500,000 individuals. They are also decade-long efforts. We hypothesized that automation and analytic tools can repurpose the informational byproducts of routine clinical care, bringing sample acquisition and phenotyping to the same high-throughput pace and commodity price-point as is currently true of genome-wide genotyping. Described here is a demonstration of the capability to acquire samples and data from densely phenotyped and genotyped individuals in the tens of thousands for common diseases (e.g., in a 1-yr period: N = 15,798 for rheumatoid arthritis; N = 42,238 for asthma; N = 34,535 for major depressive disorder) in one academic health center at an order of magnitude lower cost. Even for rare diseases caused by rare, highly penetrant mutations such as Huntington disease (N = 102) and autism (N = 756), these capabilities are also of interest.

Architecture of the open-source clinical research chart from Informatics for Integrating Biology and the Bedside.

The Informatics for Integrating Biology and the Bedside (i2b2) is one of the sponsored initiatives of the NIH Roadmap National Centers for Biomedical Computing (http://www.bisti.nih.gov/ncbc/). One of the goals of i2b2 is to provide clinical investigators broadly with the software tools necessary to collect and manage project-related clinical research data in the genomics age as a cohesive entity, a software suite to construct and manage the modern clinical research chart. The i2b2 "hive" is a set of software modules called "cells" that have a common messaging protocol that allow them to interact using web services and XML messages. Each cell can be developed by independent investigators to achieve specific analytic goals, and then be integrated into the hive to enhance the functionality available in the i2b2 Hive. We have applied this architecture through several ongoing clinical studies and found it to be of high value. The current version of this software has been released into the public domain and is available at the URL-http://www.i2b2.org.

A web portal that enables collaborative use of advanced medical image processing and informatics tools through the Biomedical Informatics Research Network (BIRN).

Launched in 2001, the Biomedical Informatics Research Network (BIRN; http://www.nbirn.net) is an NIH - NCRR initiative that enables researchers to collaborate in an environment for biomedical research and clinical information management, focused particularly upon medical imaging. Although it supports a vast array of programs to transform and calculate upon medical images, three fundamental problems emerged that inhibited collaborations. The first was that the complexity of the programs, and at times legal restrictions, combined to prohibit these programs from being accessible to all members of the teams and indeed the general researcher, although this was a fundamental mission of the BIRN. Second, the calculations that needed to be performed were very complex, and required many steps that often needed to be performed by different groups. Third, many of the analysis programs were not interoperable. These problems combined to created tremendous logistical problems. The solution was to create a portal-based workflow application that allowed the complex, collaborative tasks to take place and enabled new kinds of calculations that had not previously been practical.

Integration of clinical and genetic data in the i2b2 architecture.

The Informatics for Integrating Biology and the Bedside (i2b2) is one of the sponsored initiatives of the NIH Roadmap National Centers for Biomedical Computing (http://www.bisti.nih.gov/ncbc/). One of the goals of i2b2 is to provide clinical investigators broadly with the software tools necessary to collect and manage project-related clinical research data in the genomics age as a cohesive entity - a software suite to construct and manage the modern clinical research chart.