The SAGE (Standards-Based Active Guideline Environment) project was formed to create a methodology and infrastructure required to demonstrate integration of decision-support technology for guideline-based care in commercial clinical information systems. This paper describes the development and innovative features of the SAGE Guideline Model and reports our experience encoding four guidelines. Innovations include methods for integrating guideline-based decision support with clinical workflow and employment of enterprise order sets. Using SAGE, a clinician informatician can encode computable guideline content as recommendation sets using only standard terminologies and standards-based patient information models. The SAGE Model supports encoding large portions of guideline knowledge as re-usable declarative evidence statements and supports querying external knowledge sources.
We extend the use of amino acid sequence patterns [Cohen, F.E., Abarbanel, R. M., Kuntz, I. D., & Fletterick, R. J. (1983) Biochemistry 22, 4894-4904] to the identification of turns in globular proteins. The approach uses a conservative strategy, combined with a hierarchical search (strongest patterns first) and length-dependent masking, to achieve high accuracy (95%) on a test set of proteins of known structure. Applying the same procedure to homologous families gives a 90% success rate. Straightforward changes are suggested to improve the predictive power. The computer program, written in Lisp, provides a general pattern-recognition language well suited for a number of investigations of protein and nucleic acid sequences.
Careful study of medical informatics research and library-resource projects is necessary to increase the productivity of the research and development enterprise. Medical informatics research projects can present unique problems with respect to evaluation. It is not always possible to adapt directly the evaluation methods that are commonly employed in the natural and social sciences. Problems in evaluating medical informatics projects may be overcome by formulating system development work in terms of a testable hypothesis; subdividing complex projects into modules, each of which can be developed, tested and evaluated rigorously; and utilizing qualitative studies in situations where more definitive quantitative studies are impractical.
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