A longitudinal assessment of chronic care pathways in real-life: self-care and outcomes of chronic heart failure patients in Tuscany

Background: Worldwide healthcare systems face challenges in assessing and monitoring chronic care pathways and, even more, the value generated for patients. Patient-reported outcomes measures (PROMs) represent a valid Real-World Evidence (RWE) source to fully assess health systems’ performance in managing chronic care pathways. Methods: The originality of the study consists in the chance of adopting PROMs, as a longitudinal assessment tool for continuous monitoring of patients’ adherence to therapies and self-care behavior recommendations in clinical practice and as a chance to provide policy makers insights to improve chronic pathways adopting a patient perspective. The focus was on PROMs of patients with chronic heart failure (CHF) collected in the Gabriele Monasterio Tuscan Foundation (FTGM), a tertiary referral CHF centre in Pisa, Italy. During the hospital stay, CHF patients were enrolled and received a link (via SMS or email) to access to the first questionnaire. Follow-up questionnaires were sent 1, 7 and 12 months after the index hospitalisation. Professionals invited 200 patients to participate to PROMs surveys. 174 answers were digitally collected at baseline from 2018 to 2020 and analysed. Quantitative and qualitative analyses were conducted, using Chi2, t-tests and regression models together with narrative evidence from free text responses. Results: Both quantitative and qualitative results showed FTGM patients declared to strongly adhere to the pharmacological therapy across the entire pathway, while seemed less careful to adhere to self-care behavior recommendations (e.g., physical activity). CHF patients that performed adequate Self-Care Maintenance registered outcome improvements. Respondents declared to be supported by family members in managing their adherence. Conclusions: The features of such PROMs collection model are relevant for researchers, policymakers and for managers to implement interventions aimed at improving pathway adherence dimensions. Among those, behavioral economics interventions could be implemented to increase physical activity among CHF patients since proven successful in Tuscany. Strategies to increase territorial care and support patients’ caregivers in their daily support to patients’ adherence should be further explored. Systematic PROMs collection would allow to monitor changes in the whole pathway organization. This study brings opportunities for extending such monitoring systems to other organizations to allow for reliable benchmarking opportunities

A numerical framework for simulating fluid-structure interaction phenomena

In this paper, a numerical tool able to solve fluid-structure interaction problems is proposed. The lattice Boltzmann method is used to compute fluid dynamics, while the corotational finite element formulation together with the Time Discontinuous Galerkin method are adopted to predict structure dynamics. The Immersed Boundary method is used to account for the presence of an immersed solid in the lattice fluidbackground and to handle fluid-structure interface conditions, while a Volume-of-Fluid-based method isadopted to take trace of the evolution of the free surface. These ingredients are combined through a partitioned staggered explicit strategy, according to an efficient and accurate algorithm recently developed by the authors. The effectiveness of the proposed methodology is tested against two different cases. The former investigates the dam break phenomenon, involving the modeling of the free surface. The latter involves the vibration regime experienced by two highly deformable flapping flags obstructing a flow. A wide numerical campaign is carried out by computing the error in terms of interface energy artificially introduced at the fluid-solid interface. Moreover, the structure behavior is dissected by simulating scenarios characterized by different values of the Reynolds number. Present findings are compared to literature results, showing a very close agreement.&nbsp

A numerical framework for simulating fluid-structure interaction phenomena

In this paper, a numerical tool able to solve fluid-structure interaction problems is proposed. The lattice Boltzmann method is used to compute fluid dynamics, while the corotational finite element formulation together with the Time Discontinuous Galerkin method are adopted to predict structure dynamics. The Immersed Boundary method is used to account for the presence of an immersed solid in the lattice fluid background and to handle fluid-structure interface conditions, while a Volume-of-Fluid-based method is adopted to take trace of the evolution of the free surface. These ingredients are combined through a partitioned staggered explicit strategy, according to an efficient and accurate algorithm recently developed by the authors. The effectiveness of the proposed methodology is tested against two different cases. The former investigates the dam break phenomenon, involving the modeling of the free surface. The latter involves the vibration regime experienced by two highly deformable flapping flags obstructing a flow. A wide numerical campaign is carried out by computing the error in terms of interface energy artificially introduced at the fluid-solid interface. Moreover, the structure behavior is dissected by simulating scenarios characterized by different values of the Reynolds number. Present findings are compared to literature results, showing a very close agreement

Up and down the number line: modelling collaboration in contrasting school and home environments

This paper is concerned with user modelling issues such as adaptive educational environments, adaptive information retrieval, and support for collaboration. The HomeWork project is examining the use of learner modelling strategies within both school and home environments for young children aged 5 – 7 years. The learning experience within the home context can vary considerably from school especially for very young learners, and this project focuses on the use of modelling which can take into account the informality and potentially contrasting learning styles experienced within the home and school

Quality of life assessment in amyloid transthyretin (ATTR) amyloidosis

Background: Amyloid transthyretin (ATTR) amyloidosis is caused by the systemic deposition of transthyretin molecules, either normal (wild-type ATTR, ATTRwt) or mutated (variant ATTR, ATTRv). ATTR amyloidosis is a disease with a severe impact on patients’ quality of life (QoL). Nonetheless, limited attention has been paid to QoL so far, and no specific tools for QoL assessment in ATTR amyloidosis currently exist. QoL can be evaluated through patient-reported outcome measures (PROMs), which are completed by patients, or through scales, which are compiled by clinicians. The scales investigate QoL either directly or indirectly, i.e., by assessing the degree of functional impairment and limitations imposed by the disease. Design: Search for the measures of QoL evaluated in phase 2 and phase 3 clinical trials on ATTR amyloidosis. Results: Clinical trials on ATTR amyloidosis have used measures of general health status, such as the Short Form 36 Health Survey (SF-36), or tools developed in other disease settings such as the Kansas City Cardiomyopathy Questionnaire (KCCQ) or adaptations of other scales such as the modified Neuropathy Impairment Score +7 (mNIS+7). Conclusions: Scales or PROMs for ATTR amyloidosis would be useful to better characterize newly diagnosed patients and to assess disease progression and response to treatment. The ongoing ITALY (Impact of Transthyretin Amyloidosis on Life qualitY) study aims to develop and validate 2 PROMs encompassing the whole phenotypic spectrum of ATTRwt and ATTRv amyloidosis, that might be helpful for patient management and may serve as surrogate endpoints for clinical trials

Tune in to your emotions: a robust personalized affective music player

The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listeners’ personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application

Making effective use of healthcare data using data-to-text technology

Healthcare organizations are in a continuous effort to improve health outcomes, reduce costs and enhance patient experience of care. Data is essential to measure and help achieving these improvements in healthcare delivery. Consequently, a data influx from various clinical, financial and operational sources is now overtaking healthcare organizations and their patients. The effective use of this data, however, is a major challenge. Clearly, text is an important medium to make data accessible. Financial reports are produced to assess healthcare organizations on some key performance indicators to steer their healthcare delivery. Similarly, at a clinical level, data on patient status is conveyed by means of textual descriptions to facilitate patient review, shift handover and care transitions. Likewise, patients are informed about data on their health status and treatments via text, in the form of reports or via ehealth platforms by their doctors. Unfortunately, such text is the outcome of a highly labour-intensive process if it is done by healthcare professionals. It is also prone to incompleteness, subjectivity and hard to scale up to different domains, wider audiences and varying communication purposes. Data-to-text is a recent breakthrough technology in artificial intelligence which automatically generates natural language in the form of text or speech from data. This chapter provides a survey of data-to-text technology, with a focus on how it can be deployed in a healthcare setting. It will (1) give an up-to-date synthesis of data-to-text approaches, (2) give a categorized overview of use cases in healthcare, (3) seek to make a strong case for evaluating and implementing data-to-text in a healthcare setting, and (4) highlight recent research challenges.Comment: 27 pages, 2 figures, book chapte