Document Detail


Automated identification of postoperative complications within an electronic medical record using natural language processing.
MedLine Citation:
PMID:  21862746     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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:
Harvey J Murff; Fern FitzHenry; Michael E Matheny; Nancy Gentry; Kristen L Kotter; Kimberly Crimin; Robert S Dittus; Amy K Rosen; Peter L Elkin; Steven H Brown; Theodore Speroff
Related Documents :
19236956 - Tracking medical students' clinical experiences using natural language processing.
11433546 - An approach for integrating heterogeneous information sources in a medical data warehouse.
18852916 - Semantic structuring of and information extraction from medical documents using the umls.
16823146 - Mobile peer-to-grid architecture for paramedical emergency operations.
1797466 - Serious intestinal complication five years after insertion of a nova-t.
21811176 - Patterns of medication administration from 2001 to 2009 in the treatment of children wi...
Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  JAMA : the journal of the American Medical Association     Volume:  306     ISSN:  1538-3598     ISO Abbreviation:  JAMA     Publication Date:  2011 Aug 
Date Detail:
Created Date:  2011-08-24     Completed Date:  2011-08-25     Revised Date:  2011-12-07    
Medline Journal Info:
Nlm Unique ID:  7501160     Medline TA:  JAMA     Country:  United States    
Other Details:
Languages:  eng     Pagination:  848-55     Citation Subset:  AIM; IM    
Affiliation:
Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA. harvey.j.murff@vanderbilt.edu
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Automation
Cross-Sectional Studies
Diagnosis-Related Groups
Electronic Health Records*
Hospitalization
Hospitals, Veterans / statistics & numerical data
Humans
Information Storage and Retrieval*
Inpatients
International Classification of Diseases
Myocardial Infarction / epidemiology
Natural Language Processing*
Patient Discharge / statistics & numerical data
Pneumonia / epidemiology
Population Surveillance
Postoperative Complications / epidemiology*
Pulmonary Embolism / epidemiology
Quality Indicators, Health Care*
Renal Insufficiency / epidemiology
Safety
Sensitivity and Specificity
Sepsis / epidemiology
Surgical Procedures, Operative
United States / epidemiology
Venous Thrombosis / epidemiology
Comments/Corrections
Comment In:
JAMA. 2011 Dec 7;306(21):2325; author reply 2325-6   [PMID:  22147375 ]
JAMA. 2011 Aug 24;306(8):880-1   [PMID:  21862751 ]

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Association of ICU or hospital admission with unintentional discontinuation of medications for chron...
Next Document:  Progression of left ventricular diastolic dysfunction and risk of heart failure.