Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions.

BACKGROUND: Electronic health record (EHR) users must regularly review large amounts of data in order to make informed clinical decisions, and such review is time-consuming and often overwhelming. Technologies like automated summarization tools, EHR search engines and natural language processing have been shown to help clinicians manage this information. OBJECTIVE: To develop a support vector machine (SVM)-based system for identifying EHR progress notes pertaining to diabetes, and to validate it at two institutions. MATERIALS AND METHODS: We retrieved 2000 EHR progress notes from patients with diabetes at the Brigham and Women's Hospital (1000 for training and 1000 for testing) and another 1000 notes from the University of Texas Physicians (for validation). We manually annotated all notes and trained a SVM using a bag of words approach. We then used the SVM on the testing and validation sets and evaluated its performance with the area under the curve (AUC) and F statistics. RESULTS: The model accurately identified diabetes-related notes in both the Brigham and Women's Hospital testing set (AUC=0.956, F=0.934) and the external University of Texas Faculty Physicians validation set (AUC=0.947, F=0.935). DISCUSSION: Overall, the model we developed was quite accurate. Furthermore, it generalized, without loss of accuracy, to another institution with a different EHR and a distinct patient and provider population. CONCLUSIONS: It is possible to use a SVM-based classifier to identify EHR progress notes pertaining to diabetes, and the model generalizes well.

Adverse drug events caused by serious medication administration errors.

OBJECTIVE: To determine how often serious or life-threatening medication administration errors with the potential to cause harm (potential adverse drug events) result in actual harm (adverse drug events (ADEs)) in the hospital setting. DESIGN: Retrospective chart review of clinical events following observed medication administration errors. BACKGROUND: Medication errors are common at the medication administration stage for inpatients. While many errors can cause harm, it is unclear exactly how often. METHODS: In a previous study where 14 041 medication administrations were directly observed, 1271 medication administration errors were discovered, of which 133 had the potential to cause serious or life-threatening harm and were considered serious or life-threatening potential adverse drug events. As a follow-up, clinical reviewers conducted detailed chart review of serious or life-threatening potential ADEs to determine if they caused an ADE. Reviewers assessed severity of the ADE and attribution to the error. RESULTS: Ten (7.5% (95% CI 6.98 to 8.01)) actual ADEs resulted from the 133 serious and life-threatening potential ADEs, of which 6 resulted in significant, three in serious, and one life threatening injury. Therefore 4 (3% (95% CI 2.12 to 3.6)) of serious or life threatening potential ADEs led to serious or life threatening ADEs. Half of the ADEs were caused by dosage or monitoring errors for anti-hypertensives. CONCLUSIONS: Unintercepted potential ADEs at the medication administration stage can cause serious patient harm. At hospitals where 6 million doses are administered per year, about 4000 preventable ADEs would be attributable to medication administration errors annually.