A Topic Model and Visualization for Automatic Summarization of Patient Records (R21 LM011937; PI: Arnold)
Primary care physicians (PCPs) are responsible for reviewing and understanding a patient’s medical history to make informed decisions regarding care. However, a variety of factors impede this process, including: the increasing complexity and number of diagnostic tests and treatments, health information exchange standards that may add more information to the medical record, and the need to efficiently see more patients in less time. These obstructions can lead to an inhibition of dialogue between patients and providers, and possibly even medical errors. New methods are required to help expedite a healthcare provider’s understanding of a patient’s medical history, summarizing key information. The use of topic models for summarizing large, unstructured data collections is a growing area of research. However, to date little work has been done on adapting these models to the clinical reporting environment. This work developed and explored the use of topic models and an ensuing visualization system for automatically summarizing medical records to support PCPs.