A Clinical Decision Support System for the Identification of Potential Hospital Readmission Patient

Recent federal legislation has incentivized hospitals to focus on quality of patient care. A primary metric of care quality is patient readmissions. Many methods exist to statistically identify patients most likely to require hospital readmission. Correct identification of high-risk patients allows hospitals to intelligently utilize limited resources in mitigating hospital readmissions. However, these methods have seen little practical adoption in the clinical setting. This research attempts to identify the many open research questions that have impeded widespread adoption of predictive hospital readmission systems.;Current systems often rely on structured data extracted from health records systems. This data can be expensive and time consuming to extract. Unstructured clinical notes are agnostic to the underlying records system and would decouple the predictive analytics system from the underlying records system. However, additional concerns in clinical natural language processing must be addressed before such a system can be implemented.;Current systems often perform poorly using standard statistical measures. Misclassification cost of patient readmissions has yet to be addressed and there currently exists a gap between current readmission system evaluation metrics and those most appropriate in the clinical setting. Additionally, data availability for localized model creation has yet to be addressed by the research community. Large research hospitals may have sufficient data to build models, but many others do not. Simply combining data from many hospitals often results in a model which performs worse than using data from a single hospital.;Current systems often produce a binary readmission classification. However, patients are often readmitted for differing reasons than index admission. There exists little research into predicting primary cause of readmission. Furthermore, co-occurring evidence discovery of clinical terms with primary diagnosis has seen only simplistic methods applied.;This research addresses these concerns to increase adoption of predictive hospital readmission systems.