We develop a model using deep learning techniques and natural language processing on unstructured text from medical records to predict hospital-wide $30$-day unplanned readmission, with c-statistic $.70$. Our model is constructed to allow physicians to interpret the significant features for prediction.
Unplanned hospital readmission is an indicator of patients' exposure to risk and an avoidable waste of medical resources. To address the unplanned readmission issue, in 2010, the Affordable Care Act (ACA) created the Hospital Readmissions Reduction Program to penalize the hospitals whose 30-day readmission rates are higher than expected . According to data released by the Centers for Medicare & Medicaid Services (CMS), since the program began on Oct. 1, 2012, hospitals have experienced nearly $2.5 billion of penalties assessed on hospitals for readmissions, including an estimated $564 million in fiscal year 2018, $144 million more than in 2016 . In addition to hospital readmission, intensive care unit (ICU) readmission brings further financial risk, along with morbidity and mortality risks [3,4]. Premature ICU discharge may potentially expose patients to the risks of unsuitable treatment, which further leads to an avoidable mortality .
Unplanned hospital readmissions raise health care costs and cause significant distress to patients. Hence, predicting which patients are at risk to be readmitted is of great interest. In this paper, we mine large amounts of administrative information from claim data, including patients demographics, dispensed drugs, medical or surgical procedures performed, and medical diagnosis, in order to predict readmission using supervised learning methods. Our objective is to gain knowledge about the predictive power of the available information. Our preliminary results on data from the provincial hospital system in Quebec illustrate the potential for this approach to reveal important information on factors that trigger hospital readmission. Our findings suggest that a substantial portion of readmissions is inherently hard to predict. Consequently, the use of the raw readmission rate as an indicator of the quality of provided care might not be appropriate.
"Hospital readmission is a high-priority health care quality measure and target for cost reduction. Despite broad interest in readmission, relatively little research has focused on patients with diabetes. The burden of diabetes among hospitalized patients, however, is substantial, growing, and costly, and readmissions contribute a significant portion of this burden. Reducing readmission rates of diabetic patients has the potential to greatly reduce health care costs while simultaneously improving care." This competition will be using a de-identified abstract (Strack et al. 2014) of the Health Facts database (Cerner Corporation, Kansas City, MO).