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FAQ
What is a RIMS?
What is Translational Research?
Who needs a RIMS?
What is the impact of RIMS in Translational Research?
Why Buy rather than Build?
How much does Labmatrix™ cost?
Scientific data management takes many forms, from pen and paper to sophisticated and powerful relational databases. Research Information Management Systems fall into the latter category. One of the characteristics of a RIMS is the ability to integrate any type of data, no matter what the source. Whether it is data that is generated from lab tests, response surveys or instrumentation, regardless of the data capture mechanism, a RIMS accommodates the data and stores it in a cross-referential manner with other data collected for a primary subject. Of necessity when considering scientific studies and clinical investigation in particular, is the breadth of data collected. A good RIMS must possess the flexibility to grow with the research pathway to facilitate research planning and decision support throughout the lifecycle of the study or project.
Research that brings discovery from the laboratory bench to practical applications in medicine for the improvement of patient care is called Translational Research.
Intended to translate knowledge derived from laboratory work (basic research) into clinical applications, translational research usually involves clinical investigation with human subjects (patients or normal volunteers) in which knowledge obtained from basic research with genes, cells, or animals is translated into diagnostic or therapeutic interventions that can be applied to the treatment or prevention of disease or frailty.
Advances originating from translational research efforts can be in areas as diverse as molecular diagnostics, drug therapy, medical devices and even drug delivery. The common factor, however, is that multi-disciplinary approaches are usually undertaken so as to leverage expertise combining more than one field. It is important to keep in mind that translational research is usually a cycle, from bench to bedside and back as knowledge obtained through clinical observations is usually cycled back to the bench for further tailoring, for example, reduction of unwanted side effects, or development of diagnostics to target therapies to specific populations identified by pharmacogenomic analyses.
Thanks to burgeoning technologies now available for life science research, especially the high-throughput “omics” applications (genomics, proteomics, metabolomics, transcriptomics, epigenomics, pharmacogenomics, etc), scientists are faced with an ever-increasing data load. The challenge is to sift through the noise to significant findings and convert this data to knowledge. At the end of the day, the scientist needs to answer the following question: “What is the data telling me?”
In a typical research laboratory, scientific data can be scattered. Patient and clinical information may be in one set of spreadsheets, while research information may be in an entirely different area, often divided into projects located on separate computers or handwritten lab notebooks. File cabinets and large binders, often containing handwritten data or lab reports, are still standard methods of information management in the modern research laboratory. Verbal history is often relied upon to find data and samples. In academic settings, the problems are compounded due to the brief two- to five-year tenure of most laboratory staff, fellows and post-docs. Pulling together multi-faceted information about a group of study subjects or samples is traditionally laborious and is a widely recognized problem.
What has also become clear, especially in the process of medical discovery through translational research, is that there is an information gap between basic molecular/genetic studies and clinical research, due to the lack of a central repository that enables query and analysis across all relevant translational research data. In addition, there are impediments to complying with the often contradictory, federal, institutional and granting agency regulatory requirements for both data privacy/security and simultaneously, access by other researchers. Other shortcomings of traditional information management systems are the loss of valuable historical, oftentimes unpublished, data records, and the barrier to real-time data sharing for effective collaboration. All of these factors necessitate implementation of a RIMS.
Consider this scenario:
A breast cancer researcher wants to determine whether there is a correlation between the incidence of breast cancer recurrence and expression of a new gene they have identified which appears to function in DNA repair. Preliminary experiments have shown that overexpression of this gene enhances DS DNA repair and could provide survival advantage to cancer cells. The group has shown that the gene is indeed overexpressed in a specific subset of clinical biopsy tissue samples and want to examine the incidence of breast cancer recurrence, following radiation treatment, in this group of patients. If results of this analysis show that there is indeed a correlation between disease recurrence and expression levels of this new gene, the research team will have a new target for therapeutic intervention in patients possessing this genetic profile. A molecular diagnostic will be developed concomitant with strategies for interfering with the function of this gene.
To do this analysis, data from disparate sources must be integrated and cross-referenced. Patient clinical data is stored in a combination of the electronic medical record and the pathology databases. Gene expression data is stored in a number of MS EXCEL files where the Q-PCR instrument data has been exported and final results analyzed. Another directory holds the image data from the in situ hybridization experiments for the clinical samples. Putting it all together requires the input of the lab and clinical personnel who are the individual project owners, and painstaking sorting through each file to combine only the relevant data from each source, a time-consuming and laborious task, at best.
A RIMS solution would provide the researchers with central data repositories that are cross-referenced by specific key information, such as patient or sample ID numbers, so that scientific questions can be answered quickly and easily. Data could be accessed in a HIPAA-compliant manner so that patient identifiers are masked, and results could be exportable with the touch of a button to downstream applications, such as statistical analysis packages. Interconnectivity to a subset of clinical data in the patient electronic medical record and pathology reports would enhance the ability to integrate it with the bench experimental results. Turnover of lab personnel is less of an issue when all the data has been validated and managed in the RIMS according to standardized procedures and with a shared vocabulary, reducing variability in the data collected and minimizing inefficient data usage.
With Labmatrix, a Scientific Query Builder is built right into the RIMS so that a researcher could either ask questions spontaneously or create saved queries to be loaded as needed for analyses, reports, or export to other applications such as Excel. The breast cancer researcher above, that wanted to analyze disease recurrence rates in a specific sample population with a particular genomic profile, would have an answer in minutes, instead of hours or days. Labmatrix is designed so that any type of study subject or biological sample data could be tracked and any type of data from either clinical observation or bench experiment could be accommodated and integrated with all other relevant data, regardless of the source or study focus. Labmatrix is an example of flexibility and power inherent in a well-designed RIMS.
Labmatrix comes with many different implementation options both in terms of licensing options, add-on functionality, hosting and professional services. For further information about Labmatrix pricing for your specific configuration, please contact BioFortis at 443-276-2464 x 2468 or send an e-mail to info@biofortis.com.
Licensing options:
Named user licenses provide access on demand to a single, named user.
Shared user licenses enable Labmatrix access to multiple users, each maintaining their individual access permissions and restrictions, but concurrent access is limited to the number of shared licenses purchased.
Enterprise / site licenses enable unlimited Labmatrix access for all members of an organization, institute or department.
Optional functionality:
Pedigree viewer licensing is available for automatic, interactive generation of family pedigrees to support inherited disease research. Pedigrees are generated based on familial relationships captured in Labmatrix and can be exported as jpeg or xml.
API developer and runtime licenses are available as options to integrate Labmatrix into existing IT infrastructure or further data generation. API licensing enables access to your data in a manner amenable for data mining, predictive algorithm development, electronic data import & export or other bioinformatic utility.
Data hosting options:
Enterprise installation options are available for customers requiring installation of Labmatrix onsite on client servers.
Professional Services:
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