CHRONIOUS: an open, ubiquitous and adaptive chronic disease management platform for chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD) and renal insufficiency

Scalable Architecture for Telemonitoring Chronic Diseases in Order to Support the CDSSs in a Common Platform

Introduction:

In recent years, a variety of clinical decision-support systems (CDSS) have been developed to monitor the health of patients with chronic disease from far away. These systems are effective in overcoming human resource limitation and analyzing information generated by Tele-monitoring systems. These systems, however, are limited to monitoring a particular disease, which allows them to be used only in one specific disease. In reuses of these systems to monitor other diseases, we need to re-establish a new system with a new knowledge base. However, this type of healthcare system faces many challenges, including low scalability for change, so that, if we want to modify a health monitoring system designed for a specific disease to be used for another disease, these changes will be very substantial, meaning that, most components of that system should be changed. The lack of scalability in these systems has led to the creation of multiple health monitoring systems, while many of these systems share a common structure.

Aim:

In this paper, to solve the scalability problem, architecture has been presented that allows a set of CDSSs to be placed on a common platform for Tele-monitoring.

Material and methods:

In order to provide the proposed architecture in this study, we extracted the related concepts from the literature. The anatomical concepts used in these studies are as follow: users, transmitted data, patient data storage databases, data transfer network, and medical setting and the work is done in this setting. Finally, to design the proposed architecture, UML has been used.

Results:

The innovation of this research is to provide a scalable and flexible architecture, which as a platform, is able to monitor multiple diseases with a common infrastructure. In this architecture, all components are commonly used simultaneously without the interference of several CDSSs.

Conclusion:

Utilizing the proposed model in this paper, while reducing the setup costs and speeding up the launch of various remote monitoring systems, many rework in the implementation of these systems is also reduced.

Towards a Middleware Based on SOA for Ubiquitous Care of Non-Communicable Diseases

According to World Health Organization, the treatment of non-communicable diseases needs more than patient engagement to help control the diseases. Community and health organizations support is also desirable for controlling them. This work details the UDuctor middleware, which was designed for supporting ubiquitous non-communicable disease care, and so, helping the integration between patient and community resources. The UDuctor middleware gives a step forward in relation to other architectures for ubiquitous applications by integrating patients, community resources and community members through a peer-to-peer network. Each peer runs a RESTFul based middleware, which enables messaging, resource sharing, context subscription and notification, and location between other UDuctor peers. The middleware implementation was employed in two solutions and tested in three experiments. The results are promising and show feasibility for the application of the middleware in real life situations.

Clinical Decision Support Systems (CDSS) for preventive management of COPD patients

BACKGROUND

The use of information and communication technologies to manage chronic diseases allows the application of integrated care pathways, and the optimization and standardization of care processes. Decision support tools can assist in the adherence to best-practice medicine in critical decision points during the execution of a care pathway.

OBJECTIVES

The objectives are to design, develop, and assess a clinical decision support system (CDSS) offering a suite of services for the early detection and assessment of chronic obstructive pulmonary disease (COPD), which can be easily integrated into a healthcare providers' work-flow.

METHODS

The software architecture model for the CDSS, interoperable clinical-knowledge representation, and inference engine were designed and implemented to form a base CDSS framework. The CDSS functionalities were iteratively developed through requirement-adjustment/development/validation cycles using enterprise-grade software-engineering methodologies and technologies. Within each cycle, clinical-knowledge acquisition was performed by a health-informatics engineer and a clinical-expert team.

RESULTS

A suite of decision-support web services for (i) COPD early detection and diagnosis, (ii) spirometry quality-control support, (iii) patient stratification, was deployed in a secured environment on-line. The CDSS diagnostic performance was assessed using a validation set of 323 cases with 90% specificity, and 96% sensitivity. Web services were integrated in existing health information system platforms.

CONCLUSIONS

Specialized decision support can be offered as a complementary service to existing policies of integrated care for chronic-disease management. The CDSS was able to issue recommendations that have a high degree of accuracy to support COPD case-finding. Integration into healthcare providers' work-flow can be achieved seamlessly through the use of a modular design and service-oriented architecture that connect to existing health information systems.

Triggers and monitoring in intelligent personal health record

Although Web-based personal health records (PHRs) have been widely deployed, the existing ones have limited intelligence. Previously, we introduced expert system technology and Web search technology into the PHR domain and proposed the concept of an intelligent PHR (iPHR). iPHR provides personalized healthcare information to facilitate users' daily activities of living. The current iPHR is passive and follows the pull model of information distribution. This paper introduces triggers and monitoring into iPHR to make iPHR become active. Our idea is to let medical professionals pre-compile triggers and store them in iPHR's knowledge base. Each trigger corresponds to an abnormal event that may have potential medical impact. iPHR keeps collecting, processing, and analyzing the user's medical data from various sources such as wearable sensors. Whenever an abnormal event is detected from the user's medical data, the corresponding trigger fires and the related personalized healthcare information is pushed to the user using natural language generation technology, expert system technology, and Web search technology.

Designing patient-centric applications for chronic disease management

Chronic diseases such as diabetes and heart disease are the leading causes of disability and death in the developed world. Technological interventions such as mobile applications have the ability to facilitate and motivate patients in chronic disease management, but these types of interventions present considerable design challenges. The primary objective of this paper is to present the challenges arising from the design and implementation of software applications aiming to assist patients in chronic disease management. We also outline preliminary results regarding a self-management application currently under development targeting young adults suffering from type 1 diabetes.