PubMed · 14262877
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E H BURNET. 1965. YOUR PLANNING INPUT WILL DECIDE COMPUTER OUTPUT.. https://pubmed.ncbi.nlm.nih.gov/14262877/
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OBJECTIVE: To analyze the relationship of completion rates for a standardized set of computerized clinical reminders across a large healthcare system to practice and provider characteristics. METHODS: The relationship between completion rate for 13 standardized reminders at 49 primary care practices in the VA New England Healthcare System for a 30-day period and practice characteristics, provider demographics and, via survey, provider attitudes was analyzed. RESULTS: There was no difference in clinical reminder completion rate between staff physicians versus nurse practitioners/physician assistants (87.6% versus 88.1%) but both were better than residents (76.6%, p<0.0001). With residents excluded, there were no differences between hospital and community-based clinics or between teaching and non-teaching sites. Clinical reminder completion rate was lower for sites that did not fully utilize support staff in completion process versus sites that did (82.4% versus 88.1%, p<0.0001). Analysis of survey results showed no correlation of completion rate with provider demographics or attitudes towards reminders. However there was significant correlation with frequency of receiving individual feedback on reminder completion (r=0.288, p=0.004). CONCLUSION: Completion of computerized clinical reminders was not affected by a variety of provider characteristics, including professional training, demographics and provider attitude, although was lower among residents than staff providers. However incorporation of support staff into clinic processes and individualized feedback to providers were strongly associated with improved completion. These findings demonstrate the importance of considering practice and provider factors and not just technical elements when implementing informatics tools.
The analysis of genetic diversity within species is vital for understanding evolutionary processes at the population level and at the genomic level. A large quantity of data can now be produced at an unprecedented rate, requiring the use of dedicated computer programs to extract all embedded information. Several statistical packages have been recently developed, which offer a panel of standard and more sophisticated analyses. We describe here the functionalities, special features and assumptions of more than 20 such programs, indicate how they can interoperate, and discuss new directions that could lead to improved software and analyses.
Documenting the modes of interaction of uranyl (UO(2)2+) with large biomolecules, and particularly with proteins, is instrumental for the interpretation of its behavior in vitro and in vivo. The gathering of three-dimensional information concerning uranyl-first shell atoms from two structural databases, the Cambridge Structural Databank and the Protein Data Bank (PDB) allowed a screening of corresponding topologies in proteins of known structure. In the computer-aided procedure, all potentially bound residues from the template structure were granted full flexibility using a rotamer library. The Amber force-field was used to loosen constraints and score each predicted site. Our algorithm was validated as a first stage through the recognition of existing experimental data in the PDB. The coherent localization of missing atoms in the density map of an ambiguous uranium/uranyl-protein complex exemplified the efficiency of our approach, which is currently suggesting the experimental investigation of uranyl-protein binding site.