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Automated identification of postoperative complications within an electronic medical record using natural language processing.
JAMA. 2011 Aug 24; 306(8):848-55.JAMA

Abstract

CONTEXT

Currently most automated methods to identify patient safety occurrences rely on administrative data codes; however, free-text searches of electronic medical records could represent an additional surveillance approach.

OBJECTIVE

To evaluate a natural language processing search-approach to identify postoperative surgical complications within a comprehensive electronic medical record.

DESIGN, SETTING, AND PATIENTS

Cross-sectional study involving 2974 patients undergoing inpatient surgical procedures at 6 Veterans Health Administration (VHA) medical centers from 1999 to 2006.

MAIN OUTCOME MEASURES

Postoperative occurrences of acute renal failure requiring dialysis, deep vein thrombosis, pulmonary embolism, sepsis, pneumonia, or myocardial infarction identified through medical record review as part of the VA Surgical Quality Improvement Program. We determined the sensitivity and specificity of the natural language processing approach to identify these complications and compared its performance with patient safety indicators that use discharge coding information.

RESULTS

The proportion of postoperative events for each sample was 2% (39 of 1924) for acute renal failure requiring dialysis, 0.7% (18 of 2327) for pulmonary embolism, 1% (29 of 2327) for deep vein thrombosis, 7% (61 of 866) for sepsis, 16% (222 of 1405) for pneumonia, and 2% (35 of 1822) for myocardial infarction. Natural language processing correctly identified 82% (95% confidence interval [CI], 67%-91%) of acute renal failure cases compared with 38% (95% CI, 25%-54%) for patient safety indicators. Similar results were obtained for venous thromboembolism (59%, 95% CI, 44%-72% vs 46%, 95% CI, 32%-60%), pneumonia (64%, 95% CI, 58%-70% vs 5%, 95% CI, 3%-9%), sepsis (89%, 95% CI, 78%-94% vs 34%, 95% CI, 24%-47%), and postoperative myocardial infarction (91%, 95% CI, 78%-97%) vs 89%, 95% CI, 74%-96%). Both natural language processing and patient safety indicators were highly specific for these diagnoses.

CONCLUSION

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.

Authors+Show Affiliations

Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA. harvey.j.murff@vanderbilt.eduNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

21862746

Citation

Murff, Harvey J., et al. "Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing." JAMA, vol. 306, no. 8, 2011, pp. 848-55.
Murff HJ, FitzHenry F, Matheny ME, et al. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA. 2011;306(8):848-55.
Murff, H. J., FitzHenry, F., Matheny, M. E., Gentry, N., Kotter, K. L., Crimin, K., Dittus, R. S., Rosen, A. K., Elkin, P. L., Brown, S. H., & Speroff, T. (2011). Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA, 306(8), 848-55. https://doi.org/10.1001/jama.2011.1204
Murff HJ, et al. Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing. JAMA. 2011 Aug 24;306(8):848-55. PubMed PMID: 21862746.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Automated identification of postoperative complications within an electronic medical record using natural language processing. AU - Murff,Harvey J, AU - FitzHenry,Fern, AU - Matheny,Michael E, AU - Gentry,Nancy, AU - Kotter,Kristen L, AU - Crimin,Kimberly, AU - Dittus,Robert S, AU - Rosen,Amy K, AU - Elkin,Peter L, AU - Brown,Steven H, AU - Speroff,Theodore, PY - 2011/8/25/entrez PY - 2011/8/25/pubmed PY - 2011/8/27/medline SP - 848 EP - 55 JF - JAMA JO - JAMA VL - 306 IS - 8 N2 - CONTEXT: Currently most automated methods to identify patient safety occurrences rely on administrative data codes; however, free-text searches of electronic medical records could represent an additional surveillance approach. OBJECTIVE: To evaluate a natural language processing search-approach to identify postoperative surgical complications within a comprehensive electronic medical record. DESIGN, SETTING, AND PATIENTS: Cross-sectional study involving 2974 patients undergoing inpatient surgical procedures at 6 Veterans Health Administration (VHA) medical centers from 1999 to 2006. MAIN OUTCOME MEASURES: Postoperative occurrences of acute renal failure requiring dialysis, deep vein thrombosis, pulmonary embolism, sepsis, pneumonia, or myocardial infarction identified through medical record review as part of the VA Surgical Quality Improvement Program. We determined the sensitivity and specificity of the natural language processing approach to identify these complications and compared its performance with patient safety indicators that use discharge coding information. RESULTS: The proportion of postoperative events for each sample was 2% (39 of 1924) for acute renal failure requiring dialysis, 0.7% (18 of 2327) for pulmonary embolism, 1% (29 of 2327) for deep vein thrombosis, 7% (61 of 866) for sepsis, 16% (222 of 1405) for pneumonia, and 2% (35 of 1822) for myocardial infarction. Natural language processing correctly identified 82% (95% confidence interval [CI], 67%-91%) of acute renal failure cases compared with 38% (95% CI, 25%-54%) for patient safety indicators. Similar results were obtained for venous thromboembolism (59%, 95% CI, 44%-72% vs 46%, 95% CI, 32%-60%), pneumonia (64%, 95% CI, 58%-70% vs 5%, 95% CI, 3%-9%), sepsis (89%, 95% CI, 78%-94% vs 34%, 95% CI, 24%-47%), and postoperative myocardial infarction (91%, 95% CI, 78%-97%) vs 89%, 95% CI, 74%-96%). Both natural language processing and patient safety indicators were highly specific for these diagnoses. CONCLUSION: 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. SN - 1538-3598 UR - https://www.unboundmedicine.com/medline/citation/21862746/Automated_identification_of_postoperative_complications_within_an_electronic_medical_record_using_natural_language_processing_ L2 - https://jamanetwork.com/journals/jama/fullarticle/10.1001/jama.2011.1204 DB - PRIME DP - Unbound Medicine ER -