New and Increasing Rates of Adverse Events Can be Found in Unstructured Text in Electronic Health Records using the Shakespeare Method

BackgroundText in electronic health records (EHRs) and big data tools offer the opportunity for surveillance of adverse events (patient harm associated with medical care) (AEs) in the unstructured notes. Writers may explicitly state an apparent association between treatment and adverse outcome ("attributed") or state the simple treatment and outcome without an association ("unattributed"). We chose to study EHRs from 2006-2008 because of known heparin contamination during this timeframe. We hypothesized that the prevalence of adulterated heparin may have been widespread enough to manifest in EHRs through symptoms related to heparin adverse events, independent of clinicians documentation of attributed AEs. ObjectiveUse the Shakespeare Method, a new unsupervised set of tools, to identify attributed and unattributed potential AEs using the unstructured text of EHRs. MethodsWe studied 21,287 adult critical care admissions divided into three time periods. Comparisons of period 3 (7/2007 to 6/2008) to period 2 (7/2006 to 6/2007) were used to find admissions notes to review for new or increased clinical events by generating Latent Dirichlet Allocation topics among words in period 3 that were distinct from period 2. These results were further explored with frequency analyses of periods 1 (7/2001 to 6/2006) through 3. ResultsTopics represented unattributed heparin AEs, other medical AEs, rare medical diagnoses, and other clinical events; all were verified with EHRs notes review and frequency analysis. The heparin AEs were not attributed in the notes, diagnosis codes, or procedure codes. Somewhat different from our hypothesis, heparin AEs increased in prevalence from 2001 through 2007, and decreased starting in 2008 (when heparin AEs were being published). ConclusionsThe Shakespeare Method could be a useful supplement to AE reporting and surveillance of structured EHRs data. Future improvements should include automation of the manual review process.