Identification of Clinical Characteristics of Large Patient Cohorts through Analysis of Free Text Physician Notes

Background A number of important applications in medicine and biomedical research, including quality of care surveillance and identification of prospective study subjects, require identification of large cohorts of patients with specific clinical characteristics. Currently used conventional techniques are either labor-intensive or imprecise, while natural language processing-based applications are relatively slow and expensive. Specific Aims In this thesis we describe the design and formal evaluation of PACT a suite of rapid, accurate, and easily portable software tools for identification of patients with specific clinical characteristics through analysis of the text of physician notes in the electronic medical record. Methods PACT algorithm is based on sentence-level semantic analysis. The major steps involve identification of word tags (e.g. name of the disease or medications exclusively used to treat the disease) specific for the clinical characteristics in the sentences of the physician notes. Sentences with word tags and negative qualifiers (e.g. "rule out diabetes") are excluded from consideration. PACT can also identify quantitative (e.g. blood pressure, height, weight) and semi-quantitative (e.g. compliance with medical treatment) clinical characteristics. PACT performance was evaluated against blinded manual chart review (the "gold standard") and currently used computational methods (analysis of billing data).