Natural language processing IDs medical complications

While we have yet to reach the holy grail of converting free text to structured data in an electronic health record, natural language processing is beginning to show real promise in healthcare. The latest indication of this is a study showing that the application of NLP to free text in an EHR identifies postsurgical complications more accurately than the analysis of discharge billing codes. The study in the Journal of the American Medical Association compared the NLP method to the use of administrative data in the Veterans Affairs Surgical Quality Improvement Program. The study population is a randomly selected sample of nearly 3,000 patients treated at six VA hospitals between 1999 and 2006. The researchers focused on six post-operative complications that the VASQIP nurse reviewers had searched for using patient safety indicators based on billing codes. These included acute renal failure requiring dialysis, sepsis, deep vein thrombosis, pulmonary embolism, myocardial infarction, and pneumonia. The results showed that NLP was more sensitive in detecting all but one of the complications. On the other hand, NLP had somewhat lower specificity when compared to using administrative codes. (The more specific an assessment tool is, the better able it is to rule out the existence of a complication that doesn't really exist.)