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Related Experiment Video

Updated: Jun 1, 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

Computerized model for preoperative risk assessment.

X Zuidema1, R C Tromp Meesters, I Siccama

  • 1Department of Perioperative Care and Emergency Medicine, University Medical Centre Utrecht, Heidelberglaan 100, P.O. Box 85500, Mail Stop: Q04.2.313, 3508 GA Utrecht, The Netherlands. x.zuidema@umcutrecht.nl

British Journal of Anaesthesia
|June 11, 2011
PubMed
Summary
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An automated system for calculating Anesthesia Physical Status (ASA) scores closely matches human assessments, improving consistency in anesthetic risk scoring. This computed ASA (cASA) offers a reliable alternative for preoperative risk evaluation.

Area of Science:

  • Anesthesiology
  • Medical Informatics
  • Health Services Research

Background:

  • Anesthetic risk scoring consistency is crucial for patient safety.
  • Current methods rely on subjective assessments by caregivers.
  • An automated approach can standardize risk evaluation.

Purpose of the Study:

  • To develop and validate an automated method for calculating Anesthesia Physical Status (ASA) scores.
  • To compare computed ASA (cASA) scores with those estimated by human caregivers (eASA).
  • To assess the accuracy and reliability of automated anesthetic risk scoring.

Main Methods:

  • A web-based preoperative assessment system with a 22-question structured questionnaire was utilized.
  • Decision logic programming processed data from 14,349 cases to compute cASA scores.

Related Experiment Videos

Last Updated: Jun 1, 2026

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

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  • Computed cASA scores were compared against expert-estimated ASA (eASA) scores.

    • Main Results:

    • A close agreement was observed between cASA and eASA in the majority of cases.
    • Discrepancies (1.1%) were primarily due to incomplete/incorrect data entry or human overestimation.
    • The automated system demonstrated high concordance with human-evaluated anesthetic risk.

    Conclusions:

    • The computed ASA (cASA) system accurately mimics scores provided by diverse anesthesia providers.
    • Automated anesthetic risk scoring enhances consistency and reliability in preoperative assessments.
    • This technology has the potential to standardize and improve the accuracy of anesthetic risk stratification.