Browsing by Author "Dias, P"
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- Developing and Validating High-Value Patient Digital Follow-Up Services: a Pilot Study in Cardiac SurgeryPublication . Londral, A; Azevedo, S; Dias, P; Ramos, C; Santos, J; Martins, F; Silva, R; Semedo, H; Vital, C; Gualdino, A; Falcão, J; Lapão, LV; Coelho, P; Fragata, JBackground: The existing digital healthcare solutions demand a service development approach that assesses needs, experience, and outcomes, to develop high-value digital healthcare services. The objective of this study was to develop a digital transformation of the patients' follow-up service after cardiac surgery, based on a remote patient monitoring service that would respond to the real context challenges. Methods: The study followed the Design Science Research methodology framework and incorporated concepts from the Lean startup method to start designing a minimal viable product (MVP) from the available resources. The service was implemented in a pilot study with 29 patients in 4 iterative develop-test-learn cycles, with the engagement of developers, researchers, clinical teams, and patients. Results: Patients reported outcomes daily for 30 days after surgery through Internet-of-Things (IoT) devices and a mobile app. The service's evaluation considered experience, feasibility, and effectiveness. It generated high satisfaction and high adherence among users, fewer readmissions, with an average of 7 ± 4.5 clinical actions per patient, primarily due to abnormal systolic blood pressure or wound-related issues. Conclusions: We propose a 6-step methodology to design and validate a high-value digital health care service based on collaborative learning, real-time development, iterative testing, and value assessment.
- Predicting Post-Discharge Complications in Cardiothoracic Surgery: a Clinical Decision Support System to Optimize Remote Patient Monitoring ResourcesPublication . Santos, R; Ribeiro, B; Sousa, I; Santos, J; Guede-Fernández, F; Dias, P; Carreiro, A; Gamboa, H; Coelho, P; Fragata, J; Londral, ACardiac surgery patients are highly prone to severe complications post-discharge. Close follow-up through remote patient monitoring can help detect adverse outcomes earlier or prevent them, closing the gap between hospital and home care. However, equipment is limited due to economic and human resource constraints. This issue raises the need for efficient risk estimation to provide clinicians with insights into the potential benefit of remote monitoring for each patient. Standard models, such as the EuroSCORE, predict the mortality risk before the surgery. While these are used and validated in real settings, the models lack information collected during or following the surgery, determinant to predict adverse outcomes occurring further in the future. This paper proposes a Clinical Decision Support System based on Machine Learning to estimate the risk of severe complications within 90 days following cardiothoracic surgery discharge, an innovative objective underexplored in the literature. Health records from a cardiothoracic surgery department regarding 5 045 patients (60.8% male) collected throughout ten years were used to train predictive models. Clinicians' insights contributed to improving data preparation and extending traditional pipeline optimization techniques, addressing medical Artificial Intelligence requirements. Two separate test sets were used to evaluate the generalizability, one derived from a patient-grouped 70/30 split and another including all surgeries from the last available year. The achieved Area Under the Receiver Operating Characteristic curve on these test sets was 69.5% and 65.3%, respectively. Also, additional testing was implemented to simulate a real-world use case considering the weekly distribution of remote patient monitoring resources post-discharge. Compared to the random resource allocation, the selection of patients with respect to the outputs of the proposed model was proven beneficial, as it led to a higher number of high-risk patients receiving remote monitoring equipment.
- Quando o Corpo Desperta numa Mente de CriançaPublication . Moreira, A; Farinha, M; Ganhoto, R; Dias, P
- Reabilitação de Reimplantação do Antebraço Distal Pós-Amputação - a Propósito de um Caso ClínicoPublication . Pisa, F; Dias, P; Moura, M; Braz, D; Fonseca, F; Rasteiro, DActualmente, o desafio da reimplantação do membro superior pós-amputação tornou-se uma realidade alcançável e minuciosamente aperfeiçoada nas últimas décadas, e em permanente evolução. A opção cirúrgica de reimplantação deve ter em conta não apenas a análise exclusiva da viabilidade do reimplante, mas, fundamentalmente o seu potencial de recuperação funcional a longo prazo. Apresenta-se o caso clínico de um jovem de 18 anos, fumador, transferido do Hospital do Barreiro, vítima de acidente de trabalho, com traumatismo por corte, do qual resultou amputação distal do antebraço direito. O tempo de isquemia quente foi de 4horas, tendo sido submetido a cirurgia de reimplantação conjunta por Ortopedia e Cirurgia Plástica e Reconstrutiva (CPR) para reimplantação. Na sequência da cirurgia, foi precocemente referenciado a Medicina Física e de Reabilitação (MFR), realizando um programa de reabilitação funcional sequencial. Este trabalho visa enfatizar a importância do papel da MFR num precoce, criterioso e extenso programa de reabilitação, factor fundamental na recuperação funcional e prognóstico a longo prazo destas lesões e prevenção de complicações.
- Sangue do meu SanguePublication . Martins, MM; Afonso, MH; Dias, P; Ganhoto, R
- Scaling-Up Digital Follow-Up Care Services: Collaborative Development and Implementation of Remote Patient Monitoring Pilot Initiatives to Increase Access to Follow-Up CarePublication . Azevedo, S; Guede-Fernández, F; Hafe, F; Dias, P; Lopes, I; Cardoso, N; Coelho, P; Santos, J; Fragata, J; Vital, C; Semedo, H; Gualdino, A; Londral, ABackground: 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.