View clinical trials related to Readmission.
Filter by:Background: Bleeding through the staple line has been reported as one of the most common staple-related complications in laparoscopic sleeve gastrectomy (LSG). In this study, we aimed to compare the effects of clips and omentopexy techniques on postoperative bleeding and readmissions during the first 30-days following surgery. Methods: In this prospective randomized controlled study, patients were divided into two groups: clips group and omentopexy group. The groups were compared in terms of postoperative decrease in hemoglobin and hematocrit values, preoperative and peroperative blood pressure values, duration of surgery, number of patients requiring erythrocyte suspension (ES) transfusion, length of hospital stay, hospital readmissions in the first 30-days postoperatively and early postoperative complications.
Single center retrospective cohort study of all inpatient endoscopy procedures to asses factors associated with inpatient endoscopy delays and impact on length of stay and 30-day readmission
Lower respiratory tract infection(LRTI) is a prevalent disease that threatens the health of older people worldwide. Anemia is also a common disorder in the elderly, and its prevalence increases significantly with age. Most factors that contribute to the development of anemia are improvable. Therefore, we investigated whether anemia was a risk factor for LRTI-caused readmission and death in the elderly occurring within 1 year of discharge from the hospital.
The aims of this study is to integrate real-time data flow infrastructure between hospital information system and AI models and to conduct a cluster randomized crossover trial to evaluate the efficacy of the AI models in improving patient flow and relieving ED crowding.
We need to identify the essential factors that are linked to readmission among older medical patients as approximately 20% of all medical patients above the age of 65 are readmitted within 30 days after discharge. The objective of this cross-sectional survey study is to to identify factors and aspects that contribute to unplanned hospital readmissions of older medical patients. This will be done through a survey where readmitted patients and their relatives and healthcare professionals answer questions about the patients readmission. The survey questions cover following themes: 1) disease, 2) diagnostics, treatment and care, 3) social network, 4) organisation, 5) communication, 6) competences and knowledge, 7) resources and 8) practical aspects. The hypothesis is that the more knowledge we gain on factors that contribute to readmission, the more targeted actions and interventions and thus preventing readmissions.
Using weight training with virtual reality can help after discharge patients of acute exacerbation of chronic obstructive pulmonary disease, which maintained their quality of life, and improved their exercise capacity, pulmonary function, readmission condition.
The Study is a feasibility randomized controlled trial aiming to assess the feasibility of mHealth (voice call and SMS) and teach-back interventions on reducing the 30 days readmission rate in the patients enrolled in the Sehat Sahulat Programme (Prime Minister National Health Programme(PMNHP)). The prime objective of this study was to generate a proof of concept for the conduct of a definitive trial for the reduction in readmissions in PMNHP. A feasibility randomized controlled trial study consisted of three arms i.e intervention 1 (telephonic contact and text messages), intervention 2 (teach-back method) and control is planned in program beneficiaries of Islamabad, Pakistan. The trial is being carried out in the three hospitals of Islamabad and patients are being recruited as per the inclusion and exclusion criteria.
This randomized trial will study the effect of a mobile app that facilitates patient engagement (patients undergoing complex abdominal surgery will track metrics of interest to the surgeon, submit reports on their symptoms/pain/physical function, and upload wound images) on readmission to the hospital. This trial will also assess whether the app can impact surgical complication severity, number of emergency department visits, and readmission costs. 300 participants will be enrolled and can expect to be on study for 6 months.
The first person-to-person Coronavirus disease (COVID-19) transmission in Italy was reported on Feb 21st, 2020, causing one of the most massive outbreak in Europe so far that stopped immediately all elective surgical procedures. Bariatric surgery represents the most effective treatment to obtain an important, long-term weight loss and comorbidities' resolution, including respiratory disorders. A sensitive decrease of epidemic has been observed lately and a gradual and progressive stop of the lockdown (phase 2-3) was planned, when the virus is supposed to be under control and protocols are guiding the restart of the elective bariatric surgery. Several questions are currently open: Laparoscopic bariatric surgery is safe in the phase 2-3? What's the expected complications rate? The actual hospital protocols are effective to minimize the risk of postoperative COVID-19 infection? Aim: to analyse results of bariatric surgery during phase 2-3 COVID-19 pandemic in Italy. Primary end point: 30 days COVID-19 infection, mortality and complications. Secondary end points: readmission rate 30 days, reoperations for any reason related to surgery. Study design: prospective multicenter observational. Setting: Italian National Health Service 8 high-volume bariatric centres. Enrollment criteria: No previous Covid-19 infection; Primary, standard IFSO approved bariatric procedures; No concomitant procedure; No previous major abdominal surgery; >18<60 years old; Compensated comorbidities; Official SICOB's surgical informed consent given, including COVID-19 addendum; Adherence to very restrictive protocols regarding: hospital admission, management of in-hospital patients and after discharge. Follow-up: scheduled outpatient visit 30th postoperative day. Data evaluation: all the cases performed during July/December 2020 will be collected in a prospective database. Patients operated during the period July/December 2019 in the same centers will be considered comparative group (control). Expected results: Transparent information to the patients, and the introduction of the COVID-19 protocol concerning patients and health-professionals protection, should guarantee a safe restart of bariatric surgery in Italy. The network of 8 high-volume centers sharing information and protocols in this "unexplored" period will be a guarantee for patients' safety. Bariatric surgery should induce a postoperative amelioration of the comorbidities reducing the risks in case of a second outbreak.
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.