View clinical trials related to Patient Readmission.
Filter by:This study will look to implement a plan for enhanced transitional care for patients at high risk of unplanned hospital readmission in hopes of reducing their risk for readmission in the first 30 days post discharge from an inpatient encounter. Hospital readmissions are an undesirable occurrence that can increase cost for hospitals, and can cause further negative outcomes for patients. Identifying factors that increase a patient's chances of being readmitted to the hospital, as well as developing an intervention to effectively reduce this risk, has historically been challenging. Our new method uses a combination of common features such as diagnosis and length of hospital stay, with a novel artificial intelligence (AI) algorithm, the RecuR Score model developed by the University of Maryland Medical System, that identifies patients at the highest risk of having an unplanned hospital readmission. Participants identified as higher risk will then be enrolled into our pilot where they will be randomized to receive either the standard of care treatment or an enhanced protocol that includes additional disease education, coordination of home health services, and a focus on their readmission during existing multidisciplinary team huddles. The main goal of this study is to reduce unplanned hospital readmission within 30 days of initial discharge, in those most at risk of being readmitted, using the aforementioned novel methods for identifying these participants and a transitional care intervention. This success of this goal will be analyzed across different readmission risk levels in the study population. Secondary goals of this study include reducing unplanned hospital readmission within 90 days, reducing 30-day post-discharge mortality, and reducing 30- and 90-day emergency department (ED) usage after an initial hospitalization.
In an earlier study using electronic health records (EHR), the investigators have identified nine factors to be significantly associated with FA risk. These nine predictors include Furosemide intravenous 40 milligrams or more; Admissions in the past one year; Medifund status; Frequent emergency department use; Anti-depressants treatment in past one year; Charlson comorbidity index; End Stage Renal Failure on dialysis; Subsidized ward stay and Geriatric patient. The investigators have combined these nine predictors into the FAM-FACE-SG score for FA risk (defined as 3 or more inpatient admissions in the following 12 months). The FAM-FACE-SG risk score has the advantage of being deployed in our hospital's enterprise data repository known as Electronic Health Intelligence System or eHINTs for short, on a real-time or near real-time basis. On a daily basis, data from multiple data sources are extracted, transformed and loaded onto the eHINTS system. The system can be programmed to run every midnight to provide risk scores the following morning for patients admitted the previous day. In this trial, the intervention is to combine the FAM-FACE-SG risk score in addition to a decision making algorithm to guide referrals to various transitional care services based on needs assessment on nursing and function. The primary objective is to evaluate the impact of our intervention in improving healthcare utilization (hospital readmissions, emergency department (ED) attendances, length of stay up to 90 days post-discharge).