View clinical trials related to Patient Readmission.
Filter by:Unplanned 30-day hospital readmissions are an critical healthcare quality metric, with meaningful effects on patients and health systems operations. Interventions to reduce unplanned readmissions have primarily operated within a healthcare-centric frame, with enhancements to either pre- or post-discharge care planning, medication reconciliation, or visit frequency. Associations of 30-day readmission rates with poverty status and other social factors, however, suggest that attending to unmet social needs may yield added benefits to models focused on healthcare delivery. The purpose of the present trial is to provide evidence regarding the effects on 30-day readmissions of providing a one-time post-discharge income supplement to socially vulnerable older adults with medical complexity participating in an enhanced care coordination program.
Chronic obstructive pulmonary disease (COPD) is a common smoking-related lung disease. Patients with COPD are at increased risk of readmission to hospitals within the following 30 days. Hospital readmissions of COPD contribute to clinical and economic burden on society. Understanding why some COPD patients are readmitted remains a key area of unmet need. To our knowledge, no previous study has fully investigated both the social and clinical risk factors associated with these types of patients. The investigators want to prospectively and comprehensively explore the possible causes, whether clinical or social factors, that cause rehospitalisation. The investigators will be collecting demographic and clinical information including daily physical activity level, lung function, blood and sputum samples. These measurements will be collected at patient admission, discharge and at follow-up of 30 and 90 days. This process could lead to a better understanding of the reasons which prevent early hospital readmission for those patients.
The study evaluates the feasibility of providing tele-transition of care, using risk stratification, novel data tools, remote patient monitoring and virtual visits. A new communication tool for relaying tele-communication among providers caring for the virtual patient is introduced. The primary endpoint is 30-day readmissions.
Unplanned hospital readmissions are associated with increases in morbidity, mortality, cost and patient dissatisfaction,. Policymakers continue to seek effective policy solutions to avoid readmissions in order to improve quality of care and reduce unnecessary expenditures,. One attempt to reduce readmissions was implemented on June 1 2012, when the Specialist Services Committee of British Columbia (a partnership of Doctors of BC and the Ministry of Health) introduced the new "G78717" fee code for physicians. The objective of the fee code was to create a financial incentive for physicians to provide a point-of-care supplemental discharge summary to patients and their primary care providers prior to discharge from hospital. Initially, only urgent hospital admissions were eligible for this incentive payment but on Nov 1 2015 the incentive was extended to include elective admissions as well. The other eligibility criteria remained unchanged. The effectiveness and cost-effectiveness of the fee code intervention is unknown. This study will address important questions relevant to this policy intervention using rigorous methods and empirical data. This study will employ two methods for measuring changes in readmission risk. First, we will use interrupted (multivariate) time series to measure whether there was a temporal change in provincial readmission risk associated with the implementation of the new fee code. We will complement the above analyses with a stronger design, comparing hospitalizations for which the fee code was charged (intervention group) with a cohort of clinically similar hospitalizations for which the fee code was not charged (control group). For this approach, multivariate logistic regression will be the primary statistical method. Using this analytic strategy, 30-day readmission risk between the intervention and control group will be measured over time, adjusted for patient-, provider-, and hospitalization-level covariates.