Clinical Research Directory

A Study of the Effect of a Nurse Navigator Program on High Risk Patients

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.

Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data

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. This 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. This 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. Participants 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. The 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. If successful, this study may improve postoperative care, support earlier clinical intervention, reduce complications, and help healthcare teams provide safer recovery pathways for surgical patients.

Efficiency of the "Medidux" Smartphone App for Demission Management in Patients Medicated in Acute Admission Unit (AAU)

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. The main question it aims to answer is: Can the medidux™ app reduce the incidence of AAU readmissions, emergency hospitalizations, or consultations with other medical providers within 7 days after initial admission? Researchers 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. Participants will: * use the medidux™ app to monitor their symptoms and vital parameters for 7 days after discharge (intervention arm). * receive follow-up consultations at day 7 and at day 28 to assess symptom progression and any healthcare interactions (both arms).