We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




AI-Based System to Recommend Clinical Treatments for Sepsis Patients in ICU

By HospiMedica International staff writers
Posted on 01 May 2023
Print article
Researchers have developed an AI-based system to recommend clinical treatments (Photo courtesy of Freepik)
Researchers have developed an AI-based system to recommend clinical treatments (Photo courtesy of Freepik)

In an intensive care unit (ICU), clinicians must make complex decisions quickly and accurately, constantly monitoring critically ill or unstable patients. Researchers have now developed an artificial intelligence (AI)-based system to aid physicians in making decisions within the ICU.

Researchers from Carnegie Mellon University (Pittsburgh, PA, USA) collaborated with physicians and other researchers to explore whether AI could assist in decision-making and whether clinicians would trust such support. The team provided 24 ICU physicians with access to an AI-based tool designed to aid decision-making and found that most integrated the assistance into some of their decisions. Using the 2018 AI Clinician model, they developed an interactive clinical decision support (CDS) interface—named AI Clinician Explorer—that offers recommendations for treating sepsis. The model was trained on a data set of over 18,000 patients who met standard diagnostic criteria for sepsis during their ICU stays. The system allows clinical experts to filter and search for patients in the data set, visualize their disease trajectories, and compare the model predictions to actual bedside treatment decisions.

The team conducted a think-aloud study with 24 ICU clinicians experienced in sepsis treatment, having them use a simplified AI Clinician Explorer interface to assess and make treatment decisions for four simulated patient cases. The team observed four common behaviors among the clinicians: ignore, rely, consider, and negotiate. The "ignore" group disregarded the AI's influence, while the "rely" group consistently accepted at least part of the AI's input. The "consider" group contemplated the AI's recommendation before accepting or rejecting it. Most participants belonged to the "negotiate" group, accepting individual aspects of the recommendations in at least one decision, but not all.

The team found the results surprising and gained insights on how to improve the AI Clinician Explorer. Clinicians expressed concerns about the AI lacking access to more holistic data, such as the patient's general appearance, and were skeptical when the AI made recommendations contrary to their training. The research aims not to replace or replicate clinician decision-making, but to use AI to reveal patterns that may have been previously overlooked in patient outcomes.

"It feels like clinicians are excited about the potential for AI to help them, but they might not be familiar with how these AI tools would work. So it's really interesting to bring these systems to them," said Venkatesh Sivaraman, a Ph.D. student in the HCII and member of the research team. "There are a lot of diseases, like sepsis, that might present very differently for each patient, and the best course of action might be different depending on that. It's impossible for any one human to amass all that knowledge to know how to do things best in every situation. So maybe AI can nudge them in a direction they hadn't considered or help validate what they consider the best course of action."

Gold Supplier
Temperature Monitor
ThermoScan Temperature Monitoring Unit
New
Silver Supplier
Patient Simulator
PatSim 200
New
Patient Positioning Devices & OR Accessories
SchureMed Tools
New
IV Medication Safety Software
ICU Medical MedNet IV

Print article
FIME - Informa

Channels

AI

view channel
Image: The AI tool can also tackle dangerous inequalities in heart attack diagnosis (Photo courtesy of Freepik)

AI Algorithm Integrates Cardiac Troponin Test Results with Clinical Data to Quickly Rule out Heart Attacks in Patients

The accepted standard for diagnosing myocardial infarction, or heart attack, involves assessing the blood for troponin levels. However, this approach applies the same benchmark for all patients, failing... Read more

Surgical Techniques

view channel
Image: RefluxStop treats acid reflux without affecting the food passageway (Photo courtesy of Implantica AG)

Breakthrough Implant Marks Paradigm Shift in Treatment of Gastroesophageal Reflux

Acid reflux, a condition characterized by the regurgitation of the stomach's acid content into the esophagus, affects approximately 400 million individuals daily, making it the second-largest treatment... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: VCM viscoelastic testing instrument provides rapid, real-time hemostasis assessment at POC (Photo courtesy of Entegrion)

Next Gen Viscoelastic Coagulation Monitor Enables Rapid Hemostasis Assessment at Patient Side

The use of viscoelastic coagulation testing is on the rise for various applications such as trauma, surgery, obstetrics, major disease management, and more. It provides crucial information not obtained... Read more
Copyright © 2000-2023 Globetech Media. All rights reserved.