A health analytics semantic ETL service for obesity surveillance.

The increasingly large amount of data produced in healthcare (e.g. collected through health information systems such as electronic medical records - EMRs or collected through novel data sources such as personal health records - PHRs, social media, web resources) enable the creation of detailed records about people's health, sentiments and activities (e.g. physical activity, diet, sleep quality) that can be used in the public health area among others. However, despite the transformative potential of big data in public health surveillance there are several challenges in integrating big data. In this paper, the interoperability challenge is tackled and a semantic Extract Transform Load (ETL) service is proposed that seeks to semantically annotate big data to result into valuable data for analysis. This service is considered as part of a health analytics engine on the cloud that interacts with existing healthcare information exchange networks, like the Integrating the Healthcare Enterprise (IHE), PHRs, sensors, mobile applications, and other web resources to retrieve patient health, behavioral and daily activity data. The semantic ETL service aims at semantically integrating big data for use by analytic mechanisms. An illustrative implementation of the service on big data which is potentially relevant to human obesity, enables using appropriate analytic techniques (e.g. machine learning, text mining) that are expected to assist in identifying patterns and contributing factors (e.g. genetic background, social, environmental) for this social phenomenon and, hence, drive health policy changes and promote healthy behaviors where residents live, work, learn, shop and play.

Adaptive healthcare processes for personalized emergency clinical pathways.

Pre-hospital and in-hospital emergency healthcare delivery involves a variety of activities and people that should be coordinated in order effectively to create an emergency care plan. Emergency care provided by emergency healthcare professionals can be improved by personalized emergency clinical pathways that are instances of relevant emergency clinical guidelines based on emergency case needs as well as on ambulance and hospital resource availability, while also enabling better resource use. Business Process Management Systems (BPMSs) in conjunction with semantic technologies can be used to support personalized emergency clinical pathways by incorporating clinical guidelines logic into the emergency healthcare processes at run-time according to emergency care context information (current emergency case and resource information). On these grounds, a framework is proposed that uses ontology to model knowledge on emergency case medical history, on healthcare resource availability, on relevant clinical guidelines and on process logic; this is inferred to result in the most suitable process model for the case, in line with relevant clinical guidelines.

A cloud-based semantic wiki for user training in healthcare process management.

Successful healthcare process design requires active participation of users who are familiar with the cooperative and collaborative nature of healthcare delivery, expressed in terms of healthcare processes. Hence, a reusable, flexible, agile and adaptable training material is needed with the objective to enable users instill their knowledge and expertise in healthcare process management and (re)configuration activities. To this end, social software, such as a wiki, could be used as it supports cooperation and collaboration anytime, anywhere and combined with semantic web technology that enables structuring pieces of information for easy retrieval, reuse and exchange between different systems and tools. In this paper a semantic wiki is presented as a means for developing training material for healthcare providers regarding healthcare process management. The semantic wiki should act as a collective online memory containing training material that is accessible to authorized users, thus enhancing the training process with collaboration and cooperation capabilities. It is proposed that the wiki is stored in a secure virtual private cloud that is accessible from anywhere, be it an excessively open environment, while meeting the requirements of redundancy, high performance and autoscaling.

Using ESB and BPEL for evolving healthcare systems towards SOA.

Healthcare organizations often face the challenge of integrating diverse and geographically disparate information technology systems to respond to changing requirements and to exploit the capabilities of modern technologies. Hence, systems evolution, through modification and extension of the existing information technology infrastructure, becomes a necessity. This paper takes a process perspective of healthcare delivery within and across organizational boundaries and the presents a disciplined approach for evolving healthcare systems towards a service-oriented architecture using the enterprise system bus middleware technology for resolving integration issues and the business process execution language for supporting collaboration requirements.

Risk factors in ocular hypertension.

The aim of the study was to determine whether certain factors are related to an increased risk of developing open-angle glaucoma. A total of 345 untreated glaucoma suspects with intraocular pressure (IOP) > or = 21 mmHg, cup to disc ratio 0.4 or less and no visual field defects, were followed up for 6 to 8 years (mean 7.3). During the follow-up 71 patients developed established glaucoma and were compared to the remaining 274 patients. The following factors were analysed: age, family history of glaucoma, IOP, Humphrey 30-2 visual fields, optic disc appearance, myopia, exfoliation, arterial hypertension and diabetes. Analysis yielded statistically significant results regarding a number of these factors in the patients who subsequently developed open-angle glaucoma. A significant association with the subsequent development of field loss in ocular hypertension (OHT) included: heredity (p < 0.001), age > or = 60 years (p = 0.013), axial myopia (0.001 < p < 0.01) and arterial hypertension (p = 0.05). About 20% of patients with ocular hypertension developed glaucoma over a period of seven years. Risk factors such as heredity, age, myopia and arterial hypertension, among others, must be considered in the follow-up of glaucoma suspects.