HOSPITAL

Hospital readmissions create challenges for healthcare systems, increasing costs and putting pressure on resources. This project introduces a machine learning-based system designed to predict patient readmissions, helping medical personnel and hospitals take early action to improve patient care and manage resources more effectively. By analyzing electronic health record (EHR) data, the model assesses a patient’s risk and provides explanations for key factors influencing the prediction. The system was trained on a dataset containing patient details such as age, medical history, lab results, and past hospital visits. It was developed using Python for machine learning, Express.js for the backend, and TypeScript with React for the frontend, ensuring smooth data processing and an easy-to-use interface. Strong security features like authentication, encryption, and error handling were added to protect patient information. The result shows that the model was able to achieve 63.87% accuracy, with recall scores of 72% and 55% in different areas. These results highlight the model’s ability to predict readmissions while also showing areas where improvements can be madethrough better data processing and tuning. By using predictive analytics, this system helps healthcare professionals make informed decisions, reduce avoidable readmissions, and improve hospital efficiency. This project demonstrates how AI-powered solutions can transform healthcare by enabling proactive patient management and better decision-making.

This study examines the talent management and organisational performance; a case study of selected hospital in Benin city, with a focus on how leadership styles, administrative competence, and decision-making processes influence employee performance and organizational effectiveness. The research investigates the extent to which leadership practices such as communication, motivation, delegation of duties, and supervision contribute to the productivity levels within the local government system. Using a descriptive research design, data were collected through structured questionnaires administered to employees across various departments, and were analyzed using simple percentage and frequency distribution

Abdominal pain is one of the most frequent presenting complaints in emergency departments (EDs), with a wide differential diagnosis ranging from benign to life-threatening conditions. These conditions include gastrointestinal pathologies such as appendicitis, peptic ulcers, and bowel obstructions, as well as non-gastrointestinal disorders like myocardial infarction or aortic aneurysm rupture. The aim of this study was to evaluate evaluate the common findings of emergency abdominal x-rays at university of Benin teaching hospital. A retrospective design was adopted and a convenience sampling was used to select 200 sample size. The results revealed that abdominal pain (30.0%), and bowel obstruction (26.0%), were the most common clinical signs. Normal study (40.0%) was the most common radiography appearance, and the most common abnormal results were dilated bowel loops and pneumoperitoneum. Demographically, young adults (20-39 years old) constituted the largest percentage (52.0) with majority of them being females (58.0) as compared to the males. The chi-square showed that there was no statistically significant relationship between age or gender and radiography findings. The conclusion of the findings was that the most common requests of an emergency abdominal X-ray at UBTH are related to patients presenting with abdominal pain with suspected bowel obstruction. Despite a large percentage of tests with normal outcomes, the modality is still applicable in the diagnosis of bowel obstruction, pneumoperitoneum, and foreign bodies.