AIMC Topic: Length of Stay

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Leveraging Artificial Intelligence to Reduce Neuroscience ICU Length of Stay.

Journal of healthcare management / American College of Healthcare Executives
GOAL: Efficient patient flow is critical at Tampa General Hospital (TGH), a large academic tertiary care center and safety net hospital with more than 50,000 discharges and 30,000 surgical procedures per year. TGH collaborated with GE HealthCare Comm...

Developing a Novel Artificial Intelligence Framework to Measure the Balance of Clinical Versus Nonclinical Influences on Posthepatectomy Length of Stay.

Annals of surgical oncology
BACKGROUND: Length of stay (LOS) is a key indicator of posthepatectomy care quality. While clinical factors influencing LOS are identified, the balance between clinical and nonclinical influences remains unquantified. We developed an artificial intel...

A novel artificial intelligence framework to quantify the impact of clinical compared with nonclinical influences on postoperative length of stay.

Surgery
BACKGROUND: The relative proportion of clinical compared with nonclinical influences on length of stay after colectomy has never been measured. We developed a novel machine-learning framework that quantifies the proportion of length of stay after col...

Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model.

BMC oral health
BACKGROUND: Postoperative fever (POF) is a common occurrence in patients undergoing major surgery, presenting challenges and burdens for both patients and surgeons yet. This study endeavors to examine the incidence, identify risk factors, and establi...

Development and Validation of Machine Learning-Based Model for Hospital Length of Stay in Patients Undergoing Endovascular Interventional Embolization for Intracranial Aneurysms.

World neurosurgery
OBJECTIVE: This study was to explore the factors associated with prolonged hospital length of stay (LOS) in patients with intracranial aneurysms (IAs) undergoing endovascular interventional embolization and construct prediction model machine learning...

Use of artificial intelligence to study the hospitalization of women undergoing caesarean section.

BMC public health
OBJECTIVE: The incidence of caesarean sections (CSs) has increased significantly in recent years, especially in developed countries. This study aimed to identify the factors that most influence the length of hospital stay (LOS) after a CS, using data...

Developing a decision support tool to predict delayed discharge from hospitals using machine learning.

BMC health services research
BACKGROUND: The growing demand for healthcare services challenges patient flow management in health systems. Alternative Level of Care (ALC) patients who no longer need acute care yet face discharge barriers contribute to prolonged stays and hospital...

The Adelaide Score: prospective implementation of an artificial intelligence system to improve hospital and cost efficiency.

ANZ journal of surgery
BACKGROUND: The Adelaide Score is an artificial intelligence system that integrates objective vital signs and laboratory tests to predict likelihood of hospital discharge.