Informatics and IT are vital components of personalized health care. A key strategy in the OSUMC IT plan is “to automate the patient’s electronic medical record and the workflow surrounding patient care to enhance the quality, efficiency and personalization of patient care.” This includes the migration of the inpatient electronic medical record (EMR) from 2nd generation to 3rd generation, including clinician-driven workflow and decision support. It also includes expansion of the EMR to the ambulatory environment and to the peri-operative and ICU environments. Also planned is extensive collaboration between various departments to integrate genomic and clinical data within the Information Warehouse and the online patient care systems.
OSUMC leverages the Information Warehouse (IW) to develop its personalized health care agenda. Ohio State University Medical Center has a rich history of innovation and achievement in managing information systems and patient care. OSU Medical Center was among the first in the country to adopt and implement a paperless patient record system, and for the past eight years, the Medical Center has been named one of the country’s “Most Wired” hospitals.
The Information Warehouse takes in data from a broad array of medical center information sources (more than can be shown here), cleanses it, arranges it into efficient datamarts, and then provides access to the data through a robust selection of presentation, mining, and analysis tools. Through this integration the IW works to establish a truly translational research environment at OSUMC.
The OSUMC Information Warehouse is one the few nationwide that include billing, financial, and other non-clinical data into a patient’s record, a paradigm that offers unique opportunities for research into such areas as administration, operations, quality analysis, finance, strategic planning and education, as well as both clinical and basic research. Additionally, it is one of the few IS centers that does not simply store data, but actively designs and promotes customized solutions to users’ needs. To extend its services even further, the IW recently hired a translational research informatics architect, who holds a joint appointment in the department of biomedical informatics and who will be the key liaison between the IW and our growing number of translational scientists. Some of the core services offered by OSUMC IW provide the necessary support for a best-in-class bio-repository:
Honest Broker Protocol: This protocol addresses one of the most important aspects of handling personal patient data and permitting legitimate research to proceed without jeopardizing the patient’s privacy. This process, approved by the OSUMC Institutional Review Board (IRB), enables the IW to supply de-identified clinical data for use in preliminary research without requiring the approval of a formalized protocol from each investigator. The protocol essentially authorizes the IW to safeguard patient privacy on behalf of the IRB while enabling researchers to explore new ideas without the high cost in time and effort that often accompany the creation and approval of a rigorous protocol. This enables faster validation of intuitive ideas or high risk hypotheses that could lead to the development of fully realized protocols.
Grid Technology: A major impediment to large-scale integration of databases stems from the fact that they are often incompatible with each other. The IW is working closely with developers in the NCI’s caBIG initiative in using some of its earliest models for data management and analysis. The objective is to enable the integration of data resources from multiple platforms and sources.
Translational Research Portal (TRP): Efficient data collection and handling will play an increasing role in shaping the direction of future clinical-translational research. The TRP is a portal dedicated to the rapid development of web-based applications for collecting, managing, and reporting data in medical research studies with tight integration into the IW clinical repository.
Statistical Tools Server (STS): The integration of large datasets increasingly requires novel statistical tools for successful analysis. Uniquely, the IW has taken steps to incorporate advanced statistical tools that can be customized for individual projects. Invaluable to the efficiency of clinical research run by our faculty, the STS is a high performance system (Xeon class) that supports statistical analysis on large datasets within the IW using either the SAS or R statistical packages.
Text Mining Tools (TMT): IW has made strong advances in overcoming problems in classifying text (e.g., from patient charts) in such a way that it can be efficiently analyzed in digital format. TMT provides more advanced and efficient methods for extracting knowledge from the IW’s extensive and growing repository of over 2 million clinical free-text reports. Employing both word-based and UMLS concept-based indexing, the framework provides a means of knowledge mining text reports in a de-identified manner.
Protein Electrophoresis (PEP) Tools: Proteomics data is another example where a highly promising new technology can only be optimally exploited if efficient data storage and interpretation are readily accessible to clinical investigators. The developed system provides integrated tools for collecting and analyzing 1D urine, serum, and Immunofixation. PEP data from instrumentation in the laboratory and can be accessed from any prepared desktop system in the network. Integration with the IW enables the use of historical results and clinical context during diagnosis.
Microarray and Tissue Data Portal: Novel technologies continue to generate large and complex data sets of increasing size that need to be seen in the context of other medical information. This datamart and portal include an integrated suite of tools supporting tissue banking and tissue analysis. Recently, researchers assessing genetic variation in wound healing used caBIG tools such as caTissue, caWorkbench and caArray, to correlate gene expression analysis with clinical phenotypes.
To locate more information pertaining to the above referenced articles please visit the PubMed database.