Using his natural language medical text analyzing system, the author has computer-processed the discharge summary segment of the patient record. The output of the text analyzer is a list of medical facts. The low-cost, high productivity process is eminently suited for screening the quality of clinical care provided in the hospital.

The author presents the complex scientific and technical foundation of the recently completed biomedical nomenclature. This nomenclature places all the terms into a single hierarchical order. Computer-compatibility is achieved by the numeric representation of the location ("address") of the term on the hierarchical tree. These unique numeric representations serve as codes, making the system language-independent, with potential for usage in various languages, and rendering medical terminology computer-compatible.

Clinical practice of medicine is highly information-intensive. At the bedside, past experience is the primary justification of reasoning and decisions. This past medical experience is an amalgamation of textbook information and personal experience. During the last 2-3 decades, both of these major sources of clinical information have appeared less and less effective. The pace of progress, resulting in better diagnostic tools and new therapies, has undermined our personal experience, and for the same reason, the time lapse between drafting the manuscripts and distributing the textbooks has become a growing problem. Emphasis has shifted from textbooks to scientific journals with shorter publishing delays, and the role of daily newspapers and television programs seems to be growing. The traditional ways of gathering clinical knowledge and experience seem to fail more and more. In addition to textbooks and scientific journals, current clinical experience is described in millions of patient records, stored in hospitals and ambulatory care offices. However, we have no easy access to patient charts, and we are lacking a method for cost-effective merging of clinical case histories to make them suitable for much-needed statistical inferences. Computers could make a major contribution in this area, but first we must bridge the gap between the narrative text in the medical record and computer technology. Recently, much encouraging progress has been made in automated medical text processing, the topic of this paper.

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In a previous paper, the authors described three paradigms applicable to automated medical text analysis. In this paper, the relative importance of the three paradigms is discussed, viz. the relative value of the linguistic word categorization, the semantic paradigm, and the medical fact delineation. The strategy adopted was to limit the linguistic disambiguation and apply probabilistic rules, in order to speed up the analytic process.