An intelligent system for improving adherence to guidelines on acute stroke

Abstract

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.

How to cite this article: Torab-Miandoab A, Samad-Soltani T, Shams-Vahdati S, Rezaei-Hachesu P. An intelligent system for improving adherence to guidelines on acute stroke. Turk J Emerg Med 2020;20:118-34.

This article does not contain any studies with human participants or animals performed by any of the authors. The Ethics Committee of Tabriz University of Medical Sciences has confirmed this research.

S SV and P RH conceived the original idea. A TM, T SS, and P RH designed the study. A TM and S SV collected data. A TM and T SS analyzed and interpreted the data. A TM, T SS, and P RH prepared the first manuscript draft. All authors contributed significantly and critically to the final manuscript.

None declared.

None declared.

This study as an MSc dissertation with Thesis number 59584/323 was done in School of Health Management and Medical Informatics of Tabriz University of Medical Sciences. Authors express their gratitude to emergency specialists at Imam Reza Hospital for their assistance and cooperation.