Mobile Clinical Decision Support Systems and Applications: A Literature and Commercial Review

Abstract

The latest advances in eHealth and mHealth have propitiated the rapidly creation and expansion of mobile applications for health care. One of these types of applications are the clinical decision support systems, which nowadays are being implemented in mobile apps to facilitate the access to health care professionals in their daily clinical decisions. The aim of this paper is twofold. Firstly, to make a review of the current systems available in the literature and in commercial stores. Secondly, to analyze a sample of applications in order to obtain some conclusions and recommendations. Two reviews have been done: a literature review on Scopus, IEEE Xplore, Web of Knowledge and PubMed and a commercial review on Google play and the App Store. Five applications from each review have been selected to develop an in-depth analysis and to obtain more information about the mobile clinical decision support systems. Ninety-two relevant papers and 192 commercial apps were found. Forty-four papers were focused only on mobile clinical decision support systems. One hundred seventy-one apps were available on Google play and 21 on the App Store. The apps are designed for general medicine and 37 different specialties, with some features common in all of them despite of the different medical fields objective. The number of mobile clinical decision support applications and their inclusion in clinical practices has risen in the last years. However, developers must be careful with their interface or the easiness of use, which can impoverish the experience of the users.

Analysis and design of mobile health interventions towards informed shared decision making: an activity theory-driven perspective

“…For example, a web-based intervention was designed to support outpatients and healthcare experts to make collaboratively shared decisions in psychopharmacological consultation (Deegan, Rapp, Holter, & Riefer, 2008). Having significant advantages of usability and mobility over these classic systems (Carroll, Marrero, & Downs, 2007;Istepanian et al, 2009), mobile health interventions have been evolved beyond typical data collection and reporting functions ( Van Woensel et al, 2015) to link health observations with health knowledge for better decision quality and outcome (Martínez-Pérez et al, 2014).…”

“…The decision support capabilities of mobile health encompass a range of healthcare processes and diseases such as prevention of cardiovascular diseases (CVD) (Hervás et al, 2013), treatment of diabetes mellitus (Garcia-Saez et al, 2014), or diagnosis of Parkinson's disease (Klucken et al, 2013). These interventions are capable of facilitating multiple sensing technologies,various communication mechanisms (e.g., notifications, messaging, and reminders) and diverse forms of decision support (e.g., algorithms, tables, logical trees, and free-text guidelines) (Martínez-Pérez et al, 2014). For instance, a Bluetoothenabled blood pressure device was utilised to compute CVD risk using SCoRE method to provide clinicians and patients with timely recommendations.…”

“…Although implementation of these technologies is relatively recent (Martínez-Pérez et al, 2014), the reliability and acceptance of these decision support interventions have been quickly recognised in institutional care. In 2015, 44% of worldwide patients have witnessed the use of mobile devices by health professionals in treatment and diagnosis (Mobile Ecosystem Forum, 2015).…”

“…By 2018, these mobile apps would promisingly extend the coverage of healthcare to 1.7 billion patients for improved clinical outcomes, higher quality of care, and cost savings in healthcare delivery (Research2guidance, 2010). The adoption of wireless sensing technologies in mobile health innovations has been widely studied as a robust mechanism to reshape the mobile capabilities for better decision support (Martínez-Pérez et al, 2014;William Van Woensel, Roy, & Abidi, 2015).…”

Development of a Decision Support System for Handling Health Insurance Deduction

“…Recently, sophisticated DM approaches have been proposed for similar retrospective analyses of both administrative and clinical data [10,11]. The use of DM to facilitate decision support provides a new approach to problem solving by discovering patterns and relationships hidden in the data, giving rise to an inductive approach to DSS.…”