View clinical trials related to Readmission.
Filter by:Patients with Chronic Obstructive Pulmonary Disease (COPD) who are admitted to hospital are at high risk of readmission. While therapies have improved and there are evidence-based guidelines to reduce readmissions, there are significant challenges to implementation including 1) identifying all patients with COPD early in admission to ensure evidence-based, high value care is provided and 2) identifying those who are at high risk of readmission in order to effectively target resources. Using machine learning and natural language processing, we want to develop models to 1) identify all patients with COPD exacerbations admitted to hospital and 2) stratify them to distinguish those who are at high risk of readmission b) How will you undertake your work? From Toronto hospitals, we will develop a very large dataset of patient admissions for all medical conditions including exacerbations of COPD from the electronic health record. This data will include both structured data such as age, gender, medications, laboratory values, co-morbidities as well as unstructured data such as discharge summaries and physician notes. Using the dataset, we will train a model through natural language processing and machine learning to be able to identify people admitted with COPD exacerbation and identify those patients who will be at high risk of readmission within 30 days. We will test the ability of these models to determine our predictive accuracies. We will then test these models at other institutions.
This is a randomized trial testing a peri- and post-discharge pharmacist-led medication management intervention on post-discharge utilization, including both readmissions and emergency department visits within 30 days of discharge. The intervention incorporates evidence addressing three main areas: medication reconciliation, medication adherence, and polypharmacy. This study uses a pragmatic trial randomized at the patient level and conducted in two large hospitals to achieve the following aims, each of which has been designed using the RE-AIM framework: Aim 1: To test the effect of PHARM-DC on post-discharge utilization among patients most at risk for post-discharge ADEs: recently discharged older adults taking >10 medications or >3 high-risk medications using a prospective, randomized, pragmatic multi-site study. Aim 2: To study barriers and facilitators of implementing PHARM-DC using a qualitative study. Aim 3: To analyze the costs of PHARM-DC, including the incremental cost per readmission averted and the net incremental cost from the health system perspective using a time-and-motion study and a cost-effectiveness analysis.