AIMC Topic: Academic Medical Centers
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Using Optimal Feature Selection and Continuous Learning to Implement Efficient Model Arrays for Predicting Daily Clinical Radiology Workload.
Academic radiology
Mar 25, 2025
RATIONALE AND OBJECTIVE: Clinical workload can fluctuate daily in radiology practice. We sought to design, validate, and implement an efficient and sustainable machine learning model to forecast daily clinical image interpretation workload.
A Neurosurgical Readmissions Reduction Program in an Academic Hospital Leveraging Machine Learning, Workflow Analysis, and Simulation.
Applied clinical informatics
Jun 19, 2024
BACKGROUND: Predicting 30-day hospital readmissions is crucial for improving patient outcomes, optimizing resource allocation, and achieving financial savings. Existing studies reporting the development of machine learning (ML) models predictive of ...
Understanding the integration of artificial intelligence in healthcare organisations and systems through the NASSS framework: a qualitative study in a leading Canadian academic centre.
BMC health services research
Jun 3, 2024
BACKGROUND: Artificial intelligence (AI) technologies are expected to "revolutionise" healthcare. However, despite their promises, their integration within healthcare organisations and systems remains limited. The objective of this study is to explor...
Artificial Intelligence-Generated Draft Replies to Patient Inbox Messages.
JAMA network open
Mar 4, 2024
IMPORTANCE: The emergence and promise of generative artificial intelligence (AI) represent a turning point for health care. Rigorous evaluation of generative AI deployment in clinical practice is needed to inform strategic decision-making.
Operative Team Familiarity and Specialization at an Academic Medical Center.
Annals of surgery
Jul 6, 2022
OBJECTIVE: To propose a framework for quantification of surgical team familiarity.
Development and Validation of an Artificial Intelligence System to Optimize Clinician Review of Patient Records.
JAMA network open
Jul 1, 2021
IMPORTANCE: Physicians are required to work with rapidly growing amounts of medical data. Approximately 62% of time per patient is devoted to reviewing electronic health records (EHRs), with clinical data review being the most time-consuming portion.
The use of a UV-C disinfection robot in the routine cleaning process: a field study in an Academic hospital.
Antimicrobial resistance and infection control
May 29, 2021
BACKGROUND: Environmental surface decontamination is a crucial tool to prevent the spread of infections in hospitals. However, manual cleaning and disinfection may be insufficient to eliminate pathogens from contaminated surfaces. Ultraviolet-C (UV-C...
Experience with 10 years of a robotic surgery program at an Academic Medical Center.
Surgical endoscopy
Apr 12, 2021
BACKGROUND: Few studies have examined robotic surgery from a programmatic standpoint, yet this is how hospitals evaluate return on investment clinically and fiscally. This study examines the 10-year experience of a robotic program at a single academi...
Personalized Risk Prediction for 30-Day Readmissions With Venous Thromboembolism Using Machine Learning.
Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
Feb 22, 2021
PURPOSE: The aim of the study was to develop and validate machine learning models to predict the personalized risk for 30-day readmission with venous thromboembolism (VTE).
Application of Machine Learning to Predict Patient No-Shows in an Academic Pediatric Ophthalmology Clinic.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Patient "no-shows" are missed appointments resulting in clinical inefficiencies, revenue loss, and discontinuity of care. Using secondary electronic health record (EHR) data, we used machine learning to predict patient no-shows in follow-up and new p...
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