Scaling-up digital follow-up care services
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2022 |
| Outros Autores: | , , , , , , , , , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/147550 |
Resumo: | Funding Information: This research has been supported by Fundação para a Ciência e Tecnologia (FCT) under CardioFollow.AI project (DSAIPA/AI/0094/2020) and Lisboa-05-3559-FSE-000003. Acknowledgments Funding Information: In 2020, in the scope of the COVID-19 pandemic, the Portuguese Foundation for Science and Technology (Fundação Portuguesa para a Ciência e Tecnologia - FCT) launched a tender to support Research and Development (R&D) projects in the areas of data science and artificial intelligence (AI) in Public Administration (). The main objective was to promote projects that could cope with pandemic-imposed challenges, improve public health services, and support citizens in better decision-making concerning health behaviors. FCT required the participation of at least one public administration entity providing health care committed to using the project results and the R&D activities. Another requirement was to provide a Data Management Plan that preserved the use of data ethical and legal aspects, such as privacy and consent issues in citizens’ data access, data sharing across different sources, and transparency of the analysis and utilization. The projects could last 24 to 36 months with a maximum funding limit per project of 240 thousand euros. This tender allocated 3 million euros from a national-based fund budget. Publisher Copyright: 2022 Azevedo, Guede-Fernández, von Hafe, Dias, Lopes, Cardoso, Coelho, Santos, Fragata, Vital, Semedo, Gualdino and Londral. |
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Scaling-up digital follow-up care servicescollaborative development and implementation of Remote Patient Monitoring pilot initiatives to increase access to follow-up carecollaborationfollow-up careimplementationparticipatory action researchremote patient monitoringHealth InformaticsComputer Science ApplicationsBiomedical EngineeringMedicine (miscellaneous)Funding Information: This research has been supported by Fundação para a Ciência e Tecnologia (FCT) under CardioFollow.AI project (DSAIPA/AI/0094/2020) and Lisboa-05-3559-FSE-000003. Acknowledgments Funding Information: In 2020, in the scope of the COVID-19 pandemic, the Portuguese Foundation for Science and Technology (Fundação Portuguesa para a Ciência e Tecnologia - FCT) launched a tender to support Research and Development (R&D) projects in the areas of data science and artificial intelligence (AI) in Public Administration (). The main objective was to promote projects that could cope with pandemic-imposed challenges, improve public health services, and support citizens in better decision-making concerning health behaviors. FCT required the participation of at least one public administration entity providing health care committed to using the project results and the R&D activities. Another requirement was to provide a Data Management Plan that preserved the use of data ethical and legal aspects, such as privacy and consent issues in citizens’ data access, data sharing across different sources, and transparency of the analysis and utilization. The projects could last 24 to 36 months with a maximum funding limit per project of 240 thousand euros. This tender allocated 3 million euros from a national-based fund budget. Publisher Copyright: 2022 Azevedo, Guede-Fernández, von Hafe, Dias, Lopes, Cardoso, Coelho, Santos, Fragata, Vital, Semedo, Gualdino and Londral.Background: COVID-19 increased the demand for Remote Patient Monitoring (RPM) services as a rapid solution for safe patient follow-up in a lockdown context. Time and resource constraints resulted in unplanned scaled-up RPM pilot initiatives posing risks to the access and quality of care. Scalability and rapid implementation of RPM services require social change and active collaboration between stakeholders. Therefore, a participatory action research (PAR) approach is needed to support the collaborative development of the technological component while simultaneously implementing and evaluating the RPM service through critical action-reflection cycles. Objective: This study aims to demonstrate how PAR can be used to guide the scalability design of RPM pilot initiatives and the implementation of RPM-based follow-up services. Methods: Using a case study strategy, we described the PAR team’s (nurses, physicians, developers, and researchers) activities within and across the four phases of the research process (problem definition, planning, action, and reflection). Team meetings were analyzed through content analysis and descriptive statistics. The PAR team selected ex-ante pilot initiatives to reflect upon features feedback and participatory level assessment. Pilot initiatives were investigated using semi-structured interviews transcribed and coded into themes following the principles of grounded theory and pilot meetings minutes and reports through content analysis. The PAR team used the MoSCoW prioritization method to define the set of features and descriptive statistics to reflect on the performance of the PAR approach. Results: The approach involved two action-reflection cycles. From the 15 features identified, the team classified 11 as must-haves in the scaled-up version. The participation was similar among researchers (52.9%), developers (47.5%), and physicians (46.7%), who focused on suggesting and planning actions. Nurses with the lowest participation (5.8%) focused on knowledge sharing and generation. The top three meeting outcomes were: improved research and development system (35.0%), socio-technical-economic constraints characterization (25.2%), and understanding of end-user technology utilization (22.0%). Conclusion: The scalability and implementation of RPM services must consider contextual factors, such as individuals’ and organizations’ interests and needs. The PAR approach supports simultaneously designing, developing, testing, and evaluating the RPM technological features, in a real-world context, with the participation of healthcare professionals, developers, and researchers.NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)Comprehensive Health Research Centre (CHRC) - pólo NMSFaculdade de Ciências e Tecnologia (FCT)LIBPhys-UNLRUNAzevedo, SaloméGuede-Fernández, Federicovon Hafe, FranciscoDias, PedroLopes, InêsCardoso, NunoCoelho, PedroSantos, JorgeFragata, JoséVital, ClaraSemedo, HelenaGualdino, AnaLondral, Ana2023-01-13T22:20:06Z2022-12-072022-12-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/147550eng2673-253XPURE: 49447210https://doi.org/10.3389/fdgth.2022.1006447info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:28:45Zoai:run.unl.pt:10362/147550Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:59.647899Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
Scaling-up digital follow-up care services collaborative development and implementation of Remote Patient Monitoring pilot initiatives to increase access to follow-up care |
| title |
Scaling-up digital follow-up care services |
| spellingShingle |
Scaling-up digital follow-up care services Azevedo, Salomé collaboration follow-up care implementation participatory action research remote patient monitoring Health Informatics Computer Science Applications Biomedical Engineering Medicine (miscellaneous) |
| title_short |
Scaling-up digital follow-up care services |
| title_full |
Scaling-up digital follow-up care services |
| title_fullStr |
Scaling-up digital follow-up care services |
| title_full_unstemmed |
Scaling-up digital follow-up care services |
| title_sort |
Scaling-up digital follow-up care services |
| author |
Azevedo, Salomé |
| author_facet |
Azevedo, Salomé Guede-Fernández, Federico von Hafe, Francisco Dias, Pedro Lopes, Inês Cardoso, Nuno Coelho, Pedro Santos, Jorge Fragata, José Vital, Clara Semedo, Helena Gualdino, Ana Londral, Ana |
| author_role |
author |
| author2 |
Guede-Fernández, Federico von Hafe, Francisco Dias, Pedro Lopes, Inês Cardoso, Nuno Coelho, Pedro Santos, Jorge Fragata, José Vital, Clara Semedo, Helena Gualdino, Ana Londral, Ana |
| author2_role |
author author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) Comprehensive Health Research Centre (CHRC) - pólo NMS Faculdade de Ciências e Tecnologia (FCT) LIBPhys-UNL RUN |
| dc.contributor.author.fl_str_mv |
Azevedo, Salomé Guede-Fernández, Federico von Hafe, Francisco Dias, Pedro Lopes, Inês Cardoso, Nuno Coelho, Pedro Santos, Jorge Fragata, José Vital, Clara Semedo, Helena Gualdino, Ana Londral, Ana |
| dc.subject.por.fl_str_mv |
collaboration follow-up care implementation participatory action research remote patient monitoring Health Informatics Computer Science Applications Biomedical Engineering Medicine (miscellaneous) |
| topic |
collaboration follow-up care implementation participatory action research remote patient monitoring Health Informatics Computer Science Applications Biomedical Engineering Medicine (miscellaneous) |
| description |
Funding Information: This research has been supported by Fundação para a Ciência e Tecnologia (FCT) under CardioFollow.AI project (DSAIPA/AI/0094/2020) and Lisboa-05-3559-FSE-000003. Acknowledgments Funding Information: In 2020, in the scope of the COVID-19 pandemic, the Portuguese Foundation for Science and Technology (Fundação Portuguesa para a Ciência e Tecnologia - FCT) launched a tender to support Research and Development (R&D) projects in the areas of data science and artificial intelligence (AI) in Public Administration (). The main objective was to promote projects that could cope with pandemic-imposed challenges, improve public health services, and support citizens in better decision-making concerning health behaviors. FCT required the participation of at least one public administration entity providing health care committed to using the project results and the R&D activities. Another requirement was to provide a Data Management Plan that preserved the use of data ethical and legal aspects, such as privacy and consent issues in citizens’ data access, data sharing across different sources, and transparency of the analysis and utilization. The projects could last 24 to 36 months with a maximum funding limit per project of 240 thousand euros. This tender allocated 3 million euros from a national-based fund budget. Publisher Copyright: 2022 Azevedo, Guede-Fernández, von Hafe, Dias, Lopes, Cardoso, Coelho, Santos, Fragata, Vital, Semedo, Gualdino and Londral. |
| publishDate |
2022 |
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2022-12-07 2022-12-07T00:00:00Z 2023-01-13T22:20:06Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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http://hdl.handle.net/10362/147550 |
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eng |
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eng |
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2673-253X PURE: 49447210 https://doi.org/10.3389/fdgth.2022.1006447 |
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