Ana Londral
@vohcolab.org
Researcher in Comprehensive Health Research Center and Director in Value for Health CoLAB
NOVA Medical School; Value for Health CoLAB
Ana was graduated in Electrical and Computer Engineering (Instituto Superior Técnico, University of Lisbon), 1999, is MSc. in Health Engineering (Portuguese Catholic University), 2017, and PhD in Biomedical Sciences/Neurosciences (Faculty of Medicine, University of Lisbon), 2016. She lectured Assistive Technologies, Machine Learning and Computer Programming courses in Portuguese Universities and has been involved in many national and international R&D projects and multidisciplinary teams.
Ana is director of Value for Health CoLAB and investigator in Comprehensive Health Research Center, Nova Medical School.
EDUCATION
2016 - PhD in Biomedical Sciences (Neurosciences)
Doctoral Programme in Neurosciences
Faculty of Medicine, University of Lisbon. Approved with Honours Distinction.
2007 - Master in Clinical Engineering
Universidade Católica Portuguesa. Approved with Excellent.
1999 - Electrical and Computer Engineering
Instituto Superior Técnico, University of Lisbon.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Ana Martins, Ana Londral, Isabel L. Nunes, and Luís V. Lapão
Elsevier BV
Joana Alegrete, Nuno Batalha, Orlando Fernandes, Jose Alberto Parraca, Ana Maria Rodrigues, Ana Rita Londral, and João Paulo Sousa
BMJ
Patients who cannot fully comply with conventional clinic-based rehabilitation (CR) sessions after ACL reconstruction (ACLR) may find additional internet-based sessions beneficial. These remote sessions include therapeutic exercises that can be done at home, potentially extending the reach of rehabilitation services to underserved areas, prolonging the duration of care and providing improved supervision. The study’s main purpose is to determine if the Knee Care at Home (KC@H) programme is more effective than conventional CR alone in improving patient-reported, clinician-reported and physical functional performance outcome measures after ACLR. Additionally, the trial assesses the significance of changes in outcome measures for clinical practice.This protocol outlines a randomised controlled trial for postoperative recovery following ACLR. Adult participants of both sexes who meet specific criteria will be randomly assigned to either the CR group or the KC@H group. Only the latter group will receive internet-based sessions of therapeutic exercises at home and CR sessions. A follow-up evaluation will be conducted for both groups 12 weeks after the intervention ends.The trial protocol was approved by the Ethics Committee of the Universidade de Évora and complies with the Code of Ethics of the World Medical Association. All recordings will be stored on a secure server with limited access and deleted as soon as they are no longer needed.The KC@H programme is expected to be superior to conventional CR for patients recovering from ACLR across multiple outcome measures. Also, the programme has the potential to promote superior recovery and extend the reach and duration of care.Trial registration number:NCT05828355.
Ricardo Santos, Bruno Ribeiro, Inês Sousa, Jorge Santos, Federico Guede-Fernández, Pedro Dias, André V. Carreiro, Hugo Gamboa, Pedro Coelho, José Fragata,et al.
Elsevier BV
João Silva, Matilde Silva, Bruno Soares, Carla Quintão, Ana Rita Londral, and Cláudia Quaresma
Informa UK Limited
Congenital limb defects occur when a limb does not develop normally during pregnancy. The quality of each person's everyday life is significantly impacted by any of these defects and there is no concrete treatment. 3D modeling and printing, enables the creation and customization of precise virtual and/or physical models, including models of the human anatomy. These technologies provide a novel method of producing new devices with optimized design and production time, improving adaptability, and incorporating functionality. To this end, we propose a method of designing and producing 3D printed assistive devices and we also present an example of an assistive device, done in the 3D Printing Center for Health, as well as its impact on the patient's daily life. With this device, the patient became able to play the guitar and hold a knife, thus helping on these two activities.
Catarina Pereira, Federico Guede-Fernández, Ricardo Vigário, Pedro Coelho, José Fragata, and Ana Londral
MDPI AG
Cardiothoracic surgery patients have the risk of developing surgical site infections which cause hospital readmissions, increase healthcare costs, and may lead to mortality. This work aims to tackle the problem of surgical site infections by predicting the existence of worrying alterations in wound images with a wound image analysis system based on artificial intelligence. The developed system comprises a deep learning segmentation model (MobileNet-Unet), which detects the wound region area and categorizes the wound type (chest, drain, and leg), and a machine learning classification model, which predicts the occurrence of wound alterations (random forest, support vector machine and k-nearest neighbors for chest, drain, and leg, respectively). The deep learning model segments the image and assigns the wound type. Then, the machine learning models classify the images from a group of color and textural features extracted from the output region of interest to feed one of the three wound-type classifiers that reach the final binary decision of wound alteration. The segmentation model achieved a mean Intersection over Union of 89.9% and a mean average precision of 90.1%. Separating the final classification into different classifiers was more effective than a single classifier for all the wound types. The leg wound classifier exhibited the best results with an 87.6% recall and 52.6% precision.
Bruno Ribeiro, Isabel Curioso, Ricardo Santos, Federico Guede-Fernández, Pedro Coelho, Jorge Santos, José Fragata, Ana Londral, and Inês Sousa
Springer Nature Switzerland
Ricardo Santos, Bruno Ribeiro, Pedro Dias, Isabel Curioso, Pedro Madeira, Federico Guede-Fernández, Jorge Santos, Pedro Coelho, Inês Sousa, and Ana Londral
Springer Nature Switzerland
Salomé Azevedo, Federico Guede-Fernández, Francisco von Hafe, Pedro Dias, Inês Lopes, Nuno Cardoso, Pedro Coelho, Jorge Santos, José Fragata, Clara Vital,et al.
Frontiers Media SA
BackgroundCOVID-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.ObjectiveThis 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.MethodsUsing 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.ResultsThe 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%).ConclusionThe 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.
Simão Gonçalves, Francisco von Hafe, Flávio Martins, Carla Menino, Maria José Guimarães, Andreia Mesquita, Susana Sampaio, and Ana Rita Londral
Springer Science and Business Media LLC
Abstract Background Emergency department (ED) High users (HU), defined as having more than ten visits to the ED per year, are a small group of patients that use a significant proportion of ED resources. The High Users Resolution Group (GRHU) identifies and provides care to HU to improve their health conditions and reduce the frequency of ED visits by delivering patient-centered case management integrated care. The main objective of this study was to measure the impact of the GRHU intervention in reducing ED visits, outpatient appointments, and hospitalizations. As secondary objectives, we aimed to compare the GRHU intervention costs against its potential savings or additional costs. Finally, we intend to study the impact of this intervention across different groups of patients. Methods We studied the changes triggered by the GRHU program in a retrospective, non-controlled before-after analysis of patients’ hospital utilization data on 6 and 12-month windows from the first appointment. Results A total of 238 ED HU were intervened. A sample of 152 and 88 patients was analyzed during the 6 and 12-month window, respectively. On the 12-month window, GRHU intervention was associated with a statistically significant reduction of 51% in ED visits and hospitalizations and a non-statistically significant increase in the total number of outpatient appointments. Overall costs were reduced by 43.56%. We estimated the intervention costs to be €79,935.34. The net cost saving was €104,305.25. The program’s Return on Investment (ROI) was estimated to be €2.3. Conclusion Patient-centered case management for ED HU seems to effectively reduce ED visits and hospitalizations, leading to better use of resources.
A. Londral, S. Azevedo, P. Dias, C. Ramos, J. Santos, F. Martins, R. Silva, H. Semedo, C. Vital, A. Gualdino,et al.
Springer Science and Business Media LLC
Abstract Background 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.
Eduarda Oliosi, Federico Guede-Fernández, and Ana Londral
MDPI AG
Frailty characterizes a state of impairments that increases the risk of adverse health outcomes such as physical limitation, lower quality of life, and premature death. Frailty prevention, early screening, and management of potential existing conditions are essential and impact the elderly population positively and on society. Advanced machine learning (ML) processing methods are one of healthcare’s fastest developing scientific and technical areas. Although research studies are being conducted in a controlled environment, their translation into the real world (clinical setting, which is often dynamic) is challenging. This paper presents a narrative review of the procedures for the frailty screening applied to the innovative tools, focusing on indicators and ML approaches. It results in six selected studies. Support vector machine was the most often used ML method. These methods apparently can identify several risk factors to predict pre-frail or frailty. Even so, there are some limitations (e.g., quality data), but they have enormous potential to detect frailty early.
Ana Londral
Frontiers Media SA
Hannes Schlieter, Lisa A Marsch, Diane Whitehouse, Lena Otto, Ana Rita Londral, Gisbert Wilhelm Teepe, Martin Benedict, Joseph Ollier, Tom Ulmer, Nathalie Gasser,et al.
JMIR Publications Inc.
Health care delivery is undergoing a rapid change from traditional processes toward the use of digital health interventions and personalized medicine. This movement has been accelerated by the COVID-19 crisis as a response to the need to guarantee access to health care services while reducing the risk of contagion. Digital health scale-up is now also vital to achieve population-wide impact: it will only accomplish sustainable effects if and when deployed into regular health care delivery services. The question of how sustainable digital health scale-up can be successfully achieved has, however, not yet been sufficiently resolved. This paper identifies and discusses enablers and barriers for scaling up digital health innovations. The results discussed in this paper were gathered by scientists and representatives of public bodies as well as patient organizations at an international workshop on scaling up digital health innovations. Results are explored in the context of prior research and implications for future work in achieving large-scale implementations that will benefit the population as a whole.
William H Seligman, Luz Fialho, Nick Sillett, Christina Nielsen, Farhala M Baloch, Philip Collis, Ingel K M Demedts, Marcelo P Fleck, Maiara A Floriani, Lucinda E K Gabriel,et al.
BMJ Open BMJ
ObjectivesThe COVID-19 pandemic has resulted in widespread morbidity and mortality with the consequences expected to be felt for many years. Significant variation exists in the care even of similar patients with COVID-19, including treatment practices within and between institutions. Outcome measures vary among clinical trials on the same therapies. Understanding which therapies are of most value is not possible unless consensus can be reached on which outcomes are most important to measure. Furthermore, consensus on the most important outcomes may enable patients to monitor and track their care, and may help providers to improve the care they offer through quality improvement. To develop a standardised minimum set of outcomes for clinical care, the International Consortium for Health Outcomes Measurement (ICHOM) assembled a working group (WG) of 28 volunteers, including health professionals, patients and patient representatives.DesignA list of outcomes important to patients and professionals was generated from a systematic review of the published literature using the MEDLINE database, from review of outcomes being measured in ongoing clinical trials, from a survey distributed to patients and patient networks, and from previously published ICHOM standard sets in other disease areas. Using an online-modified Delphi process, the WG selected outcomes of greatest importance.ResultsThe outcomes considered by the WG to be most important were selected and categorised into five domains: (1) functional status and quality of life, (2) mental functioning, (3) social functioning, (4) clinical outcomes and (5) symptoms. The WG identified demographic and clinical variables for use as case-mix risk adjusters. These included baseline demographics, clinical factors and treatment-related factors.ConclusionImplementation of these consensus recommendations could help institutions to monitor, compare and improve the quality and delivery of care to patients with COVID-19. Their consistent definition and collection could also broaden the implementation of more patient-centric clinical outcomes research.
Salome Azevedo, Teresa Cipriano Rodrigues, and Ana Rita Londral
JMIR Publications Inc.
Background The COVID-19 pandemic catalyzed the adoption of home telemonitoring to cope with social distancing challenges. Recent research on home telemonitoring demonstrated benefits concerning the capacity, patient empowerment, and treatment commitment of health care systems. Moreover, for some diseases, it revealed significant improvement in clinical outcomes. Nevertheless, when policy makers and practitioners decide whether to scale-up a technology-based health intervention from a research study to mainstream care delivery, it is essential to assess other relevant domains, such as its feasibility to be expanded under real-world conditions. Therefore, scalability assessment is critical, and it encompasses multiple domains to ensure population-wide access to the benefits of the growing technological potential for home telemonitoring services in health care. Objective This systematic review aims to identify the domains and methods used in peer-reviewed research studies that assess the scalability of home telemonitoring–based interventions under real-world conditions. Methods The authors followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines and used multiple databases (PubMed, Scopus, Web of Science, and EconLit). An integrative synthesis of the eligible studies was conducted to better explore each intervention and summarize relevant information concerning the target audience, intervention duration and setting, and type of technology. Each study design was classified based on the strength of its evidence. Lastly, the authors conducted narrative and thematic analyses to identify the domains, and qualitative and quantitative methods used to support scalability assessment. Results This review evaluated 13 articles focusing on the potential of scaling up a home telemonitoring intervention. Most of the studies considered the following domains relevant for scalability assessment: problem (13), intervention (12), effectiveness (13), and costs and benefits (10). Although cost-effectiveness was the most common evaluation method, the authors identified seven additional cost analysis methods to evaluate the costs. Other domains were less considered, such as the sociopolitical context (2), workforce (4), and technological infrastructure (3). Researchers used different methodological approaches to assess the effectiveness, costs and benefits, fidelity, and acceptability. Conclusions This systematic review suggests that when assessing scalability, researchers select the domains specifically related to the intervention while ignoring others related to the contextual, technological, and environmental factors, which are also relevant. Additionally, studies report using different methods to evaluate the same domain, which makes comparison difficult. Future work should address research on the minimum required domains to assess the scalability of remote telemonitoring services and suggest methods that allow comparison among studies to provide better support to decision makers during large-scale implementation.
Nafiseh Mollaei, Ana Rita Londral, Catia Cepeda, Salome Azevedo, Jorge Pinheiro Santos, Pedro Coelho, Jose Fragata, and Hugo Gamboa
IEEE
The goal of this study was to apply machine learning (ML) methods to predict the Length of Stay in an Intensive Care Unit (LOS-ICU) based on preoperative factors. To optimize the capacity of the ICU in surgery department, the prediction of a long stay (more than 2 days) can support the clinical decision making on accepting or delaying a patient intervention, considering the ICU occupancy. A database with records from 7364 patients that were operated in the Cardiothoracic surgery department of a public Portuguese hospital was used as the base of ML algorithms training. Regarding the risk of the patients to be in the group of long LOS-ICU, we compared five machine learning algorithms including Gradient Boosting, Random Forest, Support Vector Machine (SVM), Adaboost and Logistic Regression. We studied the classifier performance to adjust the sensitivity of a long stay classification, in order to reduce the potential of long LOS-ICU classification being miss classified as a short LOS-ICU.
Pedro Gómez, Ana R. M. Londral, Andrés Gómez, Daniel Palacios, and Victoria Rodellar
Springer Science and Business Media LLC
Ana Londral, Pedro Gómez Vilda, and Andrés Gómez-Rodellar
SCITEPRESS - Science and Technology Publications
A majority of patients with Amyotrophic Lateral Sclerosis (ALS) experiment a rapid evolution of symptoms related to a progressive decline in movement function that affects different systems. Clinical assessment is based on measures of progression for identifying the need and the pace of medical decisions, and to measure also the effects of novel therapies. But assessment is limited to the periodicity of clinical appointments that are increasingly difficult for patients due to progressive mobility impairments. In this paper, we present a novel method to assess neurodegeneration process through speech analysis. An articulation kinematic model is proposed to identify markers of neuromotor functional progression in speech. We analysed speech samples that were collected with a mobile device, in 3-month intervals, from a group of six subjects with ALS. Results suggest that the method proposed is sensitive to the symptoms of the disease, as rated by observational clinical scales, and it may contribute to assist clinicians and researchers with better and continuous measures of disease progression.
P. P.Gomez, D. Palacios, A. Gomez, V. Rodellar, and A. R. Londral
IEEE
Patients affected by Amyotrophic Lateral Sclerosis (ALS) show specific dysarthric clues in speech. These marks could be used to detect early symptoms and monitor the evolution of the disease in time. Classically articulation marks have been mainly based on static premises. Articulation Kinematics from acoustic correlates may help in producing measurements based on the dynamic behavior of speech. Specifically, distribution functions from the absolute kinematic velocity estimated by a simplified articulation model can be used in establishing distances based on Information Theory concepts between running speech segments from patients and controls. As an example, a longitudinal case of ALS has been studied using this methodology. It shows that the performance of dynamic articulation quality correlates may be more sensitive and robust than static ones. Conclusions foresee the use of speech as a valuable monitoring methodology for ALS timely evolution.
Cristina Carmona-Duarte, Réjean Plamondon, Pedro Gómez-Vilda, Miguel A. Ferrer, Jesús B. Alonso, and Ana Rita M. Londral
Springer International Publishing
Ana Londral, Susana Pinto, and Mamede de Carvalho
Elsevier BV
Ana Londral, Anabela Pinto, Susana Pinto, Luis Azevedo, and Mamede De Carvalho
Wiley
Introduction: In this study we performed a longitudinal investigation to assess the impact of early introduction of assistive communication devices (ACDs) on quality of life (QoL) in amyotrophic lateral sclerosis (ALS) patients and their caregivers. Methods: Patients were followed for 7–10 months (3 evaluation periods). Bulbar‐onset ALS patients (N = 27) and paired caregivers (N = 17) were included. Fifteen randomly selected patients received early support in ACD use. Patients were assessed using the ALS Functional Rating Scale—revised (ALSFRS‐R), the McGill QoL (MQoL), the Communication Effectiveness Index (CETI), and performance in writing; and caregivers were assessed with the MQoL and World Health Organization Quality of Life questionnaire (WHOQOL‐BREF). Results: Patients with early support had higher MQoL Psychological and MQoL Existential well‐being domains; caregivers had higher MQoL Support domain and their MQoL Psychological domain positively associated with patient CETI. Most patients could communicate using a touchscreen keyboard to write, even when handwriting and speech were not possible. Conclusion: Early intervention with an ACD seems to have a positive impact on QoL and gives patients the opportunity to improve skills for communication in later disease stages. Muscle Nerve 52: 933–941, 2015