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Technology Acceptance of a Machine Learning Algorithm Predicting Delirium in a Clinical Setting: a Mixed-Methods

Stefanie Jauk1,2, Diether Kramer3, Alexander Avian4

  • 1Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), Information and Process Management, Graz, Austria. stefanie.jauk@kages.at.

Journal of Medical Systems
|March 1, 2021
PubMed
Summary
This summary is machine-generated.

Healthcare professionals found a machine learning delirium prediction tool useful and easy to use. However, actual system use remained low during the pilot study, highlighting a need for better integration of AI in clinical settings.

Keywords:
Clinical decision supportDeliriumMachine learningPredictive modellingRisk managementTechnology acceptance model

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems

Background:

  • Early identification of patients at risk of delirium is critical for timely intervention.
  • The integration of machine learning (ML) models into clinical practice faces challenges in user acceptance.
  • Assessing user acceptance is key to the successful implementation of AI-driven healthcare tools.

Purpose of the Study:

  • To evaluate the user acceptance of an implemented ML-based application for predicting inpatient delirium risk.
  • To understand healthcare professionals' opinions and concerns regarding the use of this AI tool.
  • To assess the application's perceived ease of use, usefulness, system use, and output quality.

Main Methods:

  • A mixed-methods design was employed, combining questionnaires and expert group meetings.
  • The Technology Acceptance Model (TAM) framework guided the evaluation.
  • Data were collected from 47 nurses and physicians using the application and through four expert group discussions.

Main Results:

  • The ML application for delirium prediction was rated positively for overall usefulness by healthcare professionals.
  • Users found the application's visualization and information understandable, easy to use, and appreciated the added delirium management support.
  • The application did not increase workload, but its actual system use was low during the pilot phase.

Conclusions:

  • The study provides valuable insights into the user acceptance of ML-based decision support for delirium management.
  • High user acceptance is crucial for the successful integration of AI tools to improve healthcare quality and safety.
  • Future efforts should focus on predicting actionable events and ensuring strong user adoption of computerized decision support systems.