Review Apps to Evaluate Stroke Risk in Prehospital Setting

Mobile applications have experienced exponential growth in recent years, including mHealth apps related to stroke, one of the most prevalent diseases worldwide. This review aims to analyze the characteristics of available stroke apps designed to assist in assessing stroke severity. Initially, 809 apps were retrieved from both the App Store and Google Play Store. These apps were then filtered, primarily excluding those that did not implement a prehospital stroke scale with a resulting score. A total of 36 apps met the criteria for further analysis in this review. The majority of these apps displayed scale items using text only. Certain scales, such as RACE, VAN, and NIHSS, are supported by studies demonstrating their ability to accurately assess stroke severity. Consequently, apps featuring these scales are more likely to be useful in achieving the objective of this study. Improvements to these apps could be made by expanding the functionalities they offer and enhancing their user experience.

I. Introduction

Stroke is a leading cause of morbidity and mortality worldwide, posing a significant burden on healthcare systems and society. Rapid identification and treatment of stroke patients are crucial for minimizing the damage caused by a stroke and improving patient outcomes. In recent years, there has been an increasing interest in the development and utilization of mobile applications for prehospital stroke triage, aiming to expedite the assessment of stroke severity and facilitate the appropriate allocation of patients to specialized treatment centers. Several studies have investigated the effectiveness of various stroke scales in prehospital settings, such as the National Institutes of Health Stroke Scale (NIHSS) (Carr et al., 2019), Rapid Arterial Occlusion Evaluation (RACE) scale (Jumaa et al., 2019), Cincinnati Prehospital Stroke Scale (CPSS) (Crowe et al., 2020), and the Vision, Aphasia, Neglect (VAN) scale (Crowe et al., 2020). These studies provide valuable insights into the performance of different stroke scales and their applicability to mobile applications for prehospital stroke triage. In addition to stroke scales, other key features of prehospital stroke triage apps include usability, user satisfaction, and their impact on patient outcomes and healthcare systems. Research exploring the use of machine learning and artificial intelligence in these apps has also gained attention, as these advanced technologies hold the potential to improve the accuracy and efficiency of stroke diagnosis and triage. The primary aim of this systematic review is to provide a comprehensive analysis of the current landscape of prehospital stroke triage mobile applications, including their features, the stroke scales they incorporate, and their potential for improving patient care. Furthermore, the review seeks to identify areas for improvement and suggest future research directions to enhance the development and effectiveness of these apps. By synthesizing the existing literature and assessing the current state of prehospital stroke triage apps, this review aims to contribute to the ongoing efforts to improve stroke care, reduce time to treatment, and ultimately improve patient outcomes.