Document Type: Technical Advance
Department of Health Information Technology, School of Allied Health Sciences, Tehran University of Medical Sciences, Tehran, Iran
Department of Health Information Management, School of Allied medicine, Tehran University of Medical Sciences, Tehran, Iran
School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Fast and holistic access to the patients’ clinical record is a major requirement of modern medical decision support systems (DSS). While electronic health records (EHRs) have replaced the traditional paper-based records in most healthcare organization, the data entry into these systems remains largely manual. Speech recognition technology promises substitution of the more convenient speech-based data entry with currently laborious manual method, in the near future. Developing effective speech recognition systems (SRS) require availability of standardized vocabulary databases. This study was aimed at developing a medical speech recognition database for reconstructive hand surgery based on the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT). All codes related to hand problems were extracted from ICD-10. A sample of 2051 diagnosis codes was randomly selected from the patients’ records. The operation report paper sheets were transformed to electronic records. For each term, the SNOMED-CT was browsed to find the preferred synonym, using CliniClue® Xplore Software. For some words with several number of synonyms, the preference of reconstructive surgery specialists were asked using a researcher-made questionnaire. Ultimately, the preferred words was substituted throughout each document and used for developing a database of standard nomenclature. The developed database was used in speech-based recording clinical data in reconstructive hand surgery operating room and accuracy of 81% in correct recording of clinical data was observed. Therefore, development of standard medical nomenclature databases can facilitate accurate electronic recording of medical data and reduce the associated labor and cost posed by current manual method.