A Mobile Intelligent Checklist System for Stroke Emergency The patient safety is a significant concern during the stroke emergency. The intelligent checklist, which is to regulate medical behavior and reduce medical error, can improve the patient safety. Most existing intelligent checklist systems are used at a certain work station which does not fit the environment of acute stroke care, due to timecritical, high pressure, and heavy workload. Therefore, we develop a mobile intelligent checklist system which focus on decreasing patient issues in stroke emergency care. The requirements of intelligent checklist system are collected by questionnaire investigation, literature studies, interviews and discussions. The system architecture is designed to separate clinical rules from application based on Drools rule engine. A templatebased approach is used to generate the content of the checklist dynamically to enable flexible configuration and rapid deployment. In cooperation with clinical experts, an intravenous thrombolysis checklist is designed and implemented, which evaluted the availability of the mobile intelligent checklist system. The system guarantees that check tasks well accomplished at the right time for the right patients with the right conditions by generating checklist content based on clinical rules and using a mobile checking terminal in stroke emergency.

The Decision Support System Developed for the Stroke Patients Support Platform This paper gives an overview of the Decision Support System (DSS) integrated in the stroke survivorxe2x80x99s recovery support platform. The main goal of the platform is to improve life quality and conditions of patients who survived a stroke. The role of DSS within this platform is to collect medical data, calculate health state assessments, and to provide the recommendations to help the improvement of the patientu0027s health. The implementation of DSS utilizes various programming languages, libraries and techniques. In particular, Java, JavaScript and Python have been used, statistical and AI libraries and techniques have been used, all in a single goal of estimating and predicting the patientu0027s health state. Three major novel features are added to the DSS: personalization, multipurpose and multistakeholders support, and AI processing of data.