Patient Safety

Vanderbilt, Duke Awarded $1.25M to Improve Health Systems’ AI Oversight

November 13, 2023 - Researchers from Vanderbilt University Medical Center (VUMC) and Duke University School of Medicine have been awarded a $1.25 million grant from the Gordon and Betty Moore Foundation to establish a framework to improve oversight of health systems’ artificial intelligence (AI) tools. The project, titled “Measuring Artificial Intelligence...


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Exploring the Role of Artificial Intelligence in Anesthesiology

by Shania Kennedy

In anesthesiology, as in all medical specialties, clinicians strive to support patient safety and improve outcomes. Some anesthesiology professionals are investigating how advanced technologies like artificial intelligence (AI) and machine...

Majority of Americans Would Not Rely on AI-Based Healthcare

by Mark Melchionna

A recent survey from Pew Research Center found that although there are certain areas where patients see the benefits of artificial intelligence (AI) use in healthcare, the majority are skeptical due to...

UC Irvine Incorporates Natural Language Processing Into Data Science Platform

by Shania Kennedy

The University of California, Irvine (UCI) has partnered with artificial intelligence (AI) software company Melax Tech to enable UCI researchers to analyze EHR data using natural language processing...

TN Health System Strikes Analytics Partnership to Advance Patient Care

by Shania Kennedy

Tennessee-based LifePoint Health has launched a strategic collaboration with data and analytics technology company Health Catalyst to advance patient care and improve outcomes across the health...

What Providers Can Do to Minimize AI-Based Image Reconstruction Risks

by Anuja Vaidya

Artificial intelligence is increasingly being used to reconstruct images from data obtained during magnetic resonance imaging, computerized tomography, or other types of scans. While AI has been shown to improve the quality of scans and...

Deep Learning Detects Allergic Reactions in Patient Safety Reports

by Jessica Kent

A deep learning algorithm accurately identified allergic reactions in hospital patient safety reports, which could help providers avoid medical errors and improve event surveillance, according to a...

Patient Safety, Data Privacy Key for Use of AI-Powered Chatbots

by Jessica Kent

Patient safety, data privacy, and health equity are key considerations for the use of chatbots powered by artificial intelligence in healthcare, according to a viewpoint piece published in JAMA. With...

ACR, RSNA Caution FDA Against Autonomous AI in Medical Imaging

by Jessica Kent

Despite the promise of artificial intelligence in medical imaging, the FDA currently cannot ensure the safety and efficacy of automated AI in the imaging field, according to comments from the American...

Using Automated Risk Scoring to Spot Pediatric Patient Deterioration

by Jessica Kent

In medicine, risk scoring is one of the best methods providers can use to anticipate and prepare for an adverse event. Using patient data and analytics tools, clinicians can determine the likelihood...

New Algorithm Tracks Sepsis Incidence Among Pediatric Patients

by Jessica Kent

Researchers at Children’s Hospital of Philadelphia (CHOP) have developed a new algorithm that can track the epidemiology of sepsis among pediatric patients, allowing for more accurate data...

How Clinical Decision Support Adherence Leads to Better Patient Data

by Jessica Kent

As the volume of healthcare data increases and the shift to value-based care accelerates, clinical decision support tools are becoming more and more critical to ensure quality care delivery. From...

Deep Learning Model Identifies Drug to Combat Antibiotic Resistance

by Jessica Kent

A deep learning algorithm has identified a new drug that kills many of the world’s most challenging disease-causing bacteria, including some strains of antibiotic-resistant bacteria. Researchers...

Poor Data Quality, Weak Algorithms Lead to Patient Matching Issues

by Jessica Kent

Hospitals and health information exchanges (HIEs) still struggle with patient matching issues, with many citing data quality problems and poor algorithms as top barriers to patient matching, according...

Machine Learning System Accurately Identifies Medication Errors

by Jessica Kent

An alert system driven by machine learning could identify medication errors that traditional clinical decision support systems might otherwise miss, according to a study published in the Joint...

Geisinger Advances in CMS Artificial Intelligence Challenge

by Jessica Kent

CMS has selected Geisinger and EarlySign, a machine learning company, for their joint proposal in the agency’s Artificial Intelligence Health Outcomes Challenge. The partnering organizations are...

10 High-Value Use Cases for Predictive Analytics in Healthcare

by Jennifer Bresnick

As healthcare organizations develop more sophisticated big data analytics capabilities, they are beginning to move from basic descriptive analytics towards the realm of predictive insights. Predictive...

Using Big Data Analytics for Patient Safety, Hospital Acquired Conditions

by Jennifer Bresnick

Prediction and prevention are the two main goals for patient safety experts seeking to avoid adverse events and reduce the prevalence of hospital acquired conditions (HACs).   While workflow strategies, staff training, and human...

Will Clinical Decision Support, Health IT Cut Diagnostic Errors?

by Jennifer Bresnick

Healthcare professionals are continually being asked to do more with less.  With more patients but less time; more data but fewer meaningful tools to help them process it, it seems almost...

Understanding the Basics of Clinical Decision Support Systems

by Jennifer Bresnick

Clinical decision support systems are quickly becoming essential tools for healthcare providers as the volume of available data increases alongside their responsibility to deliver value-based care. Reducing clinical variation and...