Decision Support for Personalization on Mobile Devices

2005 ◽  
pp. 404-406 ◽  
Author(s):  
Thomas Kleemann ◽  
Alex Sinner
Keyword(s):  
Decision Support ◽  
Mobile Devices

Russian-language e-learning and decision-support app for mobile devices on tuberculosis and lung health for primary care workers in Belarus

2016 ◽  
Author(s):  
Valiantsin Rusovich ◽  
Hennadz Hurevich ◽  
Liudmila Zhylevich ◽  
Dennis Falzon ◽  
Pierpaolo de Colombani ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Mobile Devices ◽  
Russian Language ◽  
Care Workers ◽  
E Learning ◽  
Lung Health

An Environment for Guidelinebased Decision Support Systems for Outpatients Monitoring

2017 ◽  
Vol 56 (04) ◽  
pp. 283-293 ◽  
Author(s):  
Giordano Lanzola ◽  
Paolo Bossi ◽  
Silvana Quaglini ◽  
Elisa M. Zini
Keyword(s):  
Decision Support ◽  
Side Effects ◽  
Decision Support System ◽  
Mobile Devices ◽  
Support System ◽  
Mobile Application ◽  
Web Application ◽  
Mobile Technologies ◽  
Specific Patient ◽  
Patient Reported

SummaryObjectives: We propose an architecture for monitoring outpatients that relies on mobile technologies for acquiring data. The goal is to better control the onset of possible side effects between the scheduled visits at the clinic.Methods: We analyze the architectural components required to ensure a high level of abstraction from data. Clinical practice guidelines were formalized with Alium, an authoring tool based on the PROforma language, using SNOMED-CT as a terminology standard. The Alium engine is accessible through a set of APIs that may be leveraged for implementing an application based on standard web technologies to be used by doctors at the clinic. Data sent by patients using mobile devices need to be complemented with those already available in the Electronic Health Record to generate personalized recommendations. Thus a middleware pursuing data abstraction is required. To comply with current standards, we adopted the HL7 Virtual Medical Record for Clinical Decision Support Logical Model, Release 2.Results: The developed architecture for monitoring outpatients includes: (1) a guideline-based Decision Support System accessible through a web application that helps the doctors with prevention, diagnosis and treatment of therapy side effects; (2) an application for mobile devices, which allows patients to regularly send data to the clinic. In order to tailor the monitoring procedures to the specific patient, the Decision Support System also helps physicians with the configuration of the mobile application, suggesting the data to be collected and the associated collection frequency that may change over time, according to the individual patient’s conditions. A proof of concept has been developed with a system for monitoring the side effects of chemo-radiotherapy in head and neck cancer patients.Conclusions: Our environment introduces two main innovation elements with respect to similar works available in the literature. First, in order to meet the specific patients’ needs, in our work the Decision Support System also helps the physicians in properly configuring the mobile application. Then the Decision Support System is also continuously fed by patient-reported outcomes.

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