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Medical informatics--the state of the art in the Hospital Authority.

Since its inception in 1990, the Hospital Authority (HA) has strongly supported the development and implementation of information systems both to improve the delivery of care and to make better information available to managers. This paper summarizes the progress to date and discusses current and future developments. Following the first two phases of the HA information technology strategy the basic infrastructural elements were laid in place. These included the foundation administrative and financial systems and databases; establishment of a wide area network linking all hospitals and clinics together; laboratory, radiology and pharmacy systems with access to results in the ward. A major push into clinical systems began in 1994 with the clinical management system (CMS), which established a clinical workstation for use in both ward and ambulatory settings. The CMS is now running at all major hospitals, and provides single logon access to almost all the electronically collected clinical data in the HA. The next phase of development is focussed on further support for clinical activities in the CMS. Key elements include the longitudinal electronic patient record (ePR), clinical order entry, generic support for clinical reports, broadening the scope to include allied health and the rehabilitative phase, clinical decision support, an improved clinical documentation framework, sharing of clinical information with other health care providers and a comprehensive data repository for analysis and reporting purposes.

China↗

TSMI: a CEN/TC251 standard for time specific problems in healthcare informatics and telematics.

Time is the most important variable in healthcare, and standards are needed about how to represent information with explicit references to time. In this paper, the European Prestandard 'TSMI: time standards for healthcare specific problems' (CEN/TC251 preENV 12381) is presented which aims to be the first contribution to this harmonisation process, focusing on 'representation' and 'explicit reference' of temporal information in healthcare. The prestandard is mainly composed of two parts. First, the basic building blocks for modelling time-related information are introduced, and a formal representation scheme proposed. In a second part, conformance rules and principles for Healthcare Data and Information as well as for Healthcare Information Systems, are covered.

Artificial Intelligence↗

Informatics and data management in proteomics.

Proteomics has become dominated by large amounts of experimental data and interpreted results. This experimental data cannot be effectively used without understanding the fundamental structure of its information content and representing that information in such a way that knowledge can be extracted from it. This review explores the structure of this information with regard to three fundamental issues: the extraction of relevant information from raw data, the scale of the projects involved and the statistical significance of protein identification results.

Algorithms↗

An integrated instrument control and informatics system for combinatorial materials research.

The use of high-throughput synthesis and characterization techniques is increasingly prevalent in materials science research. We describe the London University Search Instrument, a research apparatus designed for the high-throughput synthesis and characterization of thick-film sample libraries of ceramic compounds. The instrument is constructed largely from commodity components, which pose particular engineering challenges for achieving the automated operation required for efficient high-throughput experimentation. This paper describes the architecture and implementation of the software system that provides integrated instrument control and data management functions.

Journal Article↗

Array of informatics: Applications in modern research.

The advent of microarray technology in the past decade has greatly enhanced gene expression studies and allowed for the acquisition of a vast amount of information simultaneously. Microarrays have been used in numerous scientific fields to identify new genes, to determine the transcriptional activity of cells, and to discover downstream targets of different loci. Recently, DNA microarrays have also been utilized in disease studies to determine outcomes at many levels including diagnosis, prognosis, and drug therapy. The promise of protein microarrays is to allow us to study the molecular interactions of protein, lipids, small molecules, and carbohydrates. They can be exploited to analyze a single protein pair interaction, to address changes in multiple protein levels as a response to treatment (i.e., drug or radiation), or in a pathological condition. Tissue microarrays allow the analysis of numerous tumor samples simultaneously. Finally, live cell-based microarrays provide an opportunity to study the function of the entire proteome en masse within living cells. However, these exciting new areas still have to overcome many inherent problems. In this review, we discuss novel microarray-based approaches that are in development and that have potential in applications for medicine, biotechnology, and basic research.

Animals↗

Gene expression informatics--it's all in your mine.

Technologies for whole-genome RNA expression studies are becoming increasingly reliable and accessible. However, universal standards to make the data more suitable for comparative analysis and for inter-operability with other information resources have yet to emerge. Improved access to large electronic data sets, reliable and consistent annotation and effective tools for 'data mining' are critical. Analysis methods that exploit large data warehouses of gene expression experiments will be necessary to realize the full potential of this technology.

Animals↗

Biomedical informatics for proteomics.

Success in proteomics depends upon careful study design and high-quality biological samples. Advanced information technologies, and also an ability to use existing knowledge to the full, will be crucial in making sense of the data. Despite its genome-scale potential, proteome analysis is at a much earlier stage of development than genomics and gene expression (microarray) studies. Fundamental issues involving biological variability, pre-analytic factors and analytical reproducibility remain to be resolved. Consequently, the analysis of proteomics data is currently informal and relies heavily on expert opinion. Databases and software tools developed for the analysis of molecular sequences and microarrays are helpful, but are limited owing to the unique attributes of proteomics data and differing research goals.

Biomedical Research↗

Informatics and multiplexing of intact protein identification in bacteria and the archaea.

Although direct fragmentation of protein ions in a mass spectrometer is far more efficient than exhaustive mapping of 1-3 kDa peptides for complete characterization of primary structures predicted from sequenced genomes, the development of this approach is still in its infancy. Here we describe a statistical model (good to within approximately 5%) that shows that the database search specificity of this method requires only three of four fragment ions to match (at +/-0.1 Da) for a 99.8% probability of being correct in a database of 5,000 protein forms. Software developed for automated processing of protein ion fragmentation data and for probability-based retrieval of whole proteins is illustrated by identification of 18 archaeal and bacterial proteins with simultaneous mass-spectrometric (MS) mapping of their entire primary structures. Dissociation of two or three proteins at once for such identifications in parallel is also demonstrated, along with retention and exact localization of a phosphorylated serine residue through the fragmentation process. These conceptual and technical advances should assist future processing of whole proteins in a higher throughput format for more robust detection of co- and post-translational modifications.

Algorithms↗

When and how to use informatics tools in caring for urologic patients.

Making predictions is an essential part of any medical decision. It is particularly crucial when considering treatment of clinically localized prostate cancer. Nomograms and prediction model software typically provide the most accurate predictions. Many nomograms have been developed, for all prostate cancer clinical states. Some of these are discussed in this review, as is their utility in facilitating decision making and informed consent.

Humans↗

Development of informatics tools for complex gene systems: killer-cell immunoglobulin-like receptor genes.

Killer-cell immunoglobulin-like receptors (KIRs) are a newly described family of polymorphic and highly homologous genes that have been difficult to classify and characterize. Before comprehensive analyzes of the genes are completed, researchers must struggle with completing the task of classifying and characterizing what is currently known. A collection and alignment of all KIR sequences found in GenBank was created to facilitate oligonucleotide reagent development and to provide an overall picture of this complex gene system. Two methods, a direct measurement of homology and phylogenetic analysis, were used to categorize sequences previously not specifically identified as belonging to a particular locus. The two methods agreed for 64.2% of sequences. A further 22.6% of uncategorized sequences were specified by only one method, although the assignments were consistent. Some sequences (11.3%) could not be assigned to a locus by either method. For one sequence, the two methods disagreed as to the locus assignment (1.9%). The failure to categorize a sequence was usually related to the short length of the sequence and the similarity among KIR loci. The tools developed in this study have been valuable in the analyses of KIR sequences and can be used for any complex gene system.

Base Sequence↗