Related Experiment Video
Updated: May 3, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Developing a strategy to identify and treat older patients with postoperative delirium
Abstract:
Postoperative delirium is one of the most common adverse outcomes in elderly patients undergoing surgery and is associated with increased morbidity, length of stay, and patient care costs. The purpose of this quality improvement project was to evaluate the effectiveness of a multicomponent strategy to identify and treat general surgical patients 65 years of age or older at risk for and who develop postoperative delirium at Cape Cod Hospital, a community hospital in southern New England. We evaluated 96 patients using the Mini-Cog assessment tool preoperatively and the Confusion Assessment Method (CAM) delirium screening tool or CAM-Intensive Care Unit (CAM-ICU) assessment tool postoperatively. Patients who tested positive during preoperative assessment underwent a postoperative delirium management protocol. We summarized data using descriptive statistics. The results showed an association between compliance and outcomes. High compliance with implementation of CAM and CAM-ICU assessment tools resulted in increased identification of postoperative delirium in the older surgical population. The use of screening tools helped facilitate early identification of postoperative delirium in elderly surgical patients.
Related Concept Videos
Drug Dosing: Geriatric Patients
Planning Nursing Care I
Ethical Dilemmas II
Alzheimer's Disease: Treatment
Acute Coronary Syndrome IV: Interprofessional Care
Pharmacodynamics in Geriatric Patients: Effects of Age