Electronic medical record: research tool for pancreatic cancer?

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.

Distributed Health Outcome Monitoring and Evaluation Using i2b2.

There is a pressing need for better tools to support comparative effectiveness research (CER) on a national scale. In addition, little is known about within-class outcome disparities for commonly used cardiovascular and diabetes medications. In this presentation, we will describe our experience implementing a new i2b2 cell, the Health Outcome Monitoring and Evaluation Cell (HOME), at 5 collaborating Clinical Translational Science Award sites (CTSAs) in the U.S. We will also describe the motivations to developing a common query framework, and findings related to the implementation and use of the HOME cell, to perform distributed CER queries. Our focus is on the assessment of race, gender, and location-based disparities in outcomes for patients treated with similar mediations for hypertension, dyslipidemias, and diabetes.

Enabling Hierarchical View of RxNorm with NDF-RT Drug Classes.

NDF-RT is the proposed source of drug classification information. We set out to construct a hierarchy of NDF-RT drug classes and RxNorm medications and evaluate it on medication records data. NDF-RT and RxNorm are distributed in different file formats, require different tools to manipulate and linking the two into a hierarchy is a non-trivial exercise. Medication data in RxNorm from two institutions was constrained by the hierarchy. Only 37% of records from one and 65% from another institution were accessible. We subsequently enriched the RxNorm mapping in NDF-RT by exploiting relationships between concepts for branded and generic drugs. Coverage improved dramatically to 93% for both institutions. To improve usability of the resulting hierarchy, we grouped clinical drugs by corresponding clinical drug form.