No abstract

We used a large national cohort to study trends in OPM prescriptions within the VA. Educational efforts to reduce the number of OPM prescriptions coincided with these reductions, but were initially associated with an increase in OPM dosage, an increase in the use of tramadol and hydrocodone, and an increase in the use of OPMs in inpatient settings.

Physicians are often required to make critical medical decisions that may be based on previous events in the patient's health history. However, these events may be very difficult to locate in the patient record due to the large volume of unstructured textual data in the patient's chart. Even when the chart is housed in an electronic health record (EHR) system, keyword search within the chart may produce many results that are not relevant or that may overlook related expressions and concepts entirely. In addition, some medical events, such as the occurrence of symptoms, are associated with important attributes such as location or severity, and require other elements such as the type of clinical note and its date and time in order to provide the proper context of the event. This paper describes a prototype system that performs ontology-based semantic search through clinical text to extract pain severity events, and then presents them in a visualization to monitor the progression of pain over time.