title: Development of an expert system for classification of medical errors.
abstract: The 1999 report published by the Institute of Medicine (IOM) indicated that between 44,000 and 98,000 unnecessary deaths per year occurred in hospitals alone, as a result of errors committed by medical professionals in the United States. There has been considerable speculation that these figures are either overestimated or underestimated. For example, the possibility that they focus on isolated injuries rather than error, or the majority of surveyed respondents did not know what constitutes a (medical) error. These disagreements have led experts to challenge the estimates of patient harm attributable to error, as well as the methodologies used to enumerate them. Of particular concern is the process used in the identification, classification and prevention of medical errors. There have been numerous attempts to develop classifications of medical errors, and currently an abundance of taxonomies exist to describe their mechanism.In previous research, (Kopec, Kabir, Reinharth, Rothschild & Castiglione, 2003) a new taxonomy of Medical Errors was designed by expanding the IOM classification. This model and its extension can be used as a blueprint for future design, development and implementation of an expert system for classification of medical errors. Effective classification can facilitate pattern recognition, and pattern recognition will help in understanding the nature, background and abatement of medical errors. Such a system's goal will be to perform convincingly as an advisory consultant, exhibiting expertise on a par with and beyond human experts in specified domains. Despite substantial disagreement on the validity of the published figures for fatalities in hospitals in the IOM report, what is of importance is that the number of deaths caused by such errors is nonetheless alarming. The identification and classification of errors in medical care delivery is a very complex process, and this process can be facilitated and simplified by the implementation of an effective classification system.
authors.affiliation: Department of Computer and Information Science, Brooklyn College, New York, USA.
authors.forename: D
authors.initials: D
authors.lastname: Kopec
authors.forename: K
authors.initials: K
authors.lastname: Levy
authors.forename: M
authors.initials: M
authors.lastname: Kabir
authors.forename: D
authors.initials: D
authors.lastname: Reinharth
authors.forename: G
authors.initials: G
authors.lastname: Shagas
mesh_terms: D003695:Delivery of Health Care; D005103:Expert Systems; D006761:Hospitals; D006801:Humans; D019300:Medical Errors / Q000517:prevention & control; D014481:United States