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Evaluation Framework for Successful Artificial Intelligence-Enabled Clinical Decision Support Systems: Mixed Methods

Mengting Ji1,2, Georgi Z Genchev3,4,5, Hengye Huang1

  • 1School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Journal of Medical Internet Research
|June 2, 2021
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Summary
This summary is machine-generated.

User acceptance is key for artificial intelligence-enabled clinical decision support systems. Perceived ease of use, information quality, and service quality directly impact acceptance, influencing clinical practice integration.

Keywords:
AIartificial intelligenceclinical decision support systemsevaluation framework

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Health Services Research

Background:

  • Clinical decision support systems (CDSS) leverage medical data and AI to provide patient-specific recommendations.
  • Effective evaluation methods are crucial for integrating AI-enabled CDSS into clinical workflows.
  • AI-enabled CDSS enhance healthcare decision-making through intelligent components.

Purpose of the Study:

  • To develop and validate a measurement instrument for evaluating AI-enabled CDSS.
  • To test interrelationships among evaluation variables within an AI-enabled CDSS framework.

Main Methods:

  • A 6-variable AI-enabled CDSS evaluation framework was established.
  • A Delphi process, cognitive interviews, and pretesting refined a 28-item measurement instrument.
  • Web-based survey data from 156 respondents were analyzed for validity, reliability, and structural relationships using path analysis.

Main Results:

  • The measurement instrument demonstrated high reliability (Cronbach α=0.963) and content validity (0.943).
  • Model fit indices indicated good overall model fit (CFI=0.991, GFI=0.957, RMSEA=0.052).
  • Key variables explained 89% of the variance in user acceptance.

Conclusions:

  • User acceptance is the central determinant of AI-enabled CDSS success.
  • Perceived ease of use, information quality, service quality, and perceived benefit directly influence user acceptance.
  • System and information quality indirectly impact acceptance via perceived ease of use.