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At least 379 records · Page 21Linked to original sources
PubMed1976

Computerization in microbiology.

Computerization of the clinical microbiology laboratory is finally coming of age. Operations and functions easily adapted from other clinical laboratories have already been implemented in the microbiology laboratory, e.g., fiscal, clerical, and other administrative housekeeping chores. Similarly, storage, retrieval, and analysis of banks of data easily collected and filed have also been successfully accomplished in microbiology for several years. The more challenging problems of computerization still deserve and require the attention of microbiologists. These problems lie in the smooth sequential formulation and transmission of clinical microbiological test results from the laboratory to clinical records; the manipulation of relevant data by the computer prospectively for the detection and prediction of nosocomial infections or miniepidemics; and the development of programed lessons and examinations for computerized instruction, retraining, and examination of technologists and other individuals interested in microbiology.

Clinical Laboratory Techniques↗
PubMed1994

Unknown primary carcinoma: natural history and prognostic factors in 657 consecutive patients.

PURPOSE: To evaluate the natural history, validate previous observations, and identify prognostic factors in patients with unknown primary carcinoma (UPC). PATIENTS AND METHODS: Nine hundred twenty-seven consecutive patients referred to the M.D. Anderson Cancer Center with a preliminary diagnosis of UPC were prospectively identified. A standardized evaluation narrowed the study population to 657 patients with UPC. All data were recorded and computerized for storage, retrieval, and analysis. The primary end point for the study was survival, which was calculated from the first day of patient registration. Survival curves were estimated using the Kaplan-Meier method and compared using the Cox-Mantel log-rank test. To identify important prognostic factors, univariate and multivariate analyses were conducted. RESULTS: The demographics of the UPC patient population mirrored those of the general population of patients referred to our cancer center except for an excess of men among the UPC patients. Most patients had histologic or cytologic evidence of adenocarcinoma and had more than one organ site metastatically involved. Univariate and multivariate analyses identified numerous important prognostic factors with a significant influence on survival, including sex, number of organ sites involved, specific organ sites involved, and pathologic subtypes. CONCLUSION: This study validated previously identified important prognostic factors for survival in UPC. Additional variables that had an impact on survival were identified and the complex interaction of the factors was explored. As patient numbers increase, this database will be able to provide further analyses of patient subsets and potentially relate specific clinical features to the evolving molecular and biochemical understanding of these malignancies.

Adenocarcinoma↗
PubMed1995

Technical note: multimedia clinical records: results of a pilot project at the Radiation Therapy Department of Florence.

A system based on Macintosh, Hypercard and a local network was developed at the University and Hospital Department of Radiation Therapy, Florence, Italy, to manage the clinical records as a complex data structure (data, texts, drawings and image storage and retrieval). After 4 years, the system produces over 2000 new charts a year and manages more than 15,000 clinical records. Electronic records are produced, printed and updated in their traditional form to be put on file in our archive. On-line consultation of the clinical records is possible from every workstation of the structure even as it is producing, updating, or printing a chart. Physicians and clinical clerks, with different access privileges, currently use the system for all these purposes. The clinical records are typed by secretaries who receive dictation on microcassettes. No extra staff were necessary to set up and manage the system and training was simple. The new system changed neither the organization and structure of the traditional records, nor the flux of information; new tools (computers, printers and a network) were introduced to manage the information and the charts. The author describes the aims of the original project and the results.

Humans↗
PubMed2004

Data quality management in pharmacovigilance.

Pharmacovigilance relies on information gathered from the collection of individual case safety reports and other pharmacoepidemiological data. Even given the inherent limitations of spontaneous reports, the usefulness of this data source can be improved with good data quality management. Although under-reporting cannot be remedied this way, the negative impact of incomplete reports, which is another serious problem in pharmacovigilance, can be reduced. Quality management consists of quality planning, quality control, quality assurance and quality improvements. The pharmacovigilance data processing cycle starts with data collection and, in computerised systems, data entry; the next step is data storage and maintenance; followed by data selection, retrieval and manipulation. The resulting data output is analysed and assessed. Finally, conclusions are drawn and decisions made. The increased knowledge feeds back into the data processing cycle. Focussing on the first three steps of the data processing cycle, the different quality dimensions associated with these steps are described in this review, together with examples relevant to pharmacovigilance data. Functioning, well documented, and transparent quality management systems will benefit not only those involved in data collection, management and output production, but, ultimately, also the pharmacovigilance end users, the patients.

Adverse Drug Reaction Reporting Systems↗
PubMedDate not supplied

On epidemiology and geographic information systems: a review and discussion of future directions.

Geographic information systems are powerful automated systems for the capture, storage, retrieval, analysis, and display of spatial data. While the systems have been in development for more than 20 years, recent software has made them substantially easier to use for those outside the field. The systems offer new and expanding opportunities for epidemiology because they allow an informed user to choose between options when geographic distributions are part of the problem. Even when used minimally, these systems allow a spatial perspective on disease. Used to their optimum level, as tools for analysis and decision making, they are indeed a new information management vehicle with a rich potential for public health and epidemiology.

Computers↗
PubMed1999

Inventory management and reagent supply for automated chemistry.

Developments in automated chemistry have kept pace with developments in HTS such that hundreds of thousands of new compounds can be rapidly synthesized in the belief that the greater the number and diversity of compounds that can be screened, the more successful HTS will be. The increasing use of automation for Multiple Parallel Synthesis (MPS) and the move to automated combinatorial library production is placing an overwhelming burden on the management of reagents. Although automation has improved the efficiency of the processes involved in compound synthesis, the bottleneck has shifted to ordering, collating and preparing reagents for automated chemistry resulting in loss of time, materials and momentum. Major efficiencies have already been made in the area of compound management for high throughput screening. Most of these efficiencies have been achieved with sophisticated library management systems using advanced engineering and data handling for the storage, tracking and retrieval of millions of compounds. The Automation Partnership has already provided many of the top pharmaceutical companies with modular automated storage, preparation and retrieval systems to manage compound libraries for high throughput screening. This article describes how these systems may be implemented to solve the specific problems of inventory management and reagent supply for automated chemistry.

Automation↗
PubMed1988

[Biomathematical approaches to the problems of radiation biology and medicine].

A vast experience in the introduction of modern mathematical methods into research activities of the Central Research Roentgenoradiology Institute was accumulated in the Department of Biomathematics. The Information-retrieval System for Storage and Analysis of Data on the Activities of X-ray Service in the USSR, a package of applied programs for statistical analysis of radiation therapy effectiveness, a controlled data base on 6 cancer sites, 2 systems for simulation of radiation effects on heterogeneous cell systems were worked out. Some priority results in the fields of mathematical biology and statistical analysis of incomplete selective data were obtained. The Department is heading the work on the problem "Mathematics and computer technology in radiobiology and radiation biophysics".

Humans↗
PubMedDate not supplied

The classification of ultrastructural topography in the context of an ultrastructural diagnostic service.

Ultrastructural diagnosis relies on the recognition of specific organelles and the identification of various subcellular features and relationships. As E.M. case loads increase, the recall and comparison of particular cases becomes increasingly difficult. A system of classification is proposed, in a format compatible with SNOP and SNOMED, which permits the precise coding of subcellular details. Such a system could assist in the classification of disease, the storage and analysis of data and the retrieval and study of case material on an inter-departmental basis.

Anatomy↗
PubMed1982

FILELB: a data file structure for the analysis of physiological systems.

FILELB is a comprehensive library of subroutines for the storage, retrieval, location, inspection and entry of sequences of experimental and analytical data. The general purpose, highly organised data file structure implemented by FILELB is the core of our interactive computer package for the analysis of physiological systems.

Data Display↗