Kenneth W. Scully

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Clinical repositories containing large amounts of biological, clinical, and administrative data are increasingly becoming available as health care systems integrate patient information for research and utilization objectives. To investigate the potential value of searching these databases for novel insights, we applied a new data mining approach,(More)
As a result of increased attention to medical errors, many institutions are contemplating increased use of information technology and clinical decision support. We conducted a retrospective analysis to estimate the frequency and cost of adverse drug events (ADEs) for inpatients at the University of Virginia. Applying published criteria for the detection of(More)
Large-scale data integration efforts to support clinical and biologic research are greatly facilitated by the adoption of standards for the representation and exchange of data. As part of a larger project to design the necessary architecture for multi-institutional sharing of disparate biomedical data, we explored the potential of the HL7 Reference(More)
We describe the development of a clinical data repository whose core consists of four years of inpatient administrative and billing data from the mainframe legacy systems of the University of Virginia Health System (UVAHS). To these data we have linked a cardiac surgery clinical database and our physician billing data (inpatient and outpatient). Other(More)
Multiple factors are driving residency programs to explicitly address practice-based learning and improvement (PBLI), yet few information systems exist to facilitate such training. We developed, implemented, and evaluated a Web-based tool that provides Internal Medicine residents at the University of Virginia Health System with population-based reports(More)
We developed Systems and Practice Analysis for Resident Competencies (SPARC), a Web-based tool to support teaching the practice-based learning and improvement (PBLI) ACGME competencies. SPARC allows Department of Medicine residents to explore de-identified, population-based data about their patient panels with peer comparisons. Data primarily comes from an(More)