Case studies in making sense of clinical text

It is widely anticipated that the full value of data in electronic health records will come from mining clinical notes. In practice, in order to perform data-mining studies, we need to process and analyze more than 10 million notes even for our relatively small population of patients at Stanford. Using wellknown NLP tools, this processing would either take years or require the kind of horsepower that makes logistics, expertise, security, and access a different kind of barrier to overcome. Using a clinical text processing system inspired by the NCBO Annotator and Resource Index, we process over 3 million notes per hour per CPU, generate a patient–feature matrix of nearly a trillion cells, and use this data to answer biomedical questions in four application areas ranging from pharmacovigilance to comparative effectiveness.