scholarly journals Applying Process Improvement Methods to Clinical and Translational Research: Conceptual Framework and Case Examples

2015 ◽  
Vol 8 (6) ◽  
pp. 779-786 ◽  
Author(s):  
Denise H. Daudelin ◽  
Harry P. Selker ◽  
Laurel K. Leslie
Keyword(s):  
Conceptual Framework ◽  
Translational Research ◽  
Process Improvement ◽  
Case Examples ◽  
Clinical And Translational Research

scholarly journals 2332

2017 ◽  
Vol 1 (S1) ◽  
pp. 74-74 ◽  
Author(s):  
Dennis P. Scanlon ◽  
Laura J. Wolf ◽  
Cynthia Chuang ◽  
Jen Kraschnewski ◽  
Eugene Lengerich ◽  
...  
Keyword(s):  
Conceptual Framework ◽  
Community Engagement ◽  
Translational Research ◽  
Research Committee ◽  
Literature Synthesis ◽  
Individual Project ◽  
Study Population ◽  
Effective Community ◽  
Major Factors ◽  
Clinical And Translational Research

OBJECTIVES/SPECIFIC AIMS: Community engagement is a commonly used term, but is complex in both meaning and application. In order to help academic institutions and administrators develop infrastructure to promote and support community engagement and to help investigators work productively with communities, this analysis discusses the major components of community engagement in research on both the institutional and individual project levels as well as the interplay between them. METHODS/STUDY POPULATION: A literature synthesis conducted by a community engagement in research committee at 1 CTSA institution that examined the myriad factors related to effective community engagement in research identified across multiple disciplines was used to distill the major factors identified, assesses the interplay of the identified factors, and produce a conceptual model to help administrators and investigators apply best practices in engaging communities in clinical and translational research. RESULTS/ANTICIPATED RESULTS: This work takes a concept—community engagement in research—that is often stated and discussed, but is highly complex and challenging to implement—and identifies and discusses the multiple, interrelated factors germane to it. The model illustrates that while community engagement in research is implemented in the context of individual projects, a deep and continual interplay between individual projects and the goals, capacity, and policies of research institutions is needed for rigorous, ethical, and effective community engagement. DISCUSSION/SIGNIFICANCE OF IMPACT: Results are presented through a conceptual framework which displays the major components needed for rigorous, ethical, and effective community engagement in clinical and translational research. In addition, the conceptual framework presented will provide assistance to those developing approaches to measure and evaluate institutional readiness for community engagement in research as well as the effectiveness of individual community engagement efforts.

scholarly journals Participatory Needs Assessment and Action Planning for a Clinical and Translational Research Network

2020 ◽  
pp. 1-23
Author(s):  
LaKaija J. Johnson ◽  
Jolene Rohde ◽  
Mary E. Cramer ◽  
Lani Zimmerman ◽  
Carol R. Geary ◽  
...  
Keyword(s):  
Needs Assessment ◽  
Translational Research ◽  
Action Planning ◽  
Research Network ◽  
Clinical And Translational Research

scholarly journals Clinical and Translational Research and Community Engagement: Implications for Researcher Capacity Building

2012 ◽  
Vol 5 (4) ◽  
pp. 329-332 ◽  
Author(s):  
Linda Sprague Martinez ◽  
Beverley Russell ◽  
Carolyn Leung Rubin ◽  
Laurel K. Leslie ◽  
Doug Brugge
Keyword(s):  
Community Engagement ◽  
Translational Research ◽  
Capacity Building ◽  
Clinical And Translational Research

High-Quality Peer Review of Clinical and Translational Research

2021 ◽  
Vol 78 (15) ◽  
pp. 1564-1568
Author(s):  
Fred M. Kusumoto ◽  
John A. Bittl ◽  
Mark A. Creager ◽  
Harold L. Dauerman ◽  
Anuradha Lala ◽  
...  
Keyword(s):  
Translational Research ◽  
Peer Review ◽  
High Quality ◽  
Clinical And Translational Research

ARGO, Automatic Record Generator for Oncology: a natural language process-based tool to capture pathology features from onco-hematological reports (Preprint)

2021 ◽  
Author(s):  
Gian Maria Zaccaria ◽  
Vito Colella ◽  
Simona Colucci ◽  
Felice Clemente ◽  
Fabio Pavone ◽  
...  
Keyword(s):  
Natural Language ◽  
Translational Research ◽  
Language Processing ◽  
Character Recognition ◽  
Web Application ◽  
Optical Character Recognition ◽  
Anatomical Site ◽  
Cell Of Origin ◽  
Molecular Features ◽  
Clinical And Translational Research

BACKGROUND The unstructured nature of medical data from Real-World (RW) patients and the scarce accessibility for researchers to integrated systems restrain the use of RW information for clinical and translational research purposes. Natural Language Processing (NLP) might help in transposing unstructured reports in electronic health records (EHR), thus prompting their standardization and sharing. OBJECTIVE We aimed at designing a tool to capture pathological features directly from hemo-lymphopathology reports and automatically record them into electronic case report forms (eCRFs). METHODS We exploited Optical Character Recognition and NLP techniques to develop a web application, named ARGO (Automatic Record Generator for Oncology), that recognizes unstructured information from diagnostic paper-based reports of diffuse large B-cell lymphomas (DLBCL), follicular lymphomas (FL), and mantle cell lymphomas (MCL). ARGO was programmed to match data with standard diagnostic criteria of the National Institute of Health, automatically assign diagnosis and, via Application Programming Interface, populate specific eCRFs on the REDCap platform, according to the College of American Pathologists templates. A selection of 239 reports (n. 106 DLBCL, n.79 FL, and n. 54 MCL) from the Pathology Unit at the IRCCS - Istituto Tumori “Giovanni Paolo II” of Bari (Italy) was used to assess ARGO performance in terms of accuracy, precision, recall and F1-score. RESULTS By applying our workflow, we successfully converted 233 paper-based reports into corresponding eCRFs incorporating structured information about diagnosis, tissue of origin and anatomical site of the sample, major molecular markers and cell-of-origin subtype. Overall, ARGO showed high performance (nearly 90% of accuracy, precision, recall and F1-score) in capturing identification report number, biopsy date, specimen type, diagnosis, and additional molecular features. CONCLUSIONS We developed and validated an easy-to-use tool that converts RW paper-based diagnostic reports of major lymphoma subtypes into structured eCRFs. ARGO is cheap, feasible, and easily transferable into the daily practice to generate REDCap-based EHR for clinical and translational research purposes.

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