Investigating Clinical Decision Support Systems Success Factors with Usability Testing

Abstract

Abstract-Clinical Decision Support Systems (CDSS) have been used widely since 2000s to improve the healthcare quality. CDSS can be utilized to support healthcare services as a tool to diagnose, predict, as well as to provide clinical interpretation, alert, and reminder. There are many researches of CDSS implementation on literatures but not many of them present the evidence of CDSS successful implementation. In spite of the potential use of CDSS, there are some researches that reveal the failures of CDSS implementation. This paper contributes to CDSS development by investigating and exploring CDSS success factors with usability testing. The testing involves participants from different types of backgrounds (physicians, IT developers, and students). The participants are being asked to experience three different CDSS to predict cardiovascular risk factors. The result of the research shows that involving different type of users give more insight to design process. It can be concluded that user center design is very critical to produce successful CDSS.

Introduction to artificial intelligence and machine learning into orthodontics: A review

“…[13] It can handle various multivariate parameters which also help in the diagnosis of various systemic or pathologies and also image as well as data analysis. [14] An important aspect of ANNs includes (1) dilated convolutional neural networks (DCNNs) that focus on sematic scene segmentation. ( 2) CNNs focus on image classification.…”

Development of Mobile Health Application for Cardiovascular Disease Prevention

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