[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"patient-readmission\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:patient-readmission":26},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,3,0,[8,47,79],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":18,"targetDuration":4,"studyType":21,"phases":22,"briefSummary":24,"conditions":25,"keywords":4,"overallStatus":34,"whyStopped":4,"lastUpdateSubmitDate":35,"lastUpdatePostDateStruct":36,"startDateStruct":39,"completionDateStruct":41,"leadSponsor":43,"locationsCount":46},"100484806","a-study-of-the-effect-of-a-nurse-navigator-program-on-high-risk-patients-100484806",false,"NCT05592847","A Study of the Effect of a Nurse Navigator Program on High Risk Patients","The Effect of a Nurse Navigator Program on Readmission Rates for Patients With Elevated BMI, COPD, CHF, Dialysis Use, and H\u002Fo Alcohol Abuse","Inclusion Criteria:\n\n\\- Require total or partial hip or knee replacement and have one or more of the following diagnosis: Heart Failure (HF); Chronic obstructive pulmonary disease (COPD); Dialysis; Alcohol Abuse; Low BMI.\n\nExclusion Criteria:\n\n\\- Decrease cognitive capacity to consent to the study.","ALL","18 Years",{"count":19,"type":20},300,"ESTIMATED","INTERVENTIONAL",[23],"NA","The purpose of this study is to examine if educational intervention in high risk patients can lead to decreased hospital readmissions when compared to patients who are not in the intervention program. Additionally, to determine patient satisfaction with the educational program.",[26,27,28,29,30,31,32,33],"Patient Readmission","Arthroplasty, Replacement, Knee","Arthroplasty, Replacement, Hip","Heart Failure","Pulmonary Disease, Chronic Obstructive","Renal Insufficiency","Body Mass Index","Alcoholism","NOT_YET_RECRUITING","2026-03-17",{"date":37,"type":38},"2026-03-19","ACTUAL",{"date":40,"type":20},"2027-01",{"date":42,"type":20},"2027-06",{"name":44,"class":45},"Mayo Clinic","OTHER",1,{"id":48,"slug":49,"hasResults":11,"nctId":50,"briefTitle":51,"officialTitle":51,"acronym":4,"eligibilityCriteria":52,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":53,"targetDuration":4,"studyType":54,"phases":4,"briefSummary":55,"conditions":56,"keywords":57,"overallStatus":69,"whyStopped":4,"lastUpdateSubmitDate":70,"lastUpdatePostDateStruct":71,"startDateStruct":72,"completionDateStruct":74,"leadSponsor":76,"locationsCount":46},"100619844","predicting-hospital-readmission-for-surgical-patients-using-deep-learning-models-with-smart-watch-and-smart-ring-sensors-data-100619844","NCT07349901","Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data","Inclusion Criteria:\n\n* Adults over 18 years of age;\n* Hospitalization for medium and\u002For large elective surgery at HUGV;\n* Conscious and oriented patients who have sufficient understanding to answer questionnaires and use wearable devices for the study;\n* Have minimal skills in the use of wearable technologies;\n* Patients who have agreed to participate voluntarily in the research by signing the Informed Consent Form (ICF).\n\nNon-inclusion Criteria:\n\n* Presence of tattoos or any other skin condition (skin pathologies or skin diseases such as vitiligo, lupus, and atopic dermatitis, among others) that affects the area of the wrist or finger where the wearable sensors are located;\n* Presence of any type of sensitivity or allergic reaction, of any degree, to the materials of the wearables (smartwatch and smartring);\n* Pregnant and lactating women;\n* Participants with implantable cardiac devices, such as pacemakers, cardioverter defibrillators, and resynchronization devices;\n* Participants in drugs abuse;\n\nExclusion Criteria:\n\n* Severe medical conditions and decompensations prior to surgery;\n* Patients who die before hospital discharge;\n* Patients with an expected postoperative hospital stay of more than 10 days.",{"count":19,"type":20},"OBSERVATIONAL","Hospital readmissions are an important measure of healthcare quality and safety. These events create a substantial burden for patients, families, and health systems because they may increase costs, extend recovery time, and lead to more serious postoperative complications. Predicting which patients are at higher risk of readmission remains difficult, as many complications begin silently and are not easily identified in routine clinical evaluations.\n\nThis study aims to evaluate whether artificial intelligence (AI) can help predict hospital readmissions in surgical patients by analyzing physiological and behavioral data collected before and after surgery. To achieve this, participants will use wearable devices-specifically a smartwatch and a smart ring-capable of continuously monitoring health biomarkers such as heart rate, electrocardiogram (ECG), oxygen saturation, sleep patterns, blood pressure trends, body composition through bioimpedance, and stress indicators. These devices are provided through a technology partnership and sponsorship from Samsung, which supports the study with advanced health technologies.\n\nThis is a prospective, single-center cohort study conducted at the main tertiary hospital in the state of Amazonas. Approximately 225 to 300 adults undergoing medium- or large-scale elective surgeries will be invited to participate over a 25-month period. All participants will provide informed consent. After enrollment, the study will collect demographic information, preoperative assessments, validated sleep questionnaires, comorbidity indexes such as the Charlson Comorbidity Index, laboratory exams, pulmonary function tests, intraoperative and postoperative data, and hospital discharge information.\n\nParticipants will be continuously monitored using wearable devices during their hospital stay-including the first 48 hours in the intensive care unit when applicable-and for 30 days after hospital discharge. These physiological data will be integrated with clinical and laboratory information to create a comprehensive dataset.\n\nThe primary objective is to develop and test artificial intelligence models capable of predicting 30-day hospital readmission following elective surgery. Both deep learning approaches and classical machine-learning techniques will be evaluated. By analyzing large volumes of continuous physiologic data, these models may identify early signs of postoperative deterioration that would otherwise go unnoticed.\n\nIf successful, this study may improve postoperative care, support earlier clinical intervention, reduce complications, and help healthcare teams provide safer recovery pathways for surgical patients.",[26],[58,59,60,61,62,63,64,65,66,67,68],"Hospital Readmission","Postoperative Complications","Artificial Intelligence","Machine Learning","Wearable Electronic Devices","Physiological Monitoring","Sleep Disorders","Surgical Procedures, Operative","Perioperative Care","Amazon Region","Predictive Modeling","RECRUITING","2026-03-14",{"date":35,"type":38},{"date":73,"type":38},"2026-03-04",{"date":75,"type":20},"2028-06",{"name":77,"class":78},"Getúlio Vargas University Hospital","OTHER_GOV",{"id":80,"slug":81,"hasResults":11,"nctId":82,"briefTitle":83,"officialTitle":84,"acronym":85,"eligibilityCriteria":86,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":87,"targetDuration":4,"studyType":21,"phases":89,"briefSummary":90,"conditions":91,"keywords":101,"overallStatus":69,"whyStopped":4,"lastUpdateSubmitDate":110,"lastUpdatePostDateStruct":111,"startDateStruct":113,"completionDateStruct":115,"leadSponsor":117,"locationsCount":46},"100566450","efficiency-of-the-medidux-smartphone-app-for-demission-management-in-patients-medicated-in-acute-admission-unit-aau-100566450","NCT06655337","Efficiency of the \"Medidux\" Smartphone App for Demission Management in Patients Medicated in Acute Admission Unit (AAU)","Efficiency of the \"Medidux\" Smartphone App for Demission Management in Patients Medicated in Acute Admission Unit (AAU): a Randomized Controlled Trial.","EffiDux","Inclusion Criteria:\n\n* Signed Informed Consent Form (ICF)\n* Age ≥ 18 years\n* (Self-)admission to the involved Acute Admissions Unit (AAU)\n* \"Lead symptom\" identified as coughing, back pain or abdominal discomfort\n* Participant within ESI triage system group 4 (standard) and 5 (non-urgent)\n* German-speaking\n* Ownership of a smartphone or other mobile device with iOS or Android operating system\n\nExclusion Criteria:\n\n* Age \\\u003C 18 years\n* Participant whose compliance to the study's protocol, e.g. due to mental health problems, physical problems, or the private life situation, can be justifiably doubted\n* Participant with insufficient knowledge about the use of a smartphone or other mobile device with iOS or Android operating system\n* Participant already using or planning to use another comparable electronic patient-reported symptom monitoring system (e.g. CANKADO) during this trial",{"count":88,"type":20},417,[23],"The goal of this clinical trial is to evaluate whether the use of the medidux™ smartphone app can optimize post-discharge management for patients admitted to Acute Admission Units (AAU) with non-urgent health complaints. This trial includes adult patients (age ≥ 18) in Emergency Severity Index (ESI) triage system groups 4 (standard) or 5 (non-urgent), presenting with primary symptoms such as cough, back pain, or abdominal discomfort.\n\nThe main question it aims to answer is:\n\nCan the medidux™ app reduce the incidence of AAU readmissions, emergency hospitalizations, or consultations with other medical providers within 7 days after initial admission?\n\nResearchers will compare participants using the medidux™ app (intervention arm) with those receiving standard care (control arm) to observe potential differences in the rates of readmissions, emergency hospitalizations, and medical consultations.\n\nParticipants will:\n\n* use the medidux™ app to monitor their symptoms and vital parameters for 7 days after discharge (intervention arm).\n* receive follow-up consultations at day 7 and at day 28 to assess symptom progression and any healthcare interactions (both arms).",[92,93,94,95,96,97,26,98,99,100],"Acute Disease","Acute Hospitalization","Coughing","Back Pain","Abdominal Pain (AP)","Patient Discharge","Mobile Health Apps","Mobile Health Technology (mHealth)","Patient Reported Outcome (PRO)",[92,97,26,94,95,102,103,104,105,106,107,108,109],"Abdominal Pain","Mobile Health Applications","Remote Monitoring","Post-discharge Care","Symptom Monitoring","Health Behaviour","Telemedicine","Randomized Controlled Trial","2025-09-29",{"date":112,"type":38},"2025-10-03",{"date":114,"type":38},"2025-09-28",{"date":116,"type":20},"2027-03-28",{"name":118,"class":119},"Mobile Health AG","INDUSTRY"]