Automated identification of patients with a diagnosis of binge eating disorder from narrative electronic health records.

Binge eating disorder (BED) does not have an International Classification of Diseases, 9th or 10th edition code, but is included under 'eating disorder not otherwise specified' (EDNOS). This historical cohort study identified patients with clinician-diagnosed BED from electronic health records (EHR) in the Department of Veterans Affairs between 2000 and 2011 using natural language processing (NLP) and compared their characteristics to patients identified by EDNOS diagnosis codes. NLP identified 1487 BED patients with classification accuracy of 91.8% and sensitivity of 96.2% compared to human review. After applying study inclusion criteria, 525 patients had NLP-identified BED only, 1354 had EDNOS only, and 68 had both BED and EDNOS. Patient characteristics were similar between the groups. This is the first study to use NLP as a method to identify BED patients from EHR data and will allow further epidemiological study of patients with BED in systems with adequate clinical notes.

Conformity between protocol eligibility criteria for electronic patient identification: a comparison of clinical trials.

Clinical trial eligibility criteria define the target patient population for research studies. We assessed the eligibility criteria from 40 different protocols for Type II Diabetes Mellitus and depression (20 protocols each), to determine the extent to which protocol eligibility criteria were similar at three levels (test, test-value, and test-value-time clause). This was done to determine criteria that could be standardized to aid in identification of eligible patients from electronic health records. It was found that Type II Diabetes Mellitus had 36.9% similar and depression protocols had 53.1% similar at the test-value-clause level. This study demonstrates the need for more standardization of study protocol criteria as well as the associated query definitions to be run against the electronic healthcare data. Standardizing criteria based on the similar eligibility criteria between protocols will aid in patient recruitment by being able to reuse criteria and minimizing the time and money it takes to recruit patients.