MobiGuide: a personalized and patient-centric decision-support system and its evaluation in the atrial fibrillation and gestational diabetes domains

MobiGuide is a ubiquitous, distributed and personalized evidence-based decision-support system (DSS) used by patients and their care providers. Its central DSS applies computer-interpretable clinical guidelines (CIGs) to provide real-time patient-specific and personalized recommendations by matching...

Full description

Saved in:
Bibliographic Details
Published in:User modeling and user-adapted interaction 2017-06, Vol.27 (2), p.159-213
Main Authors: Peleg, Mor, Shahar, Yuval, Quaglini, Silvana, Fux, Adi, García-Sáez, Gema, Goldstein, Ayelet, Hernando, M. Elena, Klimov, Denis, Martínez-Sarriegui, Iñaki, Napolitano, Carlo, Parimbelli, Enea, Rigla, Mercedes, Sacchi, Lucia, Shalom, Erez, Soffer, Pnina
Format: Article
Language:English
Subjects:
Biosensors
Cardiac arrhythmia
Computer Science
Customization
Decision making
Decision support systems
Decision trees
Diabetes
Diabetes mellitus
Feasibility studies
Fibrillation
Gestational diabetes
Management of Computing and Information Systems
Multimedia Information Systems
Patients
Smartphones
Stems
User Interfaces and Human Computer Interaction
Utilities
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:MobiGuide is a ubiquitous, distributed and personalized evidence-based decision-support system (DSS) used by patients and their care providers. Its central DSS applies computer-interpretable clinical guidelines (CIGs) to provide real-time patient-specific and personalized recommendations by matching CIG knowledge with a highly-adaptive patient model, the parameters of which are stored in a personal health record (PHR). The PHR integrates data from hospital medical records, mobile biosensors, data entered by patients, and recommendations and abstractions output by the DSS. CIGs are customized to consider the patients’ psycho-social context and their preferences; shared decision making is supported via decision trees instantiated with patient utilities. The central DSS “projects” personalized CIG-knowledge to a mobile DSS operating on the patients’ smart phones that applies that knowledge locally. In this paper we explain the knowledge elicitation and specification methodologies that we have developed for making CIGs patient-centered and enabling their personalization. We then demonstrate feasibility, in two very different clinical domains, and two different geographic sites, as part of a multi-national feasibility study, of the full architecture that we have designed and implemented. We analyze usage patterns and opinions collected via questionnaires of the 10 atrial fibrillation (AF) and 20 gestational diabetes mellitus (GDM) patients and their care providers. The analysis is guided by three hypotheses concerning the effect of the personal patient model on patients and clinicians’ behavior and on patients’ satisfaction. The results demonstrate the sustainable usage of the system by patients and their care providers and patients’ satisfaction, which stems mostly from their increased sense of safety. The system has affected the behavior of clinicians, which have inspected the patients’ models between scheduled visits, resulting in change of diagnosis for two of the ten AF patients and anticipated change in therapy for eleven of the twenty GDM patients.
ISSN:0924-1868
1573-1391
DOI:10.1007/s11257-017-9190-5