Data in its many forms is a critical component of effective and productive research. As technology continues to evolve, and the volume and variety of sources of data continue to grow, accessing and leveraging all of this information is an ever increasing challenge. Concurrently, technology and information science is also driving novel ways to analyze, visualize, process and store this increasing amount of data. The ability to take advantage of these growth areas in order to aid the research efforts of the university is a critical need. The challenge to bring all of these various components into a unified resource for the university is a prodigious and multidimensional one. A subsection of the target data streams and sources include primary source clinical data, secondary source research data, clinical trials research data, financial data, genomic data to name a few. These sources reside in multiple SQL databases, HL7 message streams, hospital tracking systems, billing systems, surveys and others. To aid in overcoming this challenge, there is an ambitious effort underway to create a platform that will facilitate the aforementioned goals. The IT department, through the efforts of its Research Computing Services division will be embarking on this leading-edge, collaborative, and much needed data repository. The proposed design of the repository will take the form of a data aggregation layer capable of handling many disparate data feeds and sources, storing data in ways that support multiple access and analysis methods, all while providing researchers with increased tools and visibility.If our ability to manage and learn from this rapid increase of information and technologies grows, then so will our research opportunities. The effect will bring new innovations to the research community here at the university and by extension the community at large.

BACKGROUND: A novel data warehouse based on automated retrieval from an institutional health care information system (HIS) was made available to be compared with a traditional prospectively maintained surgical database. METHODS: A newly established institutional data warehouse at a single-institution academic medical center autopopulated by HIS was queried for International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnosis codes for pancreatic neoplasm. Patients with ICD-9-CM diagnosis codes for pancreatic neoplasm were captured. A parallel query was performed using a prospective database populated by manual entry. Duplicated patients and those unique to either data set were identified. All patients were manually reviewed to determine the accuracy of diagnosis. RESULTS: A total of 1107 patients were identified from the HIS-linked data set with pancreatic neoplasm from 1999-2009. Of these, 254 (22.9%) patients were also captured by the surgical database, whereas 853 (77.1%) patients were only in the HIS-linked data set. Manual review of the HIS-only group demonstrated that 45.0% of patients were without identifiable pancreatic pathology, suggesting erroneous capture, whereas 36.3% of patients were consistent with pancreatic neoplasm and 18.7% with other pancreatic pathology. Of the 394 patients identified by the surgical database, 254 (64.5%) patients were captured by HIS, whereas 140 (35.5%) patients were not. Manual review of patients only captured by the surgical database demonstrated 85.9% with pancreatic neoplasm and 14.1% with other pancreatic pathology. Finally, review of the 254 patient overlap demonstrated that 80.3% of patients had pancreatic neoplasm and 19.7% had other pancreatic pathology. CONCLUSIONS: These results suggest that cautious interpretation of administrative data rely only on ICD-9-CM diagnosis codes and clinical correlation through previously validated mechanisms.