Objective: According to the American Diabetes Association, the implementation of the standards of care for diabetes has been suboptimal in most clinical settings. Diabetes is a disease that had a total estimated cost of $174 billion in 2007 for an estimated diabetes-affected population of 17.5 million in the United States. With the advent of electronic medical records (EMR), tools to analyze data residing in the EMR for healthcare surveillance can help reduce the burdens experienced today. This study was primarily designed to evaluate the efficacy of employing clinical natural language processing to analyze discharge summaries for evidence indicating a presence of diabetes, as well as to assess diabetes protocol compliance and high risk factors.Methods: Three sets of algorithms were developed to analyze discharge summaries for:(1) identification of diabetes, (2) protocol compliance, and (3) identification of high risk factors. The algorithms utilize a common natural language processing framework that extracts relevant discourse evidence from the medical text. Evidence utilized in one or more of the algorithms include assertion of the disease and associated findings in medical text, as well as numerical clinical measurements and prescribed medications. Results:The diabetes classifier was successful at classifying reports for the presence and absence of diabetes. Evaluated against 444 discharge summaries, the classifier's performance included macro and micro F-scores of 0.9698 and 0.9865, respectively. Furthermore, the protocol compliance and high risk factor classifiers showed promising results, with most F-measures exceeding 0.9. Conclusions:The presented approach accurately identified diabetes in medical discharge summaries and showed promise with regards to assessment of protocol compliance and high risk factors. Utilizing free-text analytic techniques on medical text can complement clinical-public health decision support by identifying cases and high risk factors.

OBJECTIVE Evaluate the effectiveness of a simple rule-based approach in classifying medical discharge summaries according to indicators for obesity and 15 associated co-morbidities as part of the 2008 i2b2 Obesity Challenge. METHODS The authors applied a rule-based approach that looked for occurrences of morbidity-related keywords and identified the types of assertions in which those keywords occurred. The documents were then classified using a simple scoring algorithm based on a mapping of the assertion types to possible judgment categories. MEASUREMENTS RESULTS for the challenge were evaluated based on macro F-measure. We report micro and macro F-measure results for all morbidities combined and for each morbidity separately. Results Our rule-based approach achieved micro and macro F-measures of 0.97 and 0.77, respectively, ranking fifth out of the entries submitted by 28 teams participating in the classification task based on textual judgments and substantially outperforming the average for the challenge. CONCLUSIONS As shown by its ranking in the challenge results, this approach performed relatively well under conditions in which limited training data existed for some judgment categories. Further, the approach held up well in relation to more complex approaches applied to this classification task. The approach could be enhanced by the addition of expert rules to model more complex medical reasoning.