Informatics Core Services (ICS)
Informatics Core Services (ICS) is an institutional shared resource (SR) that provides a wide range of biomedical informatics (BMI) and data analytics support to cancer-focused researchers throughout Washington University School of Medicine (WUSM), including the Siteman Cancer Center (SCC). Further, ICS works in close coordination with the SCC Biostatistics Shared Resource (BSR) and Tissue Procurement Core (TPC), as well as broader institutional units such as the Washington University Institute of Clinical and Translational Science (ICTS) and Institute for Public Health (IPH), thus facilitating access to seamless continuum of data, information, and knowledge management capabilities. More specifically, ICS provides electronic health record data brokerage and electronic data capture services to support clinical and translational research broadly at WUSM. As part of these services, ICS operates the Research Data Core (RDC), which contains data from Washington University Physicians and BJC HealthCare electronic health records spanning over 20 years. ICS provides billable services to broker access to deidentified, limited, and fully identified electronic health record (HER) data. ICS is also capable of performing extensive analytics and software development utilizing EHR data, including the use of natural language processing tools for data extraction. Over the last grant period, ICS has supported more than 100 cancer-focused publications and 20 types of NIH-NCI grants, including more than 30 R01, 10 T32, and numerous U24, U54, P50, P30, R37, K12, R21, U10, and U01 grants.
Access to ICS expertise, methods, and tools is initiated through the submission of web-based requests for services, followed by in-person project planning sessions, or by direct consultations that lead to the engagement of SR faculty and staff at an appropriate level given project needs. ICS projects are triaged, tracked, implemented, and supported using a systematic and agile project management methodology and tools that span all aspects of ICSs operations. Building upon these high-level objectives, the specific aims for ICS during the upcoming performance period are as follows:
Aim 1: Provide access to technology platforms capable of supporting heterogeneous biomedical data discovery and management requirements.
This includes: 1) Customization, deployment, and support of data capture, storage, and integration technologies that can enable the collection, management, and interrogation of heterogeneous biomedical data collections; 2) Analysis and execution/implementation of data requests, queries, and project-specific “datamarts” corresponding to the contents of the institution’s comprehensive research data warehouse (RDC) as well as specific reporting tools associated with WUSM’s shared EHR system (spanning WU Physicians and BJC Healthcare facilities); and 3) development, deployment, and management of custom software solutions as are needed to enable the research activities at SCC. Specific activities include:
- Assist in the design and delivery of program-project specific data models and electronic data capture instruments so as to codify, manage, and render discoverable and accessible a full spectrum of data resources derived from the activities of SCC investigators.
- Facilitate access to the RDC, a comprehensive research data warehouse that is fed by enterprise-wide clinical information systems and optimized for secondary use purposes.
- Establish interfaces between the EHRs and reporting/analytics platforms at participating sites and the RDC, to enable rapid and timely query and integration of data across and between SCC-affiliated entities.
Aim 2: Deliver state-of-the-art biomedical informatics and data analytics expertise and services.
Emphasis will be placed on 1) Query and analysis of publicly available datasets; 2) Integration and harmonization of complex and multi-scale datasets in order to support systems-level analyses of patient and population level phenotypes; and 3) Utilization of state-of-the-art artificial intelligence (AI) methodologies, including Machine Learning (ML) and Deep Learning (DL) techniques, in order to generate and test hypotheses in “big data” contexts. Specific activities include:
- Conduct consultative engagements with program-project investigators and their teams to ensure that state of-the-art BMI theories and methods are appropriately utilized, including AI techniques such as ML and DL where appropriate.
- Develop and conduct brown bags, seminars, and online training designed to educate the SCC community on the availability of data resources and efficient/appropriate use of these resources.
LOCATION: Cortex Building & 4444 Forest Park Parkway Building
PRICING: Please contact the core for current pricing of services offered.
TO ACCESS: e-mail firstname.lastname@example.org
NIH PUBLIC ACCESS POLICY: As of April 7, 2008, the NIH requires investigators with a publication using Siteman (or other NIH-funded) shared resources to submit (or have submitted for them) their final, peer reviewed manuscripts to PubMed Central(PMC) upon acceptance of publication, to be made publicly available within 12 months of publication. Many journals automatically submit these for authors, but Washington University also has assistance available through the Becker Medical Library. Please see http://publicaccess.nih.gov/FAQ.htm#b7 or http://becker.wustl.edu/classes-consulting/specialized-expertise/nih-public-access-policy for more information.
PUBLICATION ACKNOWLEDGEMENT: If research supported by the Informatics Core Services results in publication, please acknowledge this support by including the following in your publication(s):
We thank the Alvin J. Siteman Cancer Center at Washington University School of Medicine and Barnes-Jewish Hospital in St. Louis, MO. and the Institute of Clinical and Translational Sciences (ICTS) at Washington University in St. Louis, for the use of the Informatics Core Services, which provided __________ service. The Siteman Cancer Center is supported in part by an NCI Cancer Center Support Grant #P30 CA091842 and the ICTS is funded by the National Institutes of Health’s NCATS Clinical and Translational Science Award (CTSA) program grant #UL1 TR002345.