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This study introduces an algorithm to evaluate medical software correctness by comparing user interactions against a set standard. This method aids in assessing training effectiveness and comparing similar healthcare software products.

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Area of Science:

  • Healthcare System Management
  • Human-Computer Interaction
  • Software Engineering

Background:

  • Evaluating medical software correctness is crucial for healthcare management.
  • Existing tools like Turf capture user-computer interactions.
  • A need exists for systematic analysis of these interactions.

Purpose of the Study:

  • To propose an algorithm for comparing recorded human-computer interaction events with a predefined path.
  • To develop statistical analysis methods for evaluating software usage correctness.
  • To apply these methods for identifying training effects and comparing software products.

Main Methods:

  • Utilizing the Turf software to record human-computer interaction events.
  • Developing an algorithm to compare recorded events against a predefined correct usage path.
  • Applying statistical analysis to the pass/fail results of the comparison.

Main Results:

  • The proposed algorithm can effectively compare recorded interactions with a predefined path.
  • Statistical analysis of the results allows for the identification of training effects.
  • The methods enable the comparison of different software products with similar functionalities.

Conclusions:

  • The developed algorithm and statistical methods provide a robust framework for evaluating medical software correctness.
  • This approach can significantly contribute to improving healthcare system management through better software usage assessment.
  • The findings support the use of interaction analysis for both training evaluation and product benchmarking.