Espieen I.D.,University of Barcelona |
Espieen I.D.,University Pompeu Fabra |
Vela E.,CatSalut |
Pauws S.,Philips |
And 23 more authors.
BMJ Open | Year: 2016
Objectives: Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario. Settings: The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL). Participants: Responsible teams for regional data management in the five ACT regions. Primary and secondary outcome measures: We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction. Results: There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment. Conclusions: The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation.
Miralles F.,Eurecat |
Vargiu E.,Eurecat |
Casals E.,University Pompeu Fabra |
Cordero J.A.,Polytechnic University of Catalonia |
International Journal of E-Health and Medical Communications | Year: 2015
Telemonitoring makes possible to remotely assess health status and quality of life of individuals. By acquiring heterogeneous data coming from sensors (physiological, biometric, environmental; non-invasive, adaptive and transparent to user) and data coming from other sources to become aware of user context; by inferring user behaviour and detecting anomalies from this data; and by providing elaborated and smart knowledge to clinicians, therapists, carers, families, and the patients themselves, we will be able to foster preventive, predictive and personalized care actions, decisions and support. In this paper, by relying on a novel sensor-based telemonitoring and home support system, the authors are focused on monitoring mobility activities; the ultimate goal being to automatically assess quality of life of people. In particular, the authors are aimed at answering to an item of a quality-oflife questionnaire, namely "Mobility". Although the authors are interested in assisting disabled people, they performed preliminary experiments with a healthy user, as a proof of concept. Results show that the approach is promising. Thus, the authors are now in the process to install the final system in a number of disabled people's homes under the umbrella of the BackHome project. Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Subirats L.,Eurecat |
Subirats L.,Autonomous University of Barcelona |
Lopez-Blazquez R.,Institute Universitari Of Neurorehabilitacio Adscrit Uab |
Ceccaroni L.,1000001 Labs |
And 4 more authors.
International Journal of Environmental Research and Public Health | Year: 2015
The objective of this research is to provide a standardized platform to monitor and predict indicators of people with traumatic brain injury using the International Classification of Functioning, Disability and Health, and analyze its potential benefits for people with disabilities, health centers and administrations. We developed a platform that allows automatic standardization and automatic graphical representations of indicators of the status of individuals and populations. We used data from 730 people with acquired brain injury performing periodic comprehensive evaluations in the years 2006–2013. Health professionals noted that the use of color-coded graphical representation is useful for quickly diagnose failures, limitations or restrictions in rehabilitation. The prognosis system achieves 41% of accuracy and sensitivity in the prediction of emotional functions, and 48% of accuracy and sensitivity in the prediction of executive functions. This monitoring and prognosis system has the potential to: (1) save costs and time, (2) provide more information to make decisions, (3) promote interoperability, (4) facilitate joint decision-making, and (5) improve policies of socioeconomic evaluation of the burden of disease. Professionals found the monitoring system useful because it generates a more comprehensive understanding of health oriented to the profile of the patients, instead of their diseases and injuries. © 2015 by the authors; licensee MDPI, Basel, Switzerland.
Garcia R.,Eurecat |
Gomez D.,University of Santiago de Chile |
Parra D.,University of Santiago de Chile |
Trattner C.,Norwegian University of Science and Technology |
And 2 more authors.
HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media | Year: 2015
Using Twitter during academic conferences is a way of engaging and connecting an audience inherently multicultural by the nature of scientific collaboration. English is expected to be the lingua franca bridging the communication and integration between native speakers of different mother tongues. However, little research has been done to support this assumption. In this paper we analyzed how integrated language communities are by analyzing the scholars' tweets used in 26 Computer Science conferences over a time span of five years. We found that although English is the most popular language used to tweet during conferences, a significant proportion of people also tweet in other languages. In addition, people who tweet solely in English interact mostly within the same group (English monolinguals), while people who speak other languages interact more with different lingua groups. Finally, we also found higher interaction between people tweeting in different languages.These results suggest a relation between the number of languages a user speaks and their interaction dynamics in online communities. © 2015 ACM.
Imran M.,Qatar Computing Research Institute |
Meier P.,The World Bank |
Castillo C.,Eurecat |
Lesa A.,UNICEF |
DH 2016 - Proceedings of the 2016 Digital Health Conference | Year: 2016
In response to the growing HIV/AIDS and other health-related issues, UNICEF through their U-Report platform receives thousands of messages (SMS) every day to pro-vide prevention strategies, health case advice, and counsel-ing support to vulnerable population. Due to a rapid in-crease in U-Report usage (up to 300% in last 3 years), plus approximately 1,000 new registrations each day, the volume of messages has thus continued to increase, which made it impossible for the team at UNICEF to process them in a timely manner. In this paper, we present a platform de-signed to perform automatic classification of short messages (SMS) in real-Time to help UNICEF categorize and prior-itize health-related messages as they arrive. We employ a hybrid approach, which combines human and machine intel-ligence that seeks to resolve the information overload issue by introducing processing of large-scale data at high-speed while maintaining a high classification accuracy. The sys-Tem has recently been tested in conjunction with UNICEF in Zambia to classify short messages received via the U-Report platform on various health related issues. The system is designed to enable UNICEF make sense of a large volume of short messages in a timely manner. In terms of evalua-Tion, we report design choices, challenges, and performance of the system observed during the deployment to validate its effectiveness.