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Related Experiment Video

Updated: Jul 5, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Evaluation and Comparison of Latent Health Risk Prediction Models for Clinical Triage: Protocol for a Mixed Methods

Morgan Roberts1, Otso Pelkonen, Diana Shamsutdinova2

  • 1Emergency Department, Bristol Royal Infirmary, Bristol, United Kingdom.

JMIR Research Protocols
|July 3, 2026
PubMed
Summary

This study compares two AI tools for predicting patient deterioration, finding their alignment with clinical judgment is key for effective triage and AI implementation in healthcare.

Keywords:
artificial intelligenceclinical decision support systemsclinical validationdeep learningelectronic health recordsfrailtyhuman-computer interactionmachine learningmixed methods researchtriage

Related Experiment Videos

Last Updated: Jul 5, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Health Informatics

Background:

  • Clinical triage necessitates integrating diverse data to identify patients at risk of deterioration.
  • Developing tools for global health assessment is ongoing, using bottom-up aggregation or top-down machine learning.
  • The alignment of these tools with expert clinical judgment requires further characterization.

Purpose of the Study:

  • To evaluate two latent health measurement approaches: Frailty Index-laboratory (bottom-up) and ETHOS-ARES (top-down foundation model).
  • To assess the alignment of these tools' severity rankings with clinical consensus.
  • To determine the utility of these tools in triage decisions.

Main Methods:

  • A 3-phase mixed-methods study involving at least 30 clinicians.
  • Phase 1: Compared clinician judgments with model outputs using Spearman correlation and a Turing-inspired test.
  • Phase 2: Assessed anchoring effects and clinical utility through pre-post comparisons and semistructured interviews.

Main Results:

  • Quantification of model-clinician agreement is planned.
  • Measurement of anchoring effects on clinical judgment is anticipated.
  • Qualitative insights into utility, trust, and adoption will be generated.

Conclusions:

  • Findings will inform the implementation of latent health measurement tools in clinical practice.
  • Provides a framework for evaluating early-stage AI-based clinical decision support systems.
  • Highlights the importance of model-clinician alignment for effective healthcare AI.