JMIR Publications

ABSTRACT

Background:

Text 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 the case of transfusion adverse events (TAEs) and potential TAEs (PTAEs) because real dates were obscured in the study data, and new TAE types were becoming recognized during the study data period.

Objective:

Develop a new method to identify attributed and unattributed potential adverse events using the unstructured text of EHRs.

Methods:

We used EHRs for adult critical care admissions at a major teaching hospital, 2001-2012. We formed a transfusion (T) group (21,443 admissions treated with packed red blood cells, platelets, or plasma), excluded 2,373 ambiguous admissions, and formed a comparison (C) group of 25,468 admissions. We concatenated the text notes for each admission, sorted by date, into one document, and deleted replicate sentences and lists. We identified statistically significant words in T vs. C. T documents were filtered to those words, followed by topic modeling on the T filtered documents to produce 45 topics. For each topic, the three documents with the maximum topic scores were manually reviewed to identify events that occurred shortly after the first transfusion; documents with clear alternative explanations for heart, lung, and volume overload problems (e.g., advanced cancer, lung infection) were excluded. We also reviewed documents with the most topics, as well as 20 randomly selected T documents without alternate explanations.

Results:

Topics centered around medical conditions. The average number of significant topics was 6.1. Most PTAEs were not attributed to transfusion in the notes. Admissions with a top-scoring cardiovascular topic (heart valve repair, tapped pericardial effusion, coronary artery bypass graft, heart attack, or vascular repair) were more likely than random T admissions to have at least one heart PTAE (heart rhythm changes or hypotension, proportion difference = 0.47, p = 0.022). Admissions with a top-scoring pulmonary topic (mechanical ventilation, acute respiratory distress syndrome, inhaled nitric oxide) were more likely than random T admissions (proportion difference = 0.37, p = 0.049) to have at least one lung PTAE (hypoxia, mechanical ventilation, bilateral pulmonary effusion, or pulmonary edema).

Conclusions:

The “Shakespeare Method” could be a useful supplement to AE reporting and surveillance of structured EHR data. Future improvements should include automation of the manual review process. Clinical Trial: This was not a trial.