Evaluation of the quality of information retrieval of clinical findings from a computerized patient database using a semantic terminological model.

objectivesTo measure the strength of agreement between the concepts and records retrieved from a computerized patient database, in response to physician-derived questions, using a semantic terminological model for clinical findings with those concepts and records excerpted clinically by manual identification. The performance of the semantic terminological model is also compared with the more established retrieval methods of free-text search, ICD-10, and hierarchic retrieval.

designA clinical database (Diabeta) of 106,000 patient problem record entries containing 2,625 unique concepts in an clinical academic department was used to compare semantic, free-text, ICD-10, and hierarchic data retrieval against a gold standard in response to a battery of 47 clinical questions. MEASUREMENTS: The performance of concept and record retrieval expressed as mean detection rate, positive predictive value, Yates corrected and Mantel-Haenszel chi-squared values, and Cohen kappa value, with significance estimated using the Mann-Whitney test.

resultsThe semantic terminological model used to retrieve clinically useful concepts from a patient database performed well and better than other methods, with a mean detection rate of 0.86, a positive predictive value of 0.96, a Yates corrected chi-squared value of 1,537, a Mantel-Haenszel chi-squared value of 19,302, and a Cohen kappa of 0.88. Results for record retrieval were even better, with a mean record detection rate of 0.94, a positive predictive value of 0.99, a Yates corrected chi-squared value of 94, 774, a Mantel-Haenszel chi-squared value of 1,550,356, and a Cohen kappa value of 0.94. The mean detection rate, Yates corrected chi-squared value, and Cohen kappa value for semantic retrieval were significantly better than for the other methods.

conclusionThe use of a semantic terminological model in this test scenario provides an effective framework for representing clinical finding concepts and their relationships. Although currently incomplete, the model supports improved information retrieval from a patient database in response to clinically relevant questions, when compared with alternative methods of analysis.