REDCap
REDCap (Research Electronic Data Capture) is a web application created by Vanderbilt University to facilitate data acquisition and management for a wide variety of projects, especially Institutional Review Board (IRB)-approved clinical research and basic research. Data collected in the course of the research are managed by the program and can be analyzed separately by commonly used statistical packages, including SAS, Stata, SPSS and R.
OnCore Clinical Research Management System
MU supports use of the OnCore Clinical Trial Management System to streamline clinical trials research operations from pre-award through post-award. This tool allows you to gain visibility into all aspects of your research operations with a comprehensive, proven, standardized CRMS. Contact our Research Operations team for training and support.
NextGen Outback powered by the PCORnet Common Data Model
The NextGen Outback is a clinical data repository that stages data from electronic health records, billing systems, registries and other resources such as genomic results. This data is then transformed into the non-proprietary PCORnet Common Data Model (https://pcornet.org/wp-content/uploads/2020/12/PCORnet-Common-Data-Model-v60-2020_10_221.pdf) that is aligned with terminology standards to allow you to conduct multicenter research studies and advance reproducible research. By developing against the PCORnet CDM you can then disseminate your analyses or study inclusion criteria across regional and national research networks. Depending on your research protocol, we can provide access to de-identified, limited data sets or identifiable data and can conduct privacy preserving record linkage. We can also integrate additional data from current source systems or stage data sources from your research program (BYOD).
Greater Plains Collaborative and PCORnet Research Network
NextGen BMI leads the Greater Plains Collaborative (GPC) Clinical Research Network, which is part of PCORnet, the National Patient-Centered Clinical Research Network. Notably, the GPC maintains GROUSE (https://gpcnetwork.org/?q=GROUSE ), which combines multi-state Medicare and Medicaid claims with the EHR and registry data in PCORnet CDM format from the thirteen participating GPC health systems. These network collaborations provide the backbone and stakeholder engagement for many national and regional research programs. While you can visit our websites to learn more, please contact us locally first to help you strategize on going from local prototype to regional or national multicenter studies.
Health Facts
Health Facts® is a de-identified research database comprising health-care data recorded during the course of day-to-day patient-care encounters and aggregated from health systems that contribute to the database. Cerner maps, merges and organizes the data into one database of consistent data elements suitable for research. Health Facts® incorporates data from 90 health systems and over 600 facilities located in the United States. Data are available through the end of 2018, and the environment is supported by the UMKC team.
Cerner Real World Data (RWD)
As a participant in Cerner’s Learning Health Network, our researchers and collaborators gain free access to Cerner’s Real-World Data multi-client de-identified research database. These data are available at no cost through an AWS-based data science environment featuring common open-source tools including Apache Spark™, Jupyter™, Python®, SparkR, PySpark and Spark SQL. Licenses are provided on a first-come, first-served basis for approved projects for a period of one month or as availability lasts. Preference is given to funded projects.
i2b2
Informatics for Integrating Biology and Bedside (i2b2) is an informatics framework to leverage existing data for cohort identification, retrospective data analysis, feasibility study and hypothesis generation. i2b2 is an open-source system that was originally developed at Partners HealthCare System in Boston, Massachusetts, through NIH funding. i2b2 provides an interactive tool for querying and exporting of data by end-users and does not require knowledge of any technical programming languages.
