The Electronic Health Record (EHR) comes laden with an ambitious array of promises: at the point of patient care, it will improve quality of documentation, reduce cost of care, and promote patient safety; in parallel, as more and more data are collected about patients, the EHR gives rise to exciting opportunities for mining patient characteristics and holds out the hope of compiling comprehensive phenotypic information. Leveraging the information present in the EHR is not a trivial step, however, especially when it comes to the information conveyed in clinical notes. In this talk I will focus on one of the challenges faced by the EHR and its users: information overload. With ever-growing longitudinal health records, it is difficult for physicians to keep track of what is salient to their information needs when treating individual patients. As for text mining purposes, it is not clear that more data is always better. I will report and discuss our ongoing efforts in generating patient record summaries for clinicians.
Noemie Elhadad is an assistant professor in Biomedical Informatics at Columbia University. Her research is in informatics, natural language processing, and data mining. She investigates ways in which large, unstructured clinical datasets (e.g., patient records) and health consumer datasets (e.g., online health communities) can be processed automatically to enhance access to relevant information for clinicians, patients, and health researchers alike.