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Methods Inf Med 2017; 56(04): 283-293
DOI: 10.3414/ME16-01-0142
Paper

An Environment for Guidelinebased Decision Support Systems for Outpatients Monitoring

Elisa M. Zini
1   Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
,
Giordano Lanzola
1   Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
,
Paolo Bossi
2   Head and Neck Medical Oncology Department, IRCCS Foundation National Cancer Institute, Milan, Italy
,
Silvana Quaglini
1   Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
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Summary

Objectives: 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.

Keywords

Mobile health - decision support systems - clinical - patient monitoring - head and neck neoplasms - patient reported outcome measures


Publication History

Received: 16 December 2016

Accepted after revision: 19 May 2017

Article published online:
24 January 2018

Schattauer GmbH

 

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