Validation of a Formal Method for Human Error Rate Prediction With Negative Transfer

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Abstract
Human error is often associated with system failures. The complexity of human-automation interaction can make it difficult to anticipate what errors can occur and how they contribute to failures. Previous research has shown that task analytic behavior modeling with the enhanced operator function model and the cognitive reliability analysis method (CREAM) can be combined with statistical model checking to make predictions about human error rates, their stochastic impact on system failures, and the effect of negative transfer of design changes on these predictions. These efforts were successful, but the validation studies used artificial examples with limited data. Predictions also slightly overestimated error rates. This article addresses these deficiencies by conducting a validation study based on the prescription order entry interface of the OpenEMR electronic medical record. As part of this, we explored how prediction accuracy for the OpenEMR application changed based on the inclusion/exclusion of planning errors: errors based on people’s ability to formulate task plans, which we hypothesized contributed to error rate overestimation. Results found that our method’s predictions aligned with those observed in the experiment, especially when planning errors were excluded. Negative transfer conditions did not manifest significant differences in error rates experimentally or in model predictions. These results suggest that negative transfer’s impact on human–computer interaction may be overstated in the literature. Finally, higher error rates were observed between the original OpenEMR prescription order entry interface compared to an alternative that we tested. We highly suggest that OpenEMR adopt the alternative.
Funding Information
  • National Science Foundation (2219041, 1918314, 1918140)

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