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Background: Left atrial (LA) fibrosis has been shown to be associated with atrial fibrillation (AF) recurrence. Beat-to-beat (B2B) index is a non-invasive classifier, based on B2B P-wave morphological and wavelet analysis, shown to be associated with AF incidence and recurrence. In this study, we tested the hypothesis that the B2B index is associated with the extent of LA low-voltage areas (LVAs) on electroanatomical mapping., Methods: Patients with paroxysmal AF scheduled for pulmonary vein isolation, without evident structural remodeling, were included. Pre-ablation electroanatomical voltage maps were used to calculate the surface of LVAs (<0.5 mV). B2B index was compared between patients with small versus large LVAs., Results: 35 patients were included (87% male, median age 62). The median surface area of LVAs was 7.7 (4.4-15.8) cm 2 corresponding to 5.6 (3.3-12.1) % of LA endocardial surface. B2B index was 0.57 (0.52-0.59) in patients with small LVAs (below the median) compared to 0.65 (0.56-0.77) in those with large LVAs (above the median) (p = 0.009). In the receiver operator characteristic curve analysis for predicting large LVAs, the c-statistic was 0.75 (p = 0.006) for B2B index and 0.81 for the multivariable model including B2B index (multivariable p = 0.04) and P-wave duration., Conclusion: In patients with paroxysmal AF without overt atrial myopathy, B2B P-wave analysis appears to be a useful non-invasive correlate of low-voltage areas-and thus fibrosis-in the LA. This finding establishes a pathophysiological basis for B2B index and its potential usefulness in the selection process of patients who are likely to benefit most from further invasive treatment., Competing Interests: Declarations of interest None., (Copyright © 2024 Hellenic Society of Cardiology. Published by Elsevier Inc. All rights reserved.)

The P wave, representing the electrical fingerprint of atrial depolarization, contains information regarding spatial and temporal aspects of atrial electrical-and potentially structural-properties. However, technical and biological reasons, including-but not limited to-the low amplitude of the P wave and large interindividual variations in normal or pathologic atrial electrical activity, make gathering and utilizing this information for clinical purposes a rather cumbersome task. However, even crude ECG descriptors, such as P-wave dispersion, have been shown to be of predictive value for assessing the probability that a patient already has or will shortly present with AF. More sophisticated methods of analyzing the ECG signal, on a single- or multi- beat basis, along with novel approaches to data handling, namely machine learning, seem to be leading up to more accurate and robust ways to obtain clinically useful information from the humble P wave., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Purpose: A recent study published in the JMIR Med Educ Journal explored the potential impact of the Generative Pre-Train (ChatGPT), a generative language model, on medical education, research, and practice. In the present study, an interview with ChatGPT was conducted to determine its capabilities and potential for use in anatomy education (AE) and anatomy research (AR)., Methods: The study involved 18 questions asked of ChatGPT after obtaining an online subscription to the 4th edition. The questions were randomly selected and evaluated based on accuracy, relevance, and comprehensiveness., Results: The ChatGPT provided accurate and well-structured anatomical descriptions, including clinical relevance and relationships between structures. The chatbot also offered concise summaries of chapters and helpful advice on anatomical terminology, even with complex terms. However, when it came to anatomical variants and their clinical significance, the chatbot's replies were inadequate unless variants were systematically classified into types. ChatGPT-4 generated multiple-choice quizzes and matching questions of varying difficulty levels, as well as summaries of articles when presented with text. However, the chatbot recognized its limitations in terms of accuracy, as did the authors of the current study., Conclusion: ChatGPT-4 can be a valuable interactive educational tool for students in the field of anatomy, encouraging engagement and further questions. However, it cannot replace the critical role of educators and should be used as a complementary tool. Future research should establish guidelines for ChatGPT's optimal use and application in medical education., (© 2023. The Author(s).)

Data harmonization is one of the greatest challenges in cancer imaging studies, especially when it comes to multi-source data provision. Properly integrated data deriving from various sources can ensure data fairness on one side and can lead to a trusted dataset that will enhance AI engine development on the other side. Towards this direction, we are presenting a data integration quality check tool that ensures that all data uploaded to the repository are homogenized and share the same principles. The tool's aim is to report any human-induced errors and propose corrective actions. It focuses on checking the data prior to their upload to the repository in five levels: (i) clinical metadata integrity, (ii) template-imaging consistency, (iii) anonymization protocol applied, (iv) imaging analysis requirements, (v) case completeness. The tool produces reports with the corrective actions that must be followed by the user. This way the tool ensures that the data that will become available to the developers of the AI engine are homogenized, properly structured and contain all the necessary information needed for the analysis. The tool was validated in two rounds, internal and external, and at the user experience level. Clinical Relevance- Supporting the harmonized preparation and provision of medical imaging data and related clinical data will ensure data fairness and enhance the AI development.

The identification of patients prone to atrial fibrillation (AF) relapse after catheter ablation is essential for better patient selection and risk stratification. The current prospective cohort study aims to validate a novel P-wave index based on beat-to-beat (B2B) P-wave morphological and wavelet analysis designed to detect patients with low burden AF as a predictor of AF recurrence within a year after successful catheter ablation. From a total of 138 consecutive patients scheduled for AF ablation, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained. Univariate analysis revealed that patients with higher B2B P-wave index had a two-fold risk for AF recurrence (HR: 2.35, 95% CI: 1.24-4.44, p : 0.010), along with prolonged P-wave, interatrial block, early AF recurrence, female gender, heart failure history, previous stroke, and CHA 2 DS 2 -VASc score. Multivariate analysis of assessable predictors before ablation revealed that B2B P-wave index, along with heart failure history and a history of previous stroke or transient ischemic attack, are independent predicting factors of atrial fibrillation recurrence. Further studies are needed to assess the predictive value of the B2B index with greater accuracy and evaluate a possible relationship with atrial substrate analysis.

Atrial Fibrillation (AF) is the most common cardiac arrhythmia, and its progressive nature is associated with gradual atrial remodeling. The P-wave in the surface Electrocardiogram (ECG) reflects the atrial activation, while the modification of the atrial pathophysiological properties leads to P-wave morphology (PWM) alternations. In paroxysmal AF (pAF), the modifications of the PWM may have a spontaneous rather than permanent presence in the ECG signal. The analysis of the P-waves, during sinus rhythm, on a beat-to-beat basis, has revealed the existence of at least two PWM. In addition, the wavelet characteristics of the P-wave matching the main morphology can accurately distinguish the patients with pAF from healthy volunteers. In this work, we examine the hypothesis that there is an effect of the anti-arrhythmic medication on beat-to-beat PWM alternations of pAF patients. ECG signals of high frequency (1000Hz), in the three orthogonal leads, were collected for 81 pAF patients of minimal and mild AF burden, 47 of which receiving antiarrhythmic medication treatment, and from 56 healthy volunteers. Kruskal-Wallis test was performed, and the preliminary results denote the existence of statistically significant differences between the groups. A 3-class Random Forest classifier was trained, using the forward wrapper approach, resulting in a high overall classification performance (AUC = 85.75%). This analysis is a step towards improving understanding of medication effect on the variability of P-wave.Clinical Relevance- The methodology presented in this paper can be used to perform a non-invasive characterization of low burden pAF patients using the ECG recording.

Cancer research is increasing relying on data-driven methods and Artificial Intelligence (AI), to increase accuracy and efficiency in decision making. Such methods can solve a variety of clinically relevant problems in cancer diagnosis and treatment, provided that an adequate data availability is ensured. The generation of multicentric data repositories poses a series of integration and harmonization challenges. This work discusses the strategy, solutions and further issues identified along this procedure within the EU project INCISIVE that aims to generate an interoperable pan-European federated repository of medical images and an AI-based toolbox for medical imaging in cancer diagnosis and treatment.Clinical Relevance- Supporting the integration of medical imaging data and related clinical data into large interoperable repositories will enable the development, and validation, and wider adoption of AI-based methods in cancer diagnosis, prediction, treatment and follow-up.

Early identification of patients at risk for paroxysmal atrial fibrillation (PAF) is essential to attain optimal treatment and a favorable prognosis. We compared the performance of a beat-to-beat (B2B) P-wave analysis with that of standard P-wave indices (SPWIs) in identifying patients prone to PAF. To this end, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained from 33 consecutive, antiarrhythmic therapy naïve patients, with a short history of low burden PAF, and from 56 age- and sex-matched individuals with no AF history. For both groups, SPWIs were calculated, while the VCG recordings were analyzed on a B2B basis, and the P-waves were classified to a primary or secondary morphology. Wavelet transform was used to further analyze P-wave signals of main morphology. Univariate analysis revealed that none of the SPWIs performed acceptably in PAF detection, while five B2B features reached an AUC above 0.7. Moreover, multivariate logistic regression analysis was used to develop two classifiers-one based on B2B analysis derived features and one using only SPWIs. The B2B classifier was found to be superior to SPWIs classifier; B2B AUC: 0.849 (0.754-0.917) vs. SPWIs AUC: 0.721 (0.613-0.813), p value: 0.041. Therefore, in the studied population, the proposed B2B P-wave analysis outperforms SPWIs in detecting patients with PAF while in sinus rhythm. This can be used in further clinical trials regarding the prognosis of such patients.

Background: Obesity is a major public health problem globally and in Europe. The prevalence of childhood obesity is also soaring. Several parameters of the living environment are contributing to this increase, such as the density of fast food retailers, and thus, preventive health policies against childhood obesity must focus on the environment to which children are exposed. Currently, there are no systems in place to objectively measure the effect of living environment parameters on obesogenic behaviors and obesity. The H2020 project "BigO: Big Data Against Childhood Obesity" aims to tackle childhood obesity by creating new sources of evidence based on big data., Objective: This paper introduces the Obesity Prevention dashboard (OPdashboard), implemented in the context of BigO, which offers an interactive data platform for the exploration of objective obesity-related behaviors and local environments based on the data recorded using the BigO mHealth (mobile health) app., Methods: The OPdashboard, which can be accessed on the web, allows for (1) the real-time monitoring of children's obesogenic behaviors in a city area, (2) the extraction of associations between these behaviors and the local environment, and (3) the evaluation of interventions over time. More than 3700 children from 33 schools and 2 clinics in 5 European cities have been monitored using a custom-made mobile app created to extract behavioral patterns by capturing accelerometer and geolocation data. Online databases were assessed in order to obtain a description of the environment. The dashboard's functionality was evaluated during a focus group discussion with public health experts., Results: The preliminary association outcomes in 2 European cities, namely Thessaloniki, Greece, and Stockholm, Sweden, indicated a correlation between children's eating and physical activity behaviors and the availability of food-related places or sports facilities close to schools. In addition, the OPdashboard was used to assess changes to children's physical activity levels as a result of the health policies implemented to decelerate the COVID-19 outbreak. The preliminary outcomes of the analysis revealed that in urban areas the decrease in physical activity was statistically significant, while a slight increase was observed in the suburbs. These findings indicate the importance of the availability of open spaces for behavioral change in children. Discussions with public health experts outlined the dashboard's potential to aid in a better understanding of the interplay between children's obesogenic behaviors and the environment, and improvements were suggested., Conclusions: Our analyses serve as an initial investigation using the OPdashboard. Additional factors must be incorporated in order to optimize its use and obtain a clearer understanding of the results. The unique big data that are available through the OPdashboard can lead to the implementation of models that are able to predict population behavior. The OPdashboard can be considered as a tool that will increase our understanding of the underlying factors in childhood obesity and inform the design of regional interventions both for prevention and treatment., (©Dimitris Filos, Irini Lekka, Vasileios Kilintzis, Leandros Stefanopoulos, Youla Karavidopoulou, Christos Maramis, Christos Diou, Ioannis Sarafis, Vasileios Papapanagiotou, Leonidas Alagialoglou, Ioannis Ioakeimidis, Maria Hassapidou, Evangelia Charmandari, Rachel Heimeier, Grace O'Malley, Shane O’Donnell, Gerardine Doyle, Anastasios Delopoulos, Nicos Maglaveras. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 09.07.2021.)

Background and Objective: Supervised Machine Learning techniques have shown significant potential in medical image analysis. However, the training data that need to be collected for these techniques in the field of MRI 1) may not be available, 2) may be available but the size is small, 3) may be available but not representative and 4) may be available but with weak labels. The aim of this study was to overcome these limitations through advanced MR simulations on a realistic computer model of human anatomy without using a real MRI scanner, without scanning patients and without having personnel and the associated expenses., Methods: The 4D-XCAT model was used with the coreMRI simulation platform for generating artificial short-axis MR-images for training a neural-network to automatic delineate the LV endocardium and epicardium. Its performance was assessed on real MR-images acquired from eight healthy volunteers. The neural-network was also trained on real MR-images from a publicly available dataset and its performance was assessed on the same volunteers' data., Results: The proposed solution demonstrated a performance of 94% (endocardium) and 90% DICE (epicardium) in real mid-ventricular slices, whereas a 10% addition of real MR-images in the artificial training dataset increased the performance to 97% DICE. The use of artificial MR-images that cover the entire LV yielded 85% (endocardium) and 88% DICE (epicardium) when combined with real MR data with an 80%-20% mix respectively., Conclusions: This study suggests a low-cost solution for constructing artificial training datasets for supervised learning techniques in the field of MR by using advanced MR simulations without the use of a real MRI scanner, without scanning patients and without having to use specialized personnel, such as technologists and radiologists., Competing Interests: Competing interests CGX and AHA are co-founders of Corsmed AB., (Copyright © 2020. Published by Elsevier B.V.)

Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants.

Obstructive sleep apnea (OSA) is an increasingly prevalent sleep disorder with estimated prevalence of moderate to severe sleep apnea ranging between 6-13% in the adult population. Nocturnal intermittent hypoxia has been associated with an increased risk of developing type 2 diabetes and insulin resistance (IR). The existing indices of hypoxia, used in polysomnography (PSG), cannot express accurately the influence of the mild desaturations precisely during sleep. In the present study, 73 subjects without other comorbidities were examined by PSG. Hypoxia parameters were analyzed, in the intervals with low values of SpO 2 signal. The thresholds were set at 94% and 92% and the average value (M) of the SpO 2 signal, in areas below thresholds, were calculated. The desaturations were analyzed together with their duration within the recording in terms of SpO 2 signal parameters. Blood samples were taken for biochemical analysis. A total of 50 subjects were diagnosed with OSAS with mean AHI of 56.11±27.70/h and 23 subjects had no sleep apnea with mean AHI of 3.47±0.6/h. The amount of desaturations was significantly correlated with insulin levels (r=0.301, p=0.034) and the percentage of desaturation events (Dev) that were longer than 6 points (2 sec) (r=0.301, p=0.034). In addition, mean duration of desaturations was significantly correlated with IR as expressed by HOMA index (r=0.289, p=0.047), as well as with total duration of desaturation of SpO 2 (r=0.322, p =0.025) and percentage of Dev that were long than 6 points (2 sec) (r=0.292, p=0.044). A strong correlation was also revealed between total duration of desaturations and fasting glucose (r=0.887, p=0.000). Results suggest that hypoxia parameters derived from SpO 2 signal analysis, are strongly correlated with IR and fasting glucose levels, implying a role of hypoxia in the pathogenesis of diabetes.

The incidence and prevalence of cardiovascular diseases (CVD) is increasing which is partly due to an increase in unhealthy lifestyles, including lack of physical activity. Therefore, following a cardiovascular event, patients are encouraged to participate in a supervised exercise-based cardiac rehabilitation (CR) program. However, uptake rates of these programs are low and compliance to adequate volumes of physical activity after the completion of such programs are even lower. An approach that has been proposed towards the increase of patient adherence to exercise, is the incorporation of technology-enabled solutions which are applied at patient's homes. However, different factors may affect patient engagement with such alternative solutions. In this work, we use diverse types of data, including baseline characteristics of the patient (i.e. physiological, behavioral, demographical data) as well as usage data of a tele-rehabilitation solution during a 4-week familiarization period, in order to predict the compliance of patients with CVD to a technology-supported physical activity intervention after completion of a supervised exercise program. Patients were clustered based on their use of a technology intervention during a previously conducted study. Following a feature selection approach, a support vector machine was trained to classify patients as adherent or non-adherent to the intervention. The performance of the classifier was assessed by means of the receiving operator curve (ROC). Bio-psycho-social baseline variables predicted adherence with a ROC of 0.86, but adding usage data of the platform during a 4-week familiarization period increased the ROC up to 0.94. Furthermore, the high sensitivity values (83.8% and 95.5% respectively) support the strength of the models to identify those patients with CVD that will be adherent to a technology-enabled, home-based CR program.

The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.

Objective: Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of such databases., Approach: This paper describes a public respiratory sound database, which was compiled for an international competition, the first scientific challenge of the IFMBE's International Conference on Biomedical and Health Informatics. The database includes 920 recordings acquired from 126 participants and two sets of annotations. One set contains 6898 annotated respiratory cycles, some including crackles, wheezes, or a combination of both, and some with no adventitious respiratory sounds. In the other set, precise locations of 10 775 events of crackles and wheezes were annotated., Main Results: The best system that participated in the challenge achieved an average score of 52.5% with the respiratory cycle annotations and an average score of 91.2% with the event annotations., Significance: The creation and public release of this database will be useful to the research community and could bring attention to the respiratory sound classification problem.

Cardiovascular diseases (CVDs) are a leading cause of premature death worldwide. International guidelines recommend routine delivery of all phases of cardiac rehabilitation (CR). Uptake of traditional CR remains suboptimal, as attendance at formal hospital-based CR programs is low, with community-based CR rates and individual long-term exercise maintenance even lower. Home-based CR programs have been shown to be equally effective in clinical and health-related quality of life outcomes and yet are not readily available. The aim of the current study was to develop the PATHway intervention (physical activity toward health) for the self-management of CVD. Increasing physical activity in individuals with CVD was the primary behavior. The PATHway intervention was theoretically informed by the behavior change wheel and social cognitive theory. All relevant intervention functions, behavior change techniques, and policy categories were identified and translated into intervention content. Furthermore, a person-centered approach was adopted involving an iterative codesign process and extensive user testing. Education, enablement, modeling, persuasion, training, and social restructuring were selected as appropriate intervention functions. Twenty-two behavior change techniques, linked to the six intervention functions and three policy categories, were identified for inclusion and translated into PATHway intervention content. This paper details the use of the behavior change wheel and social cognitive theory to develop an eHealth intervention for the self-management of CVD. The systematic and transparent development of the PATHway intervention will facilitate the evaluation of intervention effectiveness and future replication.

Background: Exercise-based rehabilitation plays a key role in improving the health and quality of life of patients with Cardiovascular Disease (CVD). Home-based computer-assisted rehabilitation programs have the potential to facilitate and support physical activity interventions and improve health outcomes., Objectives: We present the development and evaluation of a computerized Decision Support System (DSS) for unsupervised exercise rehabilitation at home, aiming to show the feasibility and potential of such systems toward maximizing the benefits of rehabilitation programs., Methods: The development of the DSS was based on rules encapsulating the logic according to which an exercise program can be executed beneficially according to international guidelines and expert knowledge. The DSS considered data from a prescribed exercise program, heart rate from a wristband device, and motion accuracy from a depth camera, and subsequently generated personalized, performance-driven adaptations to the exercise program. Communication interfaces in the form of RESTful web service operations were developed enabling interoperation with other computer systems., Results: The DSS was deployed in a computer-assisted platform for exercise-based cardiac rehabilitation at home, and it was evaluated in simulation and real-world studies with CVD patients. The simulation study based on data provided from 10 CVD patients performing 45 exercise sessions in total, showed that patients can be trained within or above their beneficial HR zones for 67.1 ± 22.1% of the exercise duration in the main phase, when they are guided with the DSS. The real-world study with 3 CVD patients performing 43 exercise sessions through the computer-assisted platform, showed that patients can be trained within or above their beneficial heart rate zones for 87.9 ± 8.0% of the exercise duration in the main phase, with DSS guidance., Conclusions: Computerized decision support systems can guide patients to the beneficial execution of their exercise-based rehabilitation program, and they are feasible., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: The benefits of regular physical activity for health and quality of life are unarguable. New information, sensing and communication technologies have the potential to play a critical role in computerised decision support and coaching for physical activity., Objectives: We provide a literature review of recent research in the development of physical activity interventions employing computerised decision support, their feasibility and effectiveness in healthy and diseased individuals, and map out challenges and future research directions., Methods: We searched the bibliographic databases of PubMed and Scopus to identify physical activity interventions with computerised decision support utilised in a real-life context. Studies were synthesized according to the target user group, the technological format (e.g., web-based or mobile-based) and decision-support features of the intervention, the theoretical model for decision support in health behaviour change, the study design, the primary outcome, the number of participants and their engagement with the intervention, as well as the total follow-up duration., Results: From the 24 studies included in the review, the highest percentage (n = 7, 29%) targeted sedentary healthy individuals followed by patients with prediabetes/diabetes (n = 4, 17%) or overweight individuals (n = 4, 17%). Most randomized controlled trials reported significantly positive effects of the interventions, i.e., increase in physical activity (n = 7, 100%) for 7 studies assessing physical activity measures, weight loss (n = 3, 75%) for 4 studies assessing diet, and reductions in glycosylated hemoglobin (n = 2, 66%) for 3 studies assessing glycose concentration. Accelerometers/pedometers were used in almost half of the studies (n = 11, 46%). Most adopted decision support features included personalised goal-setting (n = 16, 67%) and motivational feedback sent to the users (n = 15, 63%). Fewer adopted features were integration with electronic health records (n = 3, 13%) and alerts sent to caregivers (n = 4, 17%). Theoretical models of decision support in health behaviour to drive the development of the intervention were not reported in most studies (n = 14, 58%)., Conclusions: Interventions employing computerised decision support have the potential to promote physical activity and result in health benefits for both diseased and healthy individuals, and help healthcare providers to monitor patients more closely. Objectively measured activity through sensing devices, integration with clinical systems used by healthcare providers and theoretical frameworks for health behaviour change need to be employed in a larger scale in future studies in order to realise the development of evidence-based computerised systems for physical activity monitoring and coaching., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Current healthcare systems are struggling with rising costs and unbalanced quality of care. Integrated care (IC) is a worldwide trend in healthcare reforms designed to tackle these problems. ACT@Scale is a partnership of leading European regions, industry and academia which aims to identify, transfer and scale-up existing integrated healthcare practices. In this context, participating programs are applying iterative process improvement cycles using collaborative methodologies. The vision of learning health systems (LHS) is similar to IC, but it focuses on IT means as a change enabler for rapid and continuous knowledge integration into better outcomes. In this paper, we present the ACT@Scale program as an example of an LHS that monitors integrated care performance and effects of process improvement cycles.