A quality-of-data aware mobile decision support system for patients with chronic illnesses

We present a mobile decision support system (mDSS) which runs on a patient Body Area Network consisting of a smartphone and a set of biosensors. Quality-of-Data (QoD) awareness in decision making is achieved by means of a component known as the Quality-of-Data Broker, which also runs on the smartphone. The QoD-aware mDSS collaborates with a more sophisticated decision support system running on a fixed back-end server in order to provide distributed decision support. This distributed decision support system has been implemented as part of a larger system developed during the European project MobiGuide. The MobiGuide system is a guideline-based Patient Guidance System designed to assist patients in the management of chronic illnesses. The system, including the QOD-aware mDSS, has been validated by clinicians and is being evaluated in patient pilots against two clinical guidelines

Application of a conceptual framework for the modelling and execution of clinical guidelines as networks of concurrent processes

We present a conceptual framework for modelling clinical guidelines as networks of concurrent processes. This enables the guideline to be partitioned and distributed at run-time across a knowledge-based telemedicine system, which is distributed by definition but whose exact physical configuration can only be determined after design-time by considering, amongst other factors, the individual patient's needs. The framework was applied to model a clinical guideline for gestational diabetes mellitus and to derive a prototype that executes the guideline on a smartphone. The framework is shown to support the full development trajectory of a decision support system, including analysis, design and implementation

Quality of clinical data aware telemedicine systems

Healthcare services have been evolving continuously owing to new demands caused by demographic and lifestyle changes. The advancements in information and communication technology (ICT) have propelled the development of new healthcare systems that can fulfil these demands. One of the key developments in this field is in the form of telemedicine systems, which aims to reliably deliver remote healthcare to patients through information exchange across distances.\ud This research is conducted in the context of ambulatory patient treatment guidance where a patient can receive remote treatment that is compliant with the\ud current care-plan, without the presence of his/her doctor, with the aid of a\ud telemedicine system. In order to provide treatment guidance that complies with healthcare procedures, telemedicine systems may apply clinical guidelines.\ud Additionally, the ICT of these telemedicine systems enables the acquisition\ud of patient clinical data. Hence, the clinical guidelines, in combination with ubiquitously acquired patient clinical data, may result in a personalized\ud treatment guidance that will provide the necessary remote treatment to the patient without the need of a practitioner

Comparative study of algorithms for atrial fibrillation detection

Nine algorithms for Atrial Fibrillation (AF) detection were evaluated using the same protocol. The public databases MIT-BIH Arrhythmia and AF from PhysioNet were employed for the evaluation and comparison of these nine algorithms. Their performances were reported not only in terms of sensitivity (Se), specificity (Sp), positive predictive value (PPV), and error rate (Err), but also in terms of window length, input signal length, robustness to noise and computation time. These algorithms are based on the analysis of two characteristics observed in ECGs with AF: RR intervals Irregularity (RRI) and a chaotic atrial activity (AA). This AA can be analyzed in frequency (FA) and/or in time domain (P wave absence, PWA). Five algorithms were based on RRI; one algorithm was based only on AA; and the last three algorithms combined RRI with AA techniques.Ingeniería de TelecomunicaciónTelekomunikazio Ingeniaritz

Making medical treatments resilient to technological disruptions in telemedicine systems

Telemedicine depends on Information and Communication Technology (ICT) to support remote treatment of patients. This dependency requires the telemedicine system design to be resilient for ICT performance degradation or subsystem failures. Nevertheless, using telemedicine systems create a dependency between medical and technological concerns. We propose a layering technique that links medical and technological concerns by using a two-staged scenario based requirements elicitation method. This layering technique provides functional relations between technological variables (e.g. raw ECG signal) and their technological context (e.g. measurements conditions), clinical variables (e.g. heart rate), and clinical abstractions (e.g. physical exercise target heart rate) and the non-functional quality of data relations between the layers. We use a hierarchical ontology to specify these functional and non-functional relations, which enables the development of technological context and quality-aware telemedicine systems that are able to cope with technological disruptions whilst preserving patient safety

Quality-of-data broker for quality-of-data-aware telemedicine systems

Purpose: Telemedicine systems must provide clinical data of sufficient quality (according to medical standards) to support safe treatment guidance of outpatients. Quality of clinical data (QoD) typically varies due to unstable performance of ICT-components of these telemedicine systems. Therefore, telemedicine systems that support treatment guidance of outpatients should be QoD-aware and must be able to appropriately adapt treatment guidance to QoD variations. Only in this way, the effectiveness of treatment guidance and the safety of outpatients can be guaranteed. Methods: This paper follows a design science approach for the development of a functional architecture for QoD-aware telemedicine systems, with emphasis on one key component: the QoD Broker. Existing requirements elicitation methods were refined to deal with capturing QoD-specific requirements. Furthermore, an ontology-driven knowledge management method is proposed to enable the correct interpretation and manipulation of QoD within telemedicine systems. The functional architecture was validated using various methods, including prototype experiments and expert interviews. Result: One of the key components of the proposed functional architecture is the QoD Broker, which is a novel component that adds QoDawareness to telemedicine systems. The QoD Broker obtains quality of service data from ICT components within a given telemedicine system and uses different computational models to compute QoD. This paper presents the QoD Broker architecture and the QoD management techniques implemented in the QoD Broker: (1) QoD dimensions, (2) QoD evaluation (i.e. computational models), (3) QoD stratification models and (4) technological recommendations. Discussion: The paper presents partial results of the validation that was performed in the context of the European MobiGuide project. The validation confirms that the proposed QoD Broker satisfies stakeholders’ requirements and is considered useful to support stakeholders’ goals

Assessment of a personalized and distributed patient guidance system

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 fibril-lation (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