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Area of Science:

  • Health Information Technology
  • Human-Computer Interaction
  • Clinical Informatics

Background:

  • Clinical decision support systems (CDSSs) are vital health information technologies aiding interpretation, diagnosis, and treatment.
  • While CDSSs reduce medical errors and improve outcomes, user acceptance remains a barrier to their full potential.
  • Lack of user acceptance is a significant factor limiting the effectiveness of health information technologies.

Purpose of the Study:

  • To critically review CDSS research with a focus on user acceptance.
  • To perform a task analysis of CDSS to understand user-system interactions.
  • To develop a novel framework for CDSS design to enhance user acceptance.

Main Methods:

  • Conducted a critical review of CDSS literature, emphasizing user acceptance.
  • Performed a task analysis from both machine (CDSS engine) and user (physician) perspectives.
  • Identified goals, inputs, outputs, knowledge requirements, and constraints for CDSS.

Main Results:

  • Favorability of CDSSs is linked to user acceptance of guidelines, reminders, alerts, and suggestions.
  • Proposed two models: a user acceptance and system adaptation design model and an input-process-output-engagement model.
  • The models aim to optimize CDSS design based on user needs and clarify system processes.

Conclusions:

  • Incorporating the proposed models can improve user acceptance of CDSSs.
  • Enhanced user acceptance supports the beneficial effects of CDSS adoption.
  • Low user acceptance of health technology poses risks to technology use and patient well-being.