| OBJECTIVES: A timely, accurate assessment and decision-making process is essential for the
diagnosis and treatment of the acute stroke, which is the world’s third leading cause of death. This
process is often performed using the traditional method that increases the complexity, duration, and
medical errors. The present study aimed to design and evaluate an intelligent system for improving
adherence to the guidelines on the assessment and treatment of acute stroke patients.
METHODS: Decision-making rules and data elements were used to predict the severity and to treat
patients according to the specialists’ opinions and guidelines. A system was then developed based on
the intelligent decision‑making algorithms. The system was finally evaluated by measuring the accuracy,
sensitivity, specificity, applicability, performance, esthetics, information quality, and completeness and
rates of medical errors. The segmented regression model was used to evaluate the effect of systems
on the level and the trend of guideline adherence for the assessment and treatment of acute stroke.
RESULTS: Fifty-three data elements were identified and used in the data collection and
comprehensive decision-making rules. The rules were organized in a decision tree. In our analysis,
150 patients were included. The system accuracy was 98.30%. Evaluation results indicated an error
rate of 1.69% by traditional methods. Documentation quality (completeness) increased from 78.66%
to 100%. The average score of system quality was 4.60 indicating an acceptable range. After the
system intervention, the mean of the adherence to the guideline significantly increased from 65%
to 99.5% (P < 0.0008).
CONCLUSION: The designed system was accurate and can improve adherence to the guideline for
the severity assessment and the determination of a therapeutic trend for acute stroke patients. It leads
to physicians’ empowerment, significantly reduces medical errors, and improves the documentation
quality. |