Innovative applications of an enterprise-wide information warehouse.

The Information Warehouse at The Ohio State University Medical Center is a comprehensive effort integrating data from over 70 sources throughout the enterprise. The IW serves a broad diversity of customers in all mission areas of the medical center, from clinical operations and administration to education, to research. This comprehensiveness has facilitated an innovative application of cross-disciplinary technologies and methodologies to problem domains beyond the roles traditionally envisioned for data warehousing.

Data delivery workflow in an academic information warehouse.

The Ohio State University Medical Center (OSUMC) Information Warehouse (IW) collects data from many systems throughout the OSUMC on load cycles ranging from real-time to on-demand. The data then is prepared for delivery to diversity of customers across the clinical, education, and research sectors of the OSUMC. Some of the data collected at the IW include patient management, billing and finance, procedures, medications, lab results, clinical reports, physician order entry, outcomes, demographics, and so on. This data is made available to the users of the IW in variety of formats and methods.

Leveraging an information warehouse to create translational research environment for wound care center.

In order to discover new biomarkers and therapeutic agents for personalized wound care, a vast amount of clinical information is collected and stored at The Ohio State University Medical Center (OSUMC) Comprehensive Wound Center (CWC). The Information Warehouse (IW) group at OSUMC has developed and implemented a comprehensive data collection network and analysis pipeline to support clinical, translational and outcomes research, and cost analyses that can be converted into clinical best practices for wound care.

Data mining tools for genotype-phenotype correlation.

Single Nucleotide Polymorphisms (SNPs) may be the key to diagnosing and treating certain diseases. A preliminary study was conducted at The Ohio State University Medical Center Information Warehouse to correlate such SNPs with a selected group of lab values for cardiology patients. Early results show that data mining tools can be valuable for understanding such correlations, but further refinement of the methodology and data preparation is needed to fully realize such value.

Using data mining tools to discover novel clinical laboratory test batteries.

Using statistical analysis and data mining tools, we examined possible associations among clinical laboratory orders placed at the Ohio State University Medical Center between January and October of 2006. Upon applying the Frequent Itemset data mining technique to this data set, the results indicated that, while the most frequently ordered battery of tests was not associated with others, some highly associated orders may be good candidates to comprise new test batteries.

Using an information warehouse to screen patients for clinical trials: a prototype.

Success of a clinical trial recruitment process for drug discovery and new treatments depends on screening and identifying eligible patients in a timely manner. This can be a complex and tedious process that in many instances requires a research nurse to manually track patients that may be eligible. At the Ohio State University Medical Center (OSUMC) we have developed a web-based functional prototype that uses the data stored in the Information Warehouse (IW) to screen patients that meet the eligibility criteria for clinical trials. Using this prototype, a researcher can apply a set of inclusion/exclusion criteria pertinent to a research protocol and instantly find patients that may qualify for the clinical trial without revealing their identity. A researcher has access to all detailed historical clinical information such as lab results, diagnosis codes, pathology reports and clinical notes.