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While the secondary use of medical data has gained attention, its adoption has been constrained due to protection of patient privacy. Making medical data secure by de-identification can be problematic, especially when the data concerns rare diseases. We require rigorous security management measures.

The objective of this investigation is to evaluate binary prediction methods for predicting disease status using high-dimensional genomic data. The central hypothesis is that the Bayesian network (BN)-based method called efficient Bayesian multivariate classifier (EBMC) will do well at this task because EBMC builds on BN-based methods that have performed well at learning epistatic interactions.

Increasing the adoption of electronic health records (EHRs) with integrated clinical decision support (CDS) is a key initiative of the current US healthcare administration. High over-ride rates of CDS alerts strongly limit these potential benefits. As a result, EHR designers aspire to improve alert design to achieve better acceptance rates. In this study, we evaluated drug-drug interaction (DDI) alerts generated in EHRs and compared them for compliance with human factors [...]

Administration of human subject research is complex, involving not only the institutional review board but also many other regulatory and compliance entities within a research enterprise. Its efficiency has a direct and substantial impact on the conduct and management of clinical research. In this paper, we report on the Clinical Research Administration (CLARA) platform developed at the University of Arkansas for Medical Sciences. CLARA is a comprehensive web-based system that [...]

To evaluate attitudes regarding privacy of genomic data in a sample of patients with breast cancer.

This paper describes Health Level 7 (HL7) V.3 Care Transfer, Care Record Query, and Care Record messages. This is the core of the Care Provision Domain in the HL7 standard which became normative at the end of 2012 and is an American National Standards Institute (ANSI)-approved HL7 standard.

Evidence indicates that users incur significant physical and cognitive costs in the use of order sets, a core feature of computerized provider order entry systems. This paper develops data-driven approaches for automating the construction of order sets that match closely with user preferences and workflow while minimizing physical and cognitive workload.

Clinical decision support has the potential to improve prevention of venous thromboembolism (VTE). The purpose of this prospective study was to analyze the effect of electronic reminders on thromboprophylaxis rates in wards to which patients were admitted and transferred. The latter was of particular interest since patient handoffs are considered to be critical safety issues.

To apply human factors engineering principles to improve alert interface design. We hypothesized that incorporating human factors principles into alerts would improve usability, reduce workload for prescribers, and reduce prescribing errors.