Search

Objectives: The MobiGuide project aimed to establish a ubiquitous, user-friendly, patient-centered mobile decision-support system for patients and for their care providers, based on the continuous application of clinical guidelines and on semantically integrated electronic health records. Patients would be empowered by the system, which would enable them to lead their normal daily lives in their regular environment, while feeling safe, because their health state would be continuously monitored using mobile sensors and self-reporting of symptoms. When conditions occur that require medical attention, patients would be notified as to what they need to do, based on evidence-based guidelines, while their medical team would be informed appropriately, in parallel. We wanted to assess the system's feasibility and potential effects on patients and care providers in two different clinical domains., Materials and Methods: We describe MobiGuide's architecture, which embodies these objectives. Our novel methodologies include a ubiquitous architecture, encompassing a knowledge elicitation process for parallel coordinated workflows for patients and care providers; the customization of computer-interpretable guidelines (CIGs) by secondary contexts affecting remote management and distributed decision-making; a mechanism for episodic, on demand projection of the relevant portions of CIGs from a centralized, backend decision-support system (DSS), to a local, mobile DSS, which continuously delivers the actual recommendations to the patient; shared decision-making that embodies patient preferences; semantic data integration; and patient and care provider notification services. MobiGuide has been implemented and assessed in a preliminary fashion in two domains: atrial fibrillation (AF), and gestational diabetes Mellitus (GDM). Ten AF patients used the AF MobiGuide system in Italy and 19 GDM patients used the GDM MobiGuide system in Spain. The evaluation of the MobiGuide system focused on patient and care providers' compliance to CIG recommendations and their satisfaction and quality of life., Results: Our evaluation has demonstrated the system's capability for supporting distributed decision-making and its use by patients and clinicians. The results show that compliance of GDM patients to the most important monitoring targets - blood glucose levels (performance of four measurements a day: 0.87±0.11; measurement according to the recommended frequency of every day or twice a week: 0.99±0.03), ketonuria (0.98±0.03), and blood pressure (0.82±0.24) - was high in most GDM patients, while compliance of AF patients to the most important targets was quite high, considering the required ECG measurements (0.65±0.28) and blood-pressure measurements (0.75±1.33). This outcome was viewed by the clinicians as a major potential benefit of the system, and the patients have demonstrated that they are capable of self-monitoring - something that they had not experienced before. In addition, the system caused the clinicians managing the AF patients to change their diagnosis and subsequent treatment for two of the ten AF patients, and caused the clinicians managing the GDM patients to start insulin therapy earlier in two of the 19 patients, based on system's recommendations. Based on the end-of-study questionnaires, the sense of safety that the system has provided to the patients was its greatest asset. Analysis of the patients' quality of life (QoL) questionnaires for the AF patients was inconclusive, because while most patients reported an improvement in their quality of life in the EuroQoL questionnaire, most AF patients reported a deterioration in the AFEQT questionnaire., Discussion: Feasibility and some of the potential benefits of an evidence-based distributed patient-guidance system were demonstrated in both clinical domains. The potential application of MobiGuide to other medical domains is supported by its standards-based patient health record with multiple electronic medical record linking capabilities, generic data insertion methods, generic medical knowledge representation and application methods, and the ability to communicate with a wide range of sensors. Future larger scale evaluations can assess the impact of such a system on clinical outcomes., Conclusion: MobiGuide's feasibility was demonstrated by a working prototype for the AF and GDM domains, which is usable by patients and clinicians, achieving high compliance to self-measurement recommendations, while enhancing the satisfaction of patients and care providers., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: It has been widely reported that anticoagulants (ACs) are underused for primary and secondary prevention of ischemic stroke in patients with atrial fibrillation (AFib). Furthermore, precise evidence-based guidelines about the best timing for AC initiation after acute stroke are currently lacking., Methods and Results: In this retrospective, observational study, we analyzed prescription trends in AFib patients with acute ischemic stroke who were hospitalized in four neurologic stroke units of our region (Lombardia, Italy). In-hospital antithrombotic prescription was performed in highly heterogeneous patterns. A prestroke treatment with AC was the leading factor enhancing AC prescription during hospitalization. The other factors promoting AC were male gender, younger age, lower prestroke disability and stroke severity, and smaller stroke volumes. AFib subtype influenced AC prescription only in AC-naïve patients. Interestingly, Congestive heart failure, Hypertension, Age higher than 75 years, Diabetes, previous Stroke or TIA or thromboembolism, Vascular disease, Age 64-75 years, female Sex (CHA 2 DS 2 -VASc) and Hypertension, Abnormal renal and liver function, Stroke, Bleeding, Labile INRs, Elderly, Drugs and alcohol (HAS-BLED) scores were not associated with AC prescription. However, patients who were treated with AC, including early treatment (<48 hours), showed a low rate of bleeding., Conclusions: Our findings potentially suggest that, although apparently neglecting the common risk stratification tools, our neurologists were able to select the more suitable candidates for prompt AC treatment. Further studies are needed to develop new scoring systems to aid ischemic and hemorrhagic risk estimation in the secondary prevention of stroke., (Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.)

Studies describing Computer-Interpretable Clinical Guidelines (CIG) with temporal constrains (TC) generally have not addressed issues related to their integration into Electronic Health Record (EHR) systems. This study aimed to represent TCs contained in clinical guidelines by applying archetypes and Guideline Definition Language (GDL) to incorporate decision support into EHRs. An example of each TC class in the clinical guideline for management of Atrial Fibrillation was represented using archetypes and GDL.

Leveraging the experience of the European project MobiGuide, this paper elaborates on the nurses' role in developing, delivering and evaluating e-health based services. We focus on the home monitoring of atrial fibrillation. Patients enrolled in our study are provided with a smartphone and an ECG sensor, and receive recommendations, reminders and alerts concerning medications and measurements that they should perform through a mobile decision support system that is constantly updated by a backend system. Patients' data are sent to health care personnel that may visualize them, and act accordingly. Nurses play a central role in such setting. After being involved in the design of the caregiver interface, they are responsible for the patients' enrollment phase (which includes patients' training), for the daily checking of incoming data, for the triage of patients' complaints, and for the final phase of the study where patients are interviewed about their experience with the system.

MobiGuide is a project devoted to the development of a patient-centric decision support system based on computerized clinical guidelines for chronic illnesses including Atrial Fibrillation (AF). In this paper we describe the process of (1) identifying guideline recommendations that will require patients to take actions (e.g., take measurement, take drug), thus impacting patients' daily-life behavior, (2) eliciting from the medical experts the corresponding set of personalized operationalized advices that are not explicitly written in the guideline (patient-tailored workflow patterns) and (3) delivering this advice to patients. The analysis of the AF guideline has resulted in four types of patient-tailored workflow patterns: therapy-related advisors, measurements advisors, suggestions for dealing with interventions that may require modulating patient therapy, and personalized packages for close monitoring of patients. We will show how these patterns can be generated using information stored in a patient health record that embeds clinical data and data about the patient's personal context and preferences.

Objective: Taking into account patients' preferences has become an essential requirement in health decision-making. Even in evidence-based settings where directions are summarized into clinical practice guidelines, there might exist situations where it is important for the care provider to involve the patient in the decision. In this paper we propose a unified framework to promote the shift from a traditional, physician-centered, clinical decision process to a more personalized, patient-oriented shared decision-making (SDM) environment.Methods: We present the theoretical, technological and architectural aspects of a framework that encapsulates decision models and instruments to elicit patients' preferences into a single tool, thus enabling physicians to exploit evidence-based medicine and shared decision-making in the same encounter.Results: We show the implementation of the framework in a specific case study related to the prevention and management of the risk of thromboembolism in atrial fibrillation. We describe the underlying decision model and how this can be personalized according to patients' preferences. The application of the framework is tested through a pilot clinical evaluation study carried out on 20 patients at the Rehabilitation Cardiology Unit at the IRCCS Fondazione Salvatore Maugeri hospital (Pavia, Italy). The results point out the importance of running personalized decision models, which can substantially differ from models quantified with population coefficients.Conclusions: This study shows that the tool is potentially able to overcome some of the main barriers perceived by physicians in the adoption of SDM. In parallel, the development of the framework increases the involvement of patients in the process of care focusing on the centrality of individual patients. [ABSTRACT FROM AUTHOR]