scholarly journals 3432 Stanford MedTech: An Innovative CTSA-Supported Pilot Program

2019 ◽  
Vol 3 (s1) ◽  
pp. 126-127
Author(s):  
Ashley Dunn ◽  
Linda Lucian ◽  
Gordon Saul ◽  
Paul Yock ◽  
Mark Cullen
Keyword(s):  
Patient Care ◽  
Translational Research ◽  
Mobile Technologies ◽  
Patient Specific ◽  
Pilot Program ◽  
Health Technologies ◽  
Funding Mechanism ◽  
Hands On ◽  
Practical Guidance ◽  
Clinical And Translational Research

OBJECTIVES/SPECIFIC AIMS: Helping researchers assess and effectively translate innovations into healthcare improvements is a complex process (Terry et. al., 2013). The Clinical Translational Science Awards (CTSA)—supported by the National Institute of Health (NIH) under the auspices of the National Center for Advancing Translational Sciences (NCATS)— provide the resources and support needed to strengthen our nation’s clinical and translational research (CTR) enterprise. In 2008, Stanford University was awarded a CTSA from the NIH, establishing Spectrum (the Stanford Center for Clinical and Translational Research and Education). Under the Spectrum umbrella, the Byers Center for Biodesign manages the MedTech Pilot Program with the goal of translating discoveries into novel health technologies that address important unmet health needs. The MedTech Pilot Program is an innovative funding mechanism that seeks to (1) stimulate clinical translational research, (2) help promising projects bridge the gap between the bench and the patients’ bedside, and (3) encourage collaborative, transdisciplinary work. Specifically, the Pilot Program offers up to $50,000 to support projects involving medical devices and mobile technologies used for (1) therapeutic applications and (2) device-based patient-specific (or POC) diagnostic applications. This analysis of the MedTech Pilot Program will: 1) describe the Program’s structure and process; 2) highlight the intensive, hands-on mentorship and practical guidance awardees receive that enables them to more efficiently and effectively advance their projects toward patient care; and 3) characterize the progress of the 36 funded projects. METHODS/STUDY POPULATION: Key elements of the Pilot Program’s infrastructure and mentoring processes as they relate to project outcomes were identified. Additionally, outcomes data were collected from two sources: (1) annual survey of Pilot Awardees and (2) publicly available information relevant to the pilot projects. RESULTS/ANTICIPATED RESULTS: The Pilot Program’s framework and infrastructure has supported a diverse group of transdisciplinary projects. These projects were evaluated using both traditional and non-traditional metrics (e.g., patents, startups, publications). The initial investment of $1.5 million to fund 36 projects has led to over $88 million dollars in additional funding. Additionally, taking full advantage of the expertise in Silicon Valley, strong mentorship has helped advance projects along the clinical and translational path. DISCUSSION/SIGNIFICANCE OF IMPACT: The Pilot Program has benefited Stanford innovators and researchers by providing seed funding to help promising projects bridge the gap between the bench and the bedside. The intensive, hands-on mentorship, early pilot funding, and practical guidance pilot awardees receive effectively help translate their technologies into patient care.

Clinical and Translational Research Challenges in Neuroendocrine Tumours

2020 ◽  
Vol 27 (29) ◽  
pp. 4823-4839 ◽  
Author(s):  
Jorge Barriuso ◽  
Angela Lamarca
Keyword(s):  
Translational Research ◽  
Rare Diseases ◽  
Neuroendocrine Tumours ◽  
Basic Research ◽  
Research Projects ◽  
Limited Sample ◽  
Real World Data ◽  
Health Technologies ◽  
Rare Tumours ◽  
Clinical And Translational Research

: Neuroendocrine tumours (NETs) represent a range of neoplasms that may arise from any (neuro)endocrine cell situated in any part of the human body. As any other rare diseases, NETs face several difficulties in relation to research. This review will describe some of the main challenges and proposed solutions faced by researchers with expertise in rare malignancies. : Some of the most common challenges in clinical and translational research are enumerated in this review, covering aspects from clinical, translational and basic research. NETs being a heterogeneous group of diseases and a limited sample size of clinical and translational research projects are the main challenges. : Challenges with NETs lay over the disparities between healthcare models to tackle rare diseases. NETs add an extra layer of complexity due to a numerous group of different entities. : Prospective real-world data trials are an opportunity for rare cancers with the revolution of electronic health technologies. This review explores potential solutions to these challenges that could be useful not only to the NET community but also to other rare tumours researchers.

scholarly journals 3406 Understanding the Barriers, Challenges, and Facilitators to Community-Engaged Research: A Review of a CTSA Community Engagement Pilot Program

2019 ◽  
Vol 3 (s1) ◽  
pp. 99-100
Author(s):  
Ashley Dunn ◽  
Kendra L. Smith ◽  
Rhonda McClinton-Brown ◽  
Jill W. Evans ◽  
Lisa Goldman-Rosas ◽  
...  
Keyword(s):  
Health Disparities ◽  
Focus Group ◽  
Community Engagement ◽  
Translational Research ◽  
Pilot Project ◽  
Pilot Program ◽  
Stanford University ◽  
Community Engaged Research ◽  
Academic Partnerships ◽  
Clinical And Translational Research

OBJECTIVES/SPECIFIC AIMS: Engaging patients and consumers in research is a complex process where innovative strategies are needed to effectively translate scientific discoveries into improvements in the public’s health (Wilkins et. al., 2013; Terry et. al., 2013). The Clinical Translational Science Awards (CTSA)—supported by the National Institute of Health (NIH) under the auspices of the National Center for Advancing Translational Sciences (NCATS)—aim to provide resources and support needed to strengthen our nation’s clinical and translational research (CTR) enterprise. In 2008, Stanford University was awarded a CTSA from the NIH, establishing Spectrum (Stanford Center for Clinical and Translational Research and Education) and its Community Engagement (CE) Program aimed at building long-standing community-academic research partnerships for translational research in the local area surrounding Stanford University. To date, the CE Pilot Program has funded 38 pilot projects from the 2009-2017 calendar year. The purpose of this study was to understand, through a unique pilot program, the barriers, challenges, and facilitators to community-engaged research targeting health disparities as well as community-academic partnerships. METHODS/STUDY POPULATION: Investigators conducted a qualitative study of the community engagement pilot program. Previous pilot awardees were recruited via email and phone to participate in a one-hour focus group to discuss their pilot project experience—describing any barriers, challenges, and facilitators to implementing their pilot project. RESULTS/ANTICIPATED RESULTS: The focus group revealed that community engage research through the pilot program was not only appreciated by faculty, but projects were successful, and partnerships developed were sustained after funding. Specifically, the pilot program has seen success in both traditional and capacity building metrics: the initial investment of $652,250.00 to fund 38 projects has led to over $11 million dollars in additional grant funding. In addition, pilot funding has led to peer-reviewed publications, data resources for theses and dissertations, local and national presentations/news articles, programmatic innovation, and community-level impact. Challenges and barriers were mainly related to timing, grant constraints, and university administrative processes. DISCUSSION/SIGNIFICANCE OF IMPACT: The Community Engagement Pilot Program demonstrates an innovative collaborative approach to support community-academic partnerships. This assessment highlights the value and importance of pilot program to increase community engaged research targeting health disparities. Challenges are mainly administrative in nature: pilot awardees mentioned difficulties working on university quarterly timelines, challenges of subcontracting or sharing money with community partners, onerous NIH prior approval process, and limitations to carryover funding. However, pilot grants administered through the program strengthen the capacity to develop larger scale community-based research initiatives.

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.

The process of sourcing and preparing electronic health records data to implement a machine-learning algorithm for early identification of maternal cardiovascular risk (Preprint)

2021 ◽  
Author(s):  
Nawar Shara ◽  
Kelley M. Anderson ◽  
Noor Falah ◽  
Maryam F. Ahmad ◽  
Darya Tavazoei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Risk ◽  
Electronic Health Records ◽  
Patient Care ◽  
Early Identification ◽  
Health Data ◽  
Patient Specific ◽  
Patient Records ◽  
Health Records ◽  
Electronic Health

BACKGROUND Healthcare data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes [6]. However, the differences that exist in each individual’s health records, combined with the lack of health-data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. While these problems exist throughout healthcare, they are especially prevalent within maternal health, and exacerbate the maternal morbidity and mortality (MMM) crisis in the United States. OBJECTIVE Maternal patient records were extracted from the electronic health records (EHRs) of a large tertiary healthcare system and made into patient-specific, complete datasets through a systematic method so that a machine-learning-based (ML-based) risk-assessment algorithm could effectively identify maternal cardiovascular risk prior to evidence of diagnosis or intervention within the patient’s record. METHODS We outline the effort that was required to define the specifications of the computational systems, the dataset, and access to relevant systems, while ensuring data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for its use by a proprietary risk-stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions. RESULTS Patient records can be made actionable for the goal of effectively employing machine learning (ML), specifically to identify cardiovascular risk in pregnant patients. CONCLUSIONS Upon acquiring data, including the concatenation, anonymization, and normalization of said data across multiple EHRs, the use of a machine-learning-based (ML-based) tool can provide early identification of cardiovascular risk in pregnant patients. CLINICALTRIAL N/A

Applying Translational Research in Understanding Complications and Defining Targets for Intervention: Inflammation in PD as a Model

2009 ◽  
Vol 29 (2) ◽  
pp. 135-141
Author(s):  
Roberto Pecoits–Filho
Keyword(s):  
Peritoneal Dialysis ◽  
Patient Care ◽  
Translational Research ◽  
Research Question ◽  
Underlying Disease ◽  
Cardiovascular Complications ◽  
Research Environment ◽  
The Core ◽  
Quality Of Patient Care

The bench-to-bedside approach to translational research is becoming increasingly important to efficiently advance understanding of the mechanisms underlying disease and to improve the quality of patient care. Although this investigation model has been practiced since the early days of the therapy, robust research platforms built to practice translational research have only recently been structured in the field of peritoneal dialysis. Experience with a translational research environment that generated most of the information cited in this overview is the core of this manuscript. The central investigation theme described is how to approach the cardiovascular complications of peritoneal dialysis. The research question was, could the continuous activation of inflammatory pathways be central in this process and represent a relevant target for interventions?

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