Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: Leveraging a clinical research information system to assist biospecimen data and workflow management: a hybrid approach. | Nadkarni et al. Journal of Clinical Bioinformatics 2011 1 22 http content 1 1 22 JOURNAL OF CLINICAL BIOINFORMATICS DATABASE Open Access Leveraging a clinical research information system to assist biospecimen data and workflow management a hybrid approach Prakash M Nadkarni 1 Rowena Kemp1 and Chirag R Parikh1 2 Abstract Background Large multi-center clinical studies often involve the collection and analysis of biological samples. It is necessary to ensure timely complete and accurate recording of analytical results and associated phenotypic and clinical information. The TRIBE-AKI Consortium http tribeaki supports a network of multiple related studies and sample biorepository thus allowing researchers to take advantage of a larger specimen collection than they might have at an individual institution. Description We describe a biospecimen data management system BDMS that supports TRIBE-AKI and is intended for multi-center collaborative clinical studies that involve shipment of biospecimens between sites. This system works in conjunction with a clinical research information system CRIS that stores the clinical data associated with the biospecimens along with other patient-related parameters. Inter-operation between the two systems is mediated by an interactively invoked suite of Web Services as well as by batch code. We discuss various challenges involved in integration. Conclusions Our experience indicates that an approach that emphasizes inter-operability is reasonably optimal in allowing each system to be utilized for the tasks for which it is best suited. Keywords Biospecimen data management clinical research information systems multi-center clinical studies biorepositories 1 Background Research to improve health care is increasingly supported by advances in genomics proteomics and metabolomics. To allow statistically meaningful analyses all of these methodologies demand large numbers of adequately collected and annotated .
