Toward a cognitive taxonomy of medical errors.

One critical step in addressing and resolving the problems associated with human errors is the development of a cognitive taxonomy of such errors. In the case of errors, such a taxonomy may be developed (1) to categorize all types of errors along cognitive dimensions, (2) to associate each type of error with a specific underlying cognitive mechanism, (3) to explain why, and even predict when and where, a specific error will occur, and (4) to generate intervention strategies for each type of error. Based on Reason's (1992) definition of human errors and Norman's (1986) cognitive theory of human action, we have developed a preliminary action-based cognitive taxonomy of errors that largely satisfies these four criteria in the domain of medicine. We discuss initial steps for applying this taxonomy to develop an online medical error reporting system that not only categorizes errors but also identifies problems and generates solutions.

Evaluating a medical error taxonomy

Healthcare has been slow in using human factors principles to reduce medical errors. The Center for Devices and Radiological Health (CDRH) recognizes that a lack of attention to human factors during product development may lead to errors that have the potential for patient injury, or even death. In response to the need for reducing medication errors, the National Coordinating Council for Medication Errors Reporting and Prevention (NCC MERP) released the NCC MERP taxonomy that provides a standard language for reporting medication errors. This project maps the NCC MERP taxonomy of medication error to MedWatch medical errors involving infusion pumps. Of particular interest are human factors associated with medical device errors. The NCC MERP taxonomy of medication errors is limited in mapping information from MEDWATCH because of the focus on the medical device and the format of reporting.

Taxonomy development and knowledge representation of nurses' personal cognitive artifacts.

Nurses prepare knowledge representations, or summaries of patient clinical data, each shift. These knowledge representations serve multiple purposes, including support of working memory, workload organization and prioritization, critical thinking, and reflection. This summary is integral to internal knowledge representations, working memory, and decision-making. Study of this nurse knowledge representation resulted in development of a taxonomy of knowledge representations necessary to nursing practice.This paper describes the methods used to elicit the knowledge representations and structures necessary for the work of clinical nurses, described the development of a taxonomy of this knowledge representation, and discusses translation of this methodology to the cognitive artifacts of other disciplines. Understanding the development and purpose of practitioner's knowledge representations provides important direction to informaticists seeking to create information technology alternatives. The outcome of this paper is to suggest a process template for transition of cognitive artifacts to an information system.

Functional taxonomy of bacterial hyperstructures.

The levels of organization that exist in bacteria extend from macromolecules to populations. Evidence that there is also a level of organization intermediate between the macromolecule and the bacterial cell is accumulating. This is the level of hyperstructures. Here, we review a variety of spatially extended structures, complexes, and assemblies that might be termed hyperstructures. These include ribosomal or "nucleolar" hyperstructures; transertion hyperstructures; putative phosphotransferase system and glycolytic hyperstructures; chemosignaling and flagellar hyperstructures; DNA repair hyperstructures; cytoskeletal hyperstructures based on EF-Tu, FtsZ, and MreB; and cell cycle hyperstructures responsible for DNA replication, sequestration of newly replicated origins, segregation, compaction, and division. We propose principles for classifying these hyperstructures and finally illustrate how thinking in terms of hyperstructures may lead to a different vision of the bacterial cell.

Taxonomy of nursing clinical credibility as described by nurses and physicians

Introduction: Nursing clinical credibility, a complex, abstract concept is rarely mentioned in the clinical setting, but is implicitly understood by nurses and physicians. The concept has neither been defined nor explored, despite its repeated use in literature. A review of the extant literature formed the basis for a concept analysis of nursing clinical credibility, which is currently under review for publication. ^ Methods: Using taxonomic analysis, findings of a descriptive qualitative research study in which registered nurses and physicians identified attributes of nursing clinical credibility as it applied to nurses in direct care roles in a hospital setting, formed the basis for development of taxonomies of nursing clinical credibility. A secondary review of literature was undertaken to verify congruence of the taxonomic domains with the work of previous researchers who studied credibility and source credibility. ^ Results: Three taxonomies of nursing clinical credibility emerged from the taxonomic analysis. Using an inductive approach, two separate taxonomies of nursing clinical credibility emerged; one was developed from the descriptions of nursing clinical credibility by registered nurses, and the other from physicians' descriptions of nursing clinical credibility. A third and final taxonomy reflects commonalities within both taxonomies. Three domains were consistent for both nurses and physicians: trustworthiness, expertise, and caring. The two disciplines differed in categories and emphases within the domains; however, both disciplines focused on the attributes of trustworthiness and caring, although physicians and nurses differed on components of expertise. ^ Discussion: Findings from this study of nursing clinical credibility concur with the work of previous researchers who identified trustworthiness and expertise as attributes of credibility and source credibility. Findings suggest however, that trustworthiness and expertise alone are not sufficient attributes of nursing clinical credibility. Caring emerged as an essential domain of nursing clinical credibility according to both nurses and physicians. ^ Products: Products of this research include a concept analysis, two discipline-specific taxonomies of nursing clinical credibility, a third final taxonomy, and a monograph that describes the development of the final taxonomy of nursing clinical credibility. ^

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.