An intelligent telemonitoring service
1. Measures vital signs and symptoms
SmarBEAT,
a mobile Heart Failure (HF) telemonitoring kit,
will measure relevant outcomes for practitioners and patients during the telemonitoring period.
The following measures are monitored to ensure patient's safety:
- Physical activity levels
- Blood pressure and Heart Rate
- Rhythm pulmonary congestion
- Weight
- Therapeutic Compliance
2. Provides a close communication venue
A mobile app is used so that the patient is able to:
- Report measurements and symptoms
- Receive advices from the clinical team
- Take a picture of their operative wound
3. Data-driven scorecards for value assessment
Value for Health scorecards collect data during the follow-up which allows the clinical team to easily act upon:
- Comprehensive daily reports are automatically generated
- An alarm system which can detect harmful situations
- Data visualization tools that are used to easily understand patient's recovery status
4. Iterative service design based on intelligent algorithms
A multidisciplinary team composed by medical doctors, healthcare providers, doctorates, engineers, an economist, a linguist and a designer, made it possible to create a system that provides tools that improve patients' recovery by facilitating:
- Patient Empowerment and safety
- A decreased risk of post-surgical complications
- The automatic generation, delivery and integration of daily outcomed
- The reduction of the total health pathway costs by increasing the efficiency of the service
The Team
Ana Rita Londral
VOH.CoLAB
Ines Sousa
Fraunhofer AICOS
Pedro Coelho
NMS, HSM
Clara Vital
HSM
Ana Sofia Gualdino
HSM
Jorge Pinheiro Santos
NMS, HSM
Helena Semedo
HSM
Federico Guede Fernández
VOH.CoLAB
Ricardo Santos
Fraunhofer AICOS
Pedro Dias
VOH.CoLAB
Inês Lopes
Fraunhofer AICOS
Raquel Silva
VOH.CoLAB
Salomé Azevedo
VOH.CoLAB
Ana Martins
VOH.CoLAB
Nuno Cardoso
Fraunhofer AICOS
Francisco von Hafe
VOH.CoLAB
Bruno Ribeiro
Fraunhofer AICOS
João Costa
Fraunhofer AICOS AICOS
José Fragata
NMS, HSM
The Advisory board
Liliana Ferreira
Fraunhofer AICOS
Diane Whitehouse
EHTEL
Luis Lapão
NOVA University
Publications
Lopes I, Sousa F, Moreira E, Cardoso J. Smartphone-Based Remote Monitoring Solution for Heart Failure Patients. Stud Health Technol Inform. 2019;261:109-114. PMID: 31156100.
Azevedo, S. and Londral, A. (2020). Digital Innovation in Outpatient Healthcare Delivery Services: A Common Methodology to Introduce IoT Technologies in Two Use-cases.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-398-8, ISSN 2184-4305, pages 821-826.DOI: 10.5220/0009394908210826
Azevedo S, Rodrigues TC, Londral AR. Domains and Methods Used to Assess Home Telemonitoring Scalability: Systematic Review. JMIR Mhealth Uhealth. 2021 Aug 19;9(8):e29381. doi: 10.2196/29381. PMID: 34420917; PMCID: PMC8414303.
Dias, P., Cardoso, M., Guede-Fernandez, F., Martins, A. and Londral, A. (2022). Remote Patient Monitoring Systems based on Conversational Agents for Health Data Collection. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Scale-IT-up, ISBN 978-989-758-552-4; ISSN 2184-4305, pages 812-820. DOI: 10.5220/0011011000003123
Londral, A., Azevedo, S., Dias, P. et al. Developing and validating high-value patient digital follow-up services: a pilot study in cardiac surgery. BMC Health Serv Res 22, 680 (2022). https://doi.org/10.1186/s12913-022-08073-4
Martins, A., Nunes, I., Lapão, L. and Londral, A. (2023). Designing a Digital Personal Coach to Promote a Healthy Diet and Physical Activity Among Patients After Cardiothoracic Surgery. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Scale-IT-up, ISBN 978-989-758-631-6; ISSN 2184-4305, SciTePress, pages 595-602. DOI: 10.5220/0011776800003414
Curioso, I., Santos, R., Ribeiro, B., Carreiro, A., Coelho, P., Fragata, J., & Gamboa, H. (2023). Addressing the Curse of Missing Data in Clinical Contexts: A Novel Approach to Correlation-based Imputation. Journal of King Saud University - Computer and Information Sciences, 35(6), 101562. https://doi.org/10.1016/j.jksuci.2023.101562