Clinical Data Cohort Quality Improvement: The Case of the Medication Data in The University of Minnesota's Clinical Data Repository.

Clinical and translational research centers (CTRCs) have emerged as key centers for electronic medical record related research through integrated data repositories (IDRs) and the 'secondary use' of clinical data. Researchers accessing and pre-processing ever increasing amounts of electronic medical records for data mining tasks have a growing need for best practice approaches for clinical data quality assessment and improvement. This project focused on a large data extract for 7 statin medication prescriptions for patients with cardiovascular disease. After the initial data extraction, we proceeded to analyze the data for completeness, correctness, currency, and percentage populated using established data quality frameworks. Assessment of the said data was performed through medication possession ratios, medication discontinuation reasons, and drug dosages. When we compared distributions of data elements such as drug dosage before and after changes were introduced by our pre-processing protocols, only a minimal noticeable difference was found as the clinical data cohort quality assessment and pre-processing were completed without substantially altering the original data structure. Our study demonstrated practical steps for clinical data cohort quality improvement using medication data and illustrates a best practice approach in clinical data cohort quality improvement for any data mining tasks.

Clinical Data Repositories: Primary Sources of Drug-to-Drug Interaction Detection and Risk Assessment.

Background: Polypharmacy can be a source of adverse drug events including those caused by drug to drug interaction (DDI) exposures. Web-based DDI databases are available to researchers for the identification of potential DDI exposures. Rather than relying on potentially incomplete DDI databases, large clinical data repositories (CDR) which are integrated data sources fed with millions of heterogeneous electronic health records (EHRs) containing real-world data should be leveraged for data driven DDI identification. Objective: To explore and validate the viability of clinical data repositories as data driven resources for clinically important adverse drug events detection and surveillance. Methods: This work leverages a minimum clinical data set from the University of Minnesota's CDR to identify drugs that have statin to drug interaction (SDI) potential and compares the findings with results of web based DDI databases. Using an SDI identification matrix, we identified several potential novel SDI drugs that were not mentioned in the web-based sources but explored through our study as drugs with SDI potential. Results: Drugs flagged by our SDI identification matrix but not mentioned in the web-based sources include Lysine, Ketotifen, Latanoprost, Methylcellulose, Oxazepam, Linseed Oil, and others. Conclusion: Our findings identified potential gaps regarding the completeness, currency, and overall reliability of open source and commercial DDI databases. CDRs can be a primary source for identifying drug to drug interactions. Keywords: clinical data repository, drug to drug interaction databases, drug to drug interaction, statin to drug interaction, polypharmacy, statin to drug interaction identification matrix, adverse drug event, statin.

The Evaluation of Clinical Classifications Software Using the National Inpatient Sample Database.

The Clinical Classifications Software (CCS), by grouping International Classification of Diseases (ICD), provides the capacity to better account for clinical conditions for payers, policy makers, and researchers to analyze outcomes, costs, and utilization. There is a critical need for additional research on application of CCS categories to validate the clinical condition representation and to prevent gaps in research. This study compared the event frequency and ICD codes of CCS categories with significant changes from the first three quarters of 2015 to 2016 using National Inpatient Sample data. A total of 63 of the 285 diagnostics CCS were identified with greater than 20% change, of which 32 had increased and 31 decreased over time. Due to the complexity associated with the transition from ICD-9 to ICD-10, more studies are needed to identify the reason for the changes to improve CCS use for ICD-10 and its comparability with ICD-9 based data.