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

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

The MADE reference information model for interoperable pervasive telemedicine systems

Objectives: The main objective is to develop and validate a reference information model (RIM) to support semantic interoperability of pervasive telemedicine systems. The RIM is one component within a larger, computer-interpretable "MADE language" developed by the authors in the context of the MobiGuide project. To validate our RIM, we applied it to a clinical guideline for patients with gestational diabetes mellitus (GDM). Methods: The RIM is derived from a generic data flow model of disease management which comprises a network of four types of concurrent processes: Monitoring (M), Analysis (A), Decision (D) and Effectuation (E). This resulting MADE RIM, which was specified using the formal Vienna Development Method (VDM), includes six main, high-level data types representing measurements, observations, abstractions, action plans, action instructions and control instructions. Results: The authors applied the MADE RIM to the complete GDM guideline and derived from it a domain information model (DIM) comprising 61 archetypes, specifically 1 measurement, 8 observation, 10 abstraction, 18 action plan, 3 action instruction and 21 control instruction archetypes. It was observed that there are six generic patterns for transforming different guideline elements into MADE archetypes, although a direct mapping does not exist in some cases. Most notable examples are notifications to the patient and/or clinician as well as decision conditions which pertain to specific stages in the therapy. Conclusions: The results provide evidence that the MADE RIM is suitable for modelling clinical data in the design of pervasive telemedicine systems. Together with the other components of the MADE language, the MADE RIM supports development of pervasive telemedicine systems that are interoperable and independent of particular clinical applications

Use of the virtual medical record data model for communication among components of a distributed decision-support system

MobiGuide is a distributed decision-support system (DSS) that provides decision support for patients and physicians. Patients receive support using a light-weight Smartphone DSS linked to data arriving from wearable monitoring devices and physicians receive support via a web interface connected to a backend DSS that has access to an integrated personal health record (PHR) that stores hospital EMR data, monitoring data, and recommendations provided for the patient by the DSSs. The patient data model used by the PHR and by all the system components that interact in a service-oriented architecture is based on HL7's virtual medical record (vMR) model. We describe how we used and extended the vMR model to support communication between the system components for the complex workflow needed to support guidance of patients any time everywhere

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