Readmissions are a serious problem in medicine.
Medical aspects are complicated by end of life care issues as well as a regulatory environment in which hospitals can experience financial penalties for "excess" readmission rates. Existing readmission predictive models, most of which are based on administrative data, have poor statistical performance, as do models that employ limited physiologic data.
Linguamatics are collaborating with Dr. Gabriel Escobar on a new readmission model that employs data from a comprehensive electronic medical record and which could be instantiated in real-time. He is working on a "road map" to incorporate data from natural language processing (NLP) into this model as well as on future strategies for instantiation of NLP engines into routine clinical operations.
Dr. Escobar is a research scientist at the Kaiser Permanente Division of Research in Oakland as well as being the Regional Director for Hospital Operations Research for Kaiser Permanente Northern California. An expert on risk adjustment and predictive modeling, Dr. Escobar has published over 130 peer-reviewed articles and is currently in the middle of deploying a real-time early warning system for deterioration outside the intensive care unit at two Kaiser Permanente hospitals.