Article

Automating the Assignment of Diagnosis Codes to Patient Encounters Using Example-based and Machine Learning Techniques

Division of Biomedical Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Journal of the American Medical Informatics Association (Impact Factor: 3.93). 09/2006; 13(5):516-25. DOI: 10.1197/jamia.M2077
Source: PubMed

ABSTRACT Human classification of diagnoses is a labor intensive process that consumes significant resources. Most medical practices use specially trained medical coders to categorize diagnoses for billing and research purposes.
We have developed an automated coding system designed to assign codes to clinical diagnoses. The system uses the notion of certainty to recommend subsequent processing. Codes with the highest certainty are generated by matching the diagnostic text to frequent examples in a database of 22 million manually coded entries. These code assignments are not subject to subsequent manual review. Codes at a lower certainty level are assigned by matching to previously infrequently coded examples. The least certain codes are generated by a naïve Bayes classifier. The latter two types of codes are subsequently manually reviewed.
Standard information retrieval accuracy measurements of precision, recall and f-measure were used. Micro- and macro-averaged results were computed. RESULTS At least 48% of all EMR problem list entries at the Mayo Clinic can be automatically classified with macro-averaged 98.0% precision, 98.3% recall and an f-score of 98.2%. An additional 34% of the entries are classified with macro-averaged 90.1% precision, 95.6% recall and 93.1% f-score. The remaining 18% of the entries are classified with macro-averaged 58.5%.
Over two thirds of all diagnoses are coded automatically with high accuracy. The system has been successfully implemented at the Mayo Clinic, which resulted in a reduction of staff engaged in manual coding from thirty-four coders to seven verifiers.

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Available from: Christopher G Chute, Jul 14, 2014
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    • "Clinical documentations in computer-based records are found to be more complete and appropriate for clinical decisions than those in paper-based records [18]. Likewise, automated coding and classification encompasses a variety of computerbased approaches, that are faster, reduce error rates, and are more efficient and accurate [4] [19] [20] [21]. Similarly, improvement in clinical documentation will be necessary to ensure complete automated coding [22] "
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    • "One pertinent example is the automatic categorization of informally written medical diagnoses, followed by the extraction of epidemiological information or even terms and structures needed to formulate guiding questions as a heuristic tool for helping doctors. Vector space models including LSA have been successfully used to this end (Lee et al., 2006; Pakhomov et al., 2006). Nonetheless, results from this type of models are at the mercy of the vectorial dynamics involved and the representational bias of some terms. "
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    • "In most chief complaint classifier studies, the performance of chief complaint classification methods is measured by sensitivity , specificity, positive predictive value (PPV), F measure, and F2 measure [4] [5] [8] [13] [14]. The F measure is a weighted harmonic mean of PPV and sensitivity. "
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