Key result
Natural language processing identified postoperative complications with higher sensitivity but lower specificity compared to patient safety indicators based on discharge coding.
Why the study?
Does natural language processing analysis of electronic medical records improve the identification of postoperative complications compared with discharge coding in patients undergoing inpatient surgical procedures?
Does natural language processing analysis of electronic medical records improve the identification of postoperative complications compared with discharge coding in patients undergoing inpatient surgical procedures?
Natural language processing of electronic medical records offers higher sensitivity but lower specificity than traditional discharge coding for identifying postoperative complications.
NLP should not yet change postoperative complication surveillance; leaves open whether automated text analysis can augment coding-based quality metrics.
Among patients undergoing inpatient surgical procedures at VA medical centers, natural language processing analysis of electronic medical records to identify postoperative complications had higher sensitivity and lower specificity compared with patient safety indicators based on discharge coding.
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Murff et al. (2011) studied this question. Natural language processing identified postoperative complications with higher sensitivity but lower specificity compared to patient safety indicators based on discharge coding.
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