Search PubMed⌕ Search

PubMed · 9268852

Developing a radiology data base for quality assurance.

Abstract

Radiology Information Systems (RIS) are designed to capture and manage the data associated with ordering, executing, reporting, and billing x-ray procedures. The HELP Hospital Information System contains a radiology subsystem that supports these functions. In an effort to enhance quality assurance initiatives, we have created a supplemental data base. This data base contains not only the data traditionally generated by RISs but also data from the hospital system that is relevant to quality assurance. One of the goals associated with this data base is to use techniques from the discipline of Continuous Quality Improvement (CQI) in the radiology department. A focus of our initial efforts has been the time necessary to provide x-ray reports to ordering physicians once the imaging examination has been performed. Efforts to manage the portion of this time interval caused by transcription have resulted in a substantial decrease in the time required for this function. A second goal of this project is to evaluate the quality of x-ray ordering. This objective requires a computerized record of the outcome of the x-ray procedure. Initial analysis of data derived from this data base indicates significant differences in the ordering behavior for computed tomography (CT) examinations among a test group of physicians. A third goal is to do quality assurance on x-ray reports. Experience with pilot systems has shown promising results using a mathematical model of report quality. We hope to leverage these techniques and this quality assurance data base to define a COI process for medical reports in general and for x-ray reports in particular.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

P J Haug, M Farrell, J Frear, D Blatter, P R Frederick. 1997. Developing a radiology data base for quality assurance.. https://doi.org/10.1007/bf03168670

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PageMan: an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments.

BACKGROUND: Microarray technology has become a widely accepted and standardized tool in biology. The first microarray data analysis programs were developed to support pair-wise comparison. However, as microarray experiments have become more routine, large scale experiments have become more common, which investigate multiple time points or sets of mutants or transgenics. To extract biological information from such high-throughput expression data, it is necessary to develop efficient analytical platforms, which combine manually curated gene ontologies with efficient visualization and navigation tools. Currently, most tools focus on a few limited biological aspects, rather than offering a holistic, integrated analysis. RESULTS: Here we introduce PageMan, a multiplatform, user-friendly, and stand-alone software tool that annotates, investigates, and condenses high-throughput microarray data in the context of functional ontologies. It includes a GUI tool to transform different ontologies into a suitable format, enabling the user to compare and choose between different ontologies. It is equipped with several statistical modules for data analysis, including over-representation analysis and Wilcoxon statistical testing. Results are exported in a graphical format for direct use, or for further editing in graphics programs.PageMan provides a fast overview of single treatments, allows genome-level responses to be compared across several microarray experiments covering, for example, stress responses at multiple time points. This aids in searching for trait-specific changes in pathways using mutants or transgenics, analyzing development time-courses, and comparison between species. In a case study, we analyze the results of publicly available microarrays of multiple cold stress experiments using PageMan, and compare the results to a previously published meta-analysis.PageMan offers a complete user's guide, a web-based over-representation analysis as well as a tutorial, and is freely available at http://mapman.mpimp-golm.mpg.de/pageman/. CONCLUSION: PageMan allows multiple microarray experiments to be efficiently condensed into a single page graphical display. The flexible interface allows data to be quickly and easily visualized, facilitating comparisons within experiments and to published experiments, thus enabling researchers to gain a rapid overview of the biological responses in the experiments.

Database Management Systems↗

TaxMan: a taxonomic database manager.

BACKGROUND: Phylogenetic analysis of large, multiple-gene datasets, assembled from public sequence databases, is rapidly becoming a popular way to approach difficult phylogenetic problems. Supermatrices (concatenated multiple sequence alignments of multiple genes) can yield more phylogenetic signal than individual genes. However, manually assembling such datasets for a large taxonomic group is time-consuming and error-prone. Additionally, sequence curation, alignment and assessment of the results of phylogenetic analysis are made particularly difficult by the potential for a given gene in a given species to be unrepresented, or to be represented by multiple or partial sequences. We have developed a software package, TaxMan, that largely automates the processes of sequence acquisition, consensus building, alignment and taxon selection to facilitate this type of phylogenetic study. RESULTS: TaxMan uses freely available tools to allow rapid assembly, storage and analysis of large, aligned DNA and protein sequence datasets for user-defined sets of species and genes. The user provides GenBank format files and a list of gene names and synonyms for the loci to analyse. Sequences are extracted from the GenBank files on the basis of annotation and sequence similarity. Consensus sequences are built automatically. Alignment is carried out (where possible, at the protein level) and aligned sequences are stored in a database. TaxMan can automatically determine the best subset of taxa to examine phylogeny at a given taxonomic level. By using the stored aligned sequences, large concatenated multiple sequence alignments can be generated rapidly for a subset and output in analysis-ready file formats. Trees resulting from phylogenetic analysis can be stored and compared with a reference taxonomy. CONCLUSION: TaxMan allows rapid automated assembly of a multigene datasets of aligned sequences for large taxonomic groups. By extracting sequences on the basis of both annotation and BLAST similarity, it ensures that all available sequence data can be brought to bear on a phylogenetic problem, but remains fast enough to cope with many thousands of records. By automatically assisting in the selection of the best subset of taxa to address a particular phylogenetic problem, TaxMan greatly speeds up the process of generating multiple sequence alignments for phylogenetic analysis. Our results indicate that an automated phylogenetic workbench can be a useful tool when correctly guided by user knowledge.

Database Management Systems↗