Summary: : Open access to raw clinical and molecular data related to immunological studies has created a tremendous opportunity for data-driven science. We have developed RImmPort that prepares NIAID-funded research study datasets in ImmPort (immport.org) for analysis in R. RImmPort comprises of three main components: (i) a specification of R classes that encapsulate study data, (ii) foundational methods to load data of a specific study and (iii) generic methods to slice and dice data across different dimensions in one or more studies. Furthermore, RImmPort supports open formalisms, such as CDISC standards on the open source bioinformatics platform Bioconductor, to ensure that ImmPort curated study datasets are seamlessly accessible and ready for analysis, thus enabling innovative bioinformatics research in immunology. Availability and Implementation: RImmPort is available as part of Bioconductor (bioconductor.org/packages/RImmPort). Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Most biomedical research databases contain considerable amounts of time-oriented data. However, temporal knowledge about the contextual meaning of such data is not usually represented in a principled fashion. As a result, investigators often develop custom techniques for temporal data analysis that are difficult to reuse. We addressed this problem by developing a set of knowledge-driven methods and tools for temporally representing and querying biomedical data, and have integrated them using a mediator approach. A central issue driving our work is a need to integrate temporal representations of data in relational databases with the domain-specific semantics of temporal patterns used in querying. This paper presents a formal temporal knowledge model using the semantic Web ontology and rule languages, OWL and SWRL, respectively. The model informs the mediator of the temporal semantics used for data analysis. We show that our approach provides the computational foundation for much-needed software to make sense of complex temporal patterns in two biomedical research domains

Clinical trials are formal patient studies that serve as the gold standard method for establishing the efficacy of medical treatments. There is an enormous requirement to specify the knowledge used at all stages of clinical trials, including planning, implementation, and analysis, so that they can be faithfully executed by a clinical research organization. We are building Epoch, a knowledge-based system, to help a large research consortium, the immune tolerance network (ITN), undertake trials to advance new therapeutics in immune-mediated disorders. We are currently targeting two application areas essential to the successful implementation of a trial: (1) tracking study participants as they advance through a study, and (2) tracking biological specimens as they are processed at laboratories. In this paper, we discuss our use of the Web ontology language (OWL) to create a suite of ontologies that conceptualize the clinical trial domain and show how they can inform our system. We show that our ontological framework provides a stable, consistent platform to define, maintain, and distribute the knowledge requirements used in clinical trial management

Information technology can support the implementation of clinical research findings in practice settings. Technology can address the quality gap in health care by providing automated decision support to clinicians that integrates guideline knowledge with electronic patient data to present real-time, patient-specific recommendations. However, technical success in implementing decision support systems may not translate directly into system use by clinicians. Successful technology integration into clinical work settings requires explicit attention to the organizational context. We describe the application of a "sociotechnical" approach to integration of ATHENA DSS, a decision support system for the treatment of hypertension, into geographically dispersed primary care clinics. We applied an iterative technical design in response to organizational input and obtained ongoing endorsements of the project by the organization's administrative and clinical leadership. Conscious attention to organizational context at the time of development, deployment, and maintenance of the system was associated with extensive clinician use of the system.

The Institute of Medicine recently issued a landmark report on medical error. 1 In the penumbra of this report, every aspect of health care is subject to new scrutiny regarding patient safety. Informatics technology can support patient safety by correcting problems inherent in older technology; however, new information technology can also contribute to new sources of error. We report here a categorization of possible errors that may arise in deploying a system designed to give guideline-based advice on prescribing drugs, an approach to anticipating these errors in an automated guideline system, and design features to minimize errors and thereby maximize patient safety. Our guideline implementation system, based on the EON architecture, provides a framework for a knowledge base that is sufficiently comprehensive to incorporate safety information, and that is easily reviewed and updated by clinician-experts.