Library support for clinical and translational research: research data management and data science

Objective: Librarians supporting Yale\u27s CTSA grantee, the Yale Center for Clinical Investigation, found that research data support is needed at multiple stages in the clinical research lifecycle. This poster highlights the research data needs of clinical and translational research staff and resources that medical librarians can leverage to support them. Methods: Through discussions with project managers, we identified some eighteen research support needs which are presented by clinical and translational research projects, and which library resources can meet. Several of these research support needs are related to research data management and data science. - A sink-or-swim style of research training, in terms of everything from literature searching to research data management - Confusion about data sharing requirements from funders and journals - Questions about how best to measure certain outcomes, which can be answered, in some cases, with reference to Common Data Elements - Missing or incomplete preregistrations, which are important because preregistration is an important tool to promote transparency - Questions about identifying sites, through Census data and GIS, where diverse study participants could be recruited Results: We are developing cross-training for librarians, and workshops for CTSA staff, to meet these needs. Conclusions: We hope that, after iterating versions of these workshops with CTSA staff, we will be able to share helpful insights about library support for translational research in the context of data management and data science. These findings will also inform our approach to data management training for residents and clinicians, as well as students

Prototype of running clinical trials in an untrustworthy environment using blockchain.

Monitoring and ensuring the integrity of data within the clinical trial process is currently not always feasible with the current research system. We propose a blockchain-based system to make data collected in the clinical trial process immutable, traceable, and potentially more trustworthy. We use raw data from a real completed clinical trial, simulate the trial onto a proof of concept web portal service, and test its resilience to data tampering. We also assess its prospects to provide a traceable and useful audit trail of trial data for regulators, and a flexible service for all members within the clinical trials network. We also improve the way adverse events are currently reported. In conclusion, we advocate that this service could offer an improvement in clinical trial data management, and could bolster trust in the clinical research process and the ease at which regulators can oversee trials

Data Management in Clinical Research

No matter the complexity of a clinical research, one critical component that serves as bridge between conceptualization of research idea and the eventual publication of findings is data management. It encapsulates the whole processes involved in managing the life cycle of data in a clinical research project. Data management can make or mar any research. For example, a research with scientifically sound design but poor, incoherent data management procedure would end up with poor quality data. In this article, the author provides a brief overview of key considerations for data management in clinical research

CLINICAL DATA MANAGEMENT IMPORTANCE IN CLINICAL RESEARCH

ABSTRACTOver the last few decades, most of the pharmaceutical companies and research sponsors are facing a lot of challenges in clinical research for theirnew drug approval. The sponsor research needs a high-quality data report for getting new drug approval from Food and Drug Administration for theirmedical products. Clinical trial data are important for the drug and medical device development processing pharmaceutical companies to examineand evaluate the efficacy and safety of the new medical product in human volunteers. The results of the clinical trial studies generate the mostvaluable data and in recent years; there has been massive development in the field of clinical trials. A good clinical data management system reducesthe duration of the study and cost of drug development. Further a well-designed case report form (CRF) assists data collection and make facilitatesdata management and statistical analysis. Nowadays, the electronic data capture (EDC) is very beneficial in data collection. EDC helps to speed up theclinical trial process and reduces the duration, errors and make the work easy in the data management system. This article highlights the importanceof data management processes involved in the clinical trial and provides an overview of the clinical trial data management tools. The study concludedthat data management tools play a key role in the clinical trial and well-designed CRFs reduces the errors and save the time of the clinical trials andfacilitates the drug discovery and development.Keywords: Pharmaceutical, Clinical trial, Clinical data management, Data capture

Biomedical Informatics Applications for Precision Management of Neurodegenerative Diseases

Modern medicine is in the midst of a revolution driven by “big data,” rapidly advancing computing power, and broader integration of technology into healthcare. Highly detailed and individualized profiles of both health and disease states are now possible, including biomarkers, genomic profiles, cognitive and behavioral phenotypes, high-frequency assessments, and medical imaging. Although these data are incredibly complex, they can potentially be used to understand multi-determinant causal relationships, elucidate modifiable factors, and ultimately customize treatments based on individual parameters. Especially for neurodegenerative diseases, where an effective therapeutic agent has yet to be discovered, there remains a critical need for an interdisciplinary perspective on data and information management due to the number of unanswered questions. Biomedical informatics is a multidisciplinary field that falls at the intersection of information technology, computer and data science, engineering, and healthcare that will be instrumental for uncovering novel insights into neurodegenerative disease research, including both causal relationships and therapeutic targets and maximizing the utility of both clinical and research data. The present study aims to provide a brief overview of biomedical informatics and how clinical data applications such as clinical decision support tools can be developed to derive new knowledge from the wealth of available data to advance clinical care and scientific research of neurodegenerative diseases in the era of precision medicine

Bioinformatics advances in saliva diagnostics

There is a need recognized by the National Institute of Dental & Craniofacial Research and the National Cancer Institute to advance basic, translational and clinical saliva research. The goal of the Salivaomics Knowledge Base (SKB) is to create a data management system and web resource constructed to support human salivaomics research. To maximize the utility of the SKB for retrieval, integration and analysis of data, we have developed the Saliva Ontology and SDxMart. This article reviews the informatics advances in saliva diagnostics made possible by the Saliva Ontology and SDxMart

Evidence in Practice – A Pilot Study Leveraging Companion Animal and Equine Health Data from Primary Care Veterinary Clinics in New Zealand

Veterinary practitioners have extensive knowledge of animal health from their day-to-day observations of clinical patients. There have been several recent initiatives to capture these data from electronic medical records for use in national surveillance systems and clinical research. In response, an approach to surveillance has been evolving that leverages existing computerized veterinary practice management systems to capture animal health data recorded by veterinarians. Work in the United Kingdom within the VetCompass program utilizes routinely recorded clinical data with the addition of further standardized fields. The current study describes a prototype system that was developed based on this approach. In a 4-week pilot study in New Zealand, clinical data on presentation reasons and diagnoses from a total of 344 patient consults were extracted from two veterinary clinics into a dedicated database and analyzed at the population level. New Zealand companion animal and equine veterinary practitioners were engaged to test the feasibility of this national practice-based health information and data system. Strategies to ensure continued engagement and submission of quality data by participating veterinarians were identified, as were important considerations for transitioning the pilot program to a sustainable large-scale and multi-species surveillance system that has the capacity to securely manage big data. The results further emphasized the need for a high degree of usability and smart interface design to make such a system work effectively in practice. The geospatial integration of data from multiple clinical practices into a common operating picture can be used to establish the baseline incidence of disease in New Zealand companion animal and equine populations, detect unusual trends that may indicate an emerging disease threat or welfare issue, improve the management of endemic and exotic infectious diseases, and support research activities. This pilot project is an important step toward developing a national surveillance system for companion animals and equines that moves beyond emerging infectious disease detection to provide important animal health information that can be used by a wide range of stakeholder groups, including participating veterinary practices

Implementation of a Management Registry for Storing Clinical Data in a Research Centre

In clinical research, there is great concern about the storage and veracity of electronic data to ensure the accuracy of information. Objective: To implement a management registry for storing study data in the cardiovascular area, conducted in a clinical research centre. Methods: This is a retrospective registry and prospective joint study. An electronic database was developed using REDCap software. Data elements were standardised in accordance with the American College of Cardiology Foundation and American Heart Association. Data were extracted from research participants from the clinical studies conducted in our Institution with records of cardiovascular diagnosis that were monitored by the health team from 2009 to 2015. Results: The registry was composed of eight sections: demographic variables, diagnostic tests, laboratory tests, cardiovascular risk factors (CV), comorbidities and pharmacological treatment used, and outcome of patients. Each session consisted of sub-items, totalling 113 variables. Phase III (57.8%) and phase IV (36.8%) studies with mean follow-up of 2+4 years were predominant. We used data from 490 participants randomised to 25 studies, 63 percent men, aged 63 ą 10 years, hypertensive (81.4%), with dyslipidaemia (56.5%), and diabetes 48 (36.3%). Most had previous myocardial infarction (72.7%) and underwent coronary angioplasty (87.2%). Conclusion: The implementation of an electronic database of research on participants with cardiovascular disease was applicable and reproducible in clinical practice, being a low cost and very useful tool to store and share data from multicentre studies of medium and large scale

CRUK Basic and Clinical Research DMP Assessment Rubric v2.0

This rubric is intended to assist in the assessment of CRUK Basic and Clinical Research data management plans, against the criteria required by the funder. It is not intended to be used as a template for researchers to follow when writing a data management plan. The rubric has been divided into 'performance criteria' (on the left hand side) which cover the information the funder expects to be covered by the data management plan. Each performance criteria is followed by three descriptions of how it might be addressed, each indicating a different level of response. The descriptions are intended as examples of how the performance criteria might be addressed and are not considered to be exhaustive. The rubric also lists the documents and resources on which it was based

Supporting UK-wide e-clinical trials and studies

As clinical trials and epidemiological studies become increasingly large, covering wider (national) geographical areas and involving ever broader populations, the need to provide an information management infrastructure that can support such endeavours is essential. A wealth of clinical data now exists at varying levels of care (primary care, secondary care, etc.). Simple, secure access to such data would greatly benefit the key processes involved in clinical trials and epidemiological studies: patient recruitment, data collection and study management. The Grid paradigm provides one model for seamless access to such data and support of these processes. The VOTES project (Virtual Organisations for Trials and Epidemiological Studies) is a collaboration between several UK institutions to implement a generic framework that effectively leverages the available health-care information across the UK to support more efficient gathering and processing of trial information. The structure of the information available in the health-care domain in the UK itself varies broadly in-line with the national boundaries of the constituent states (England, Scotland, Wales and Northern Ireland). Technologies must address these political boundaries and the impact these boundaries have in terms of for example, information governance, policies, and of course large-scale heterogeneous distribution of the data sets themselves. This paper outlines the methodology in implementing the framework between three specific data sources that serve as useful case studies: Scottish data from the Scottish Care Information (SCI) Store data repository, data on the General Practice Research Database (GPRD) diabetes trial at Imperial College London, and benign prostate hypoplasia (BPH) data from the University of Nottingham. The design, implementation and wider research issues are discussed along with the technological challenges encountered in the project in the application of Grid technologies