SEINE: Methods for Electronic Data Capture and Integrated Data Repository Synthesis with Patient Registry Use Cases

Integrated Data Repositories (IDR) allow clinical research to leverage electronic health records (EHR) and other data sources while Electronic Data Capture (EDC) applications often support manually maintained patient registries. Using i2b2 and REDCap, (IDR and EDC platforms respectively) we have developed methods that integrate IDR and EDC strengths supporting: 1) data delivery from the IDR as ready-to-use registries to exploit the annotation and data collection capabilities unique to EDC applications; 2) integrating EDC managed registries into data repositories allows investigators to use hypothesis generation and cohort discovery methods. This round-trip integration can lower lag between cohort discovery and establishing a registry. Investigators can also periodically augment their registry cohort as the IDR is enriched with additional data elements, data sources, and patients. We describe our open-source automated methods and provide three example registry uses cases for these methods: triple negative breast cancer, vertiginous syndrome, cancer distress

The Integrated Proactive Surveillance System for Prostate Cancer

In this paper, we present the design and implementation of the integrated proactive surveillance system for prostate cancer (PASS-PC). The integrated PASS-PC is a multi-institutional web-based system aimed at collecting a variety of data on prostate cancer patients in a standardized and efficient way. The integrated PASS-PC was commissioned by the Prostate Cancer Foundation (PCF) and built through the joint of efforts by a group of experts in medical oncology, genetics, pathology, nutrition, and cancer research informatics. Their main goal is facilitating the efficient and uniform collection of critical demographic, lifestyle, nutritional, dietary and clinical information to be used in developing new strategies in diagnosing, preventing and treating prostate cancer

Participatory Prototype Design: Developing a Sustainable Metadata Curation Workflow for Maternal Child Health Research

This paper describes the findings from a participatory prototype design project, where the authors worked with maternal and child health (MCH) researchers and stakeholders to develop a MCH metadata profile and sustainable curation workflow. This work led to the development of three prototypes: 1) a study catalogue hosted in Dataverse, 2) a metadata and research records repository hosted in REDCap and 3) a metadata harvesting tool/dashboard hosted within the Shiny RStudio environment. We present a brief overview of the methods used to develop the metadata profile, curation workflow and prototypes. Researchers and other stakeholders were participant-collaborators throughout the project. The participatory process involved a number of steps, including but not limited to: initial project design and grant writing; scoping and mapping existing practices, workflows and relevant metadata standards; creating the metadata profile; developing semi-automated and manual techniques to harvest and transform metadata; and end project sustainability/future planning. In this paper, we discuss the design process and project outcomes, limitations and benefits of the approach, and implications for researcher-oriented metadata and data curation initiatives

Why democratize bioinformatics?

Network bioinformatics and web-based data collection instruments have the capacity to improve the efficiency of the UK’s appropriately high levels of investment into cardiovascular research. A very large proportion of scientific data falls into the long-tail of the cardiovascular research distribution curve, with numerous small independent research efforts yielding a rich variety of specialty data sets. The merging of such myriad datasets and the eradication of data silos, plus linkage with outcomes could be greatly facilitated through the provision of a national set of standardised data collection instruments—a shared-cardioinformatics library of tools designed by and for clinical academics active in the long-tail of biomedical research. Across the cardiovascular research domain, like the rest of medicine, the national aggregation and democratization of diverse long-tail data is the best way to convert numerous small but expensive cohort data sources into big data, expanding our knowledge-base, breaking down translational barriers, improving research efficiency and with time, improving patient outcomes

A Software Tool for Automated Upload of Large Clinical Datasets Using REDCap and the CAPO Database

Introduction: Obtaining clinical data from healthcare sources is necessary for conducting clinical research. New technologies now allow for connecting a research database to Electronic Medical Records remotely, allowing the automatic import of clinical research data. In this paper we design and evaluate a REDCap extension to import clinical records from an external health database. Methods: Many hospital EHRs are designed to use secure file transfer protocol (SFTP) repositories for data communication. We develop a REDCap plugin to connect to an external SFTP file repository for the import of clinical record data. We use the CAPO instance of REDCap and a sample set of clinical pneumonia variables for the connection. Results: The plugin allows the input of record data in a much shorter time than traditional data entry in addition to being less error prone. However, the formatting of the data in the SFTP file repository must be exact in order for the import to be successful. This can require setup time on the part of EHR IT staff. Conclusion: Developing a direct connection from EHR to research database can be an effective way to lower the overhead for conducting clinical research. We demonstrate a means to do this using REDCap and SFTP

A Query Integrator and Manager for the Query Web

We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions

A REDCap-based model for electronic consent (eConsent): Moving toward a more personalized consent

Introduction: The updated common rule, for human subjects research, requires that consents begin with a \u27concise and focused\u27 presentation of the key information that will most likely help someone make a decision about whether to participate in a study (Menikoff, Kaneshiro, Pritchard. The New England Journal of Medicine. 2017; 376(7): 613-615.). We utilized a community-engaged technology development approach to inform feature options within the REDCap software platform centered around collection and storage of electronic consent (eConsent) to address issues of transparency, clinical trial efficiency, and regulatory compliance for informed consent (Harris, et al. Journal of Biomedical Informatics 2009; 42(2): 377-381.). eConsent may also improve recruitment and retention in clinical research studies by addressing: (1) barriers for accessing rural populations by facilitating remote consent and (2) cultural and literacy barriers by including optional explanatory material (e.g., defining terms by hovering over them with the cursor) or the choice of displaying different videos/images based on participant\u27s race, ethnicity, or educational level (Phillippi, et al. Journal of Obstetric, Gynecologic, and Neonatal Nursing. 2018; 47(4): 529-534.). Methods: We developed and pilot tested our eConsent framework to provide a personalized consent experience whereby users are guided through a consent document that utilizes avatars, contextual glossary information supplements, and videos, to facilitate communication of information. Results: The eConsent framework includes a portfolio of eight features, reviewed by community stakeholders, and tested at two academic medical centers. Conclusions: Early adoption and utilization of this eConsent framework have demonstrated acceptability. Next steps will emphasize testing efficacy of features to improve participant engagement with the consent process