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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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Ethical Dilemmas II

Resolving an ethical dilemma in healthcare involves a systematic approach that considers every aspect of the issue, respecting both the patient's needs and values and the healthcare professional's ethical obligations. Here are potential steps to resolve an ethical dilemma:

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Related Experiment Video

Updated: Jun 19, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Improving End-of-Life Screening in the Emergency Department With Collaborative Artificial Intelligence.

Adrian D Haimovich1, Gabriel Erion-Barner1, Larry A Nathanson1

  • 1Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA.

Annals of Emergency Medicine
|June 18, 2026
PubMed
Summary
This summary is machine-generated.

The Geriatric End-of-Life Screening Tool (GEST) AI model showed improved mortality prediction in older emergency department patients compared to the surprise question (SQ). A combined GEST+SQ model enhanced calibration, potentially reducing physician screening burden.

Keywords:
Artificial intelligenceEnd-of-lifeGeriatricMachine learningPalliative

Related Experiment Videos

Last Updated: Jun 19, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

  • Geriatric Medicine
  • Emergency Medicine
  • Artificial Intelligence in Healthcare
  • Predictive Analytics

Background:

  • Accurate end-of-life (EOL) mortality prediction is crucial for older emergency department (ED) patients.
  • Physician-answered surprise question (SQ) and artificial intelligence (AI) models like the Geriatric End-of-Life Screening Tool (GEST) are used for EOL predictions.
  • The efficacy of combining these tools for improved prediction accuracy and reduced clinician burden requires investigation.

Purpose of the Study:

  • To compare the performance of the SQ, GEST AI model, and a novel collaborative GEST+SQ model in predicting 6-month mortality in older ED patients.
  • To evaluate the discrimination and calibration of the GEST+SQ model compared to GEST alone.
  • To assess the potential of a sequential screening pathway using GEST and SQ to reduce physician workload.

Main Methods:

  • A prospective cohort study involving patients aged 65 years and older in a tertiary academic ED.
  • Data collected included SQ responses, GEST scores derived from electronic health records, and adjudicated 6-month mortality.
  • Statistical analyses involved comparing sensitivity, specificity, receiver-operating characteristic - area under the curve (ROC-AUC), and expected calibration error for GEST, SQ, and the GEST+SQ model.

Main Results:

  • GEST demonstrated higher sensitivity and specificity than SQ for predicting 6-month mortality.
  • The GEST+SQ model showed a non-significant improvement in discrimination (ROC-AUC 0.80) compared to GEST alone (ROC-AUC 0.79) but significantly improved calibration.
  • A sequential screening pathway utilizing GEST followed by SQ for intermediate-risk patients could decrease physician screening burden by 95%.

Conclusions:

  • The GEST AI model modestly outperformed the SQ in predicting 6-month mortality among older ED patients.
  • The collaborative GEST+SQ model enhanced prediction calibration and offers a potential strategy to reduce clinician screening burden.
  • Integrating automated tools with targeted physician input represents a promising approach for optimizing ED mortality risk assessment.