Alcuni abstract di articoli che trattano argomenti relativi all'eHealth

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A systematic and evidence-based approach to assessment of NHS organisational readiness to commence haemodynamic remote monitoring through cardiovascular implantable electronic devices (CIEDs) in routine heart failure care. (SEARCH-HF)

Background & Aims It is hypothesised that successful remote monitoring (RM) of patients with heart failure (HF) using cardiovascular implantable electronic devices (CIEDs) is related to the context within which the technology is used and integrated into decision-making. There is currently little guidance on how a UK clinic should perform high quality RM. The aim of this project was to provide an evidence-based approach to RM of HF patients by 1) identifying necessary pre-requisite competence to facilitate successful introduction of RM; 2) mapping a care pathway in an NHS setting for CIED-based RM of patients with HF; and 3) developing an assessment matrix of key requirements for optimal CIED-based RM. Methods A mixed-methods study was undertaken incorporating both quantitative and qualitative data from the process of CIED-based RM. Quantitative data were gained from a systematic review of literature on facilitators of, and barriers to, high quality RM. A process mapping workshop was undertaken at the Royal Brompton Hospital to identify the intricacies of the clinical pathway at an experienced RM centre. These data were supplemented by semi-structured interviews with patients and healthcare professionals to develop important themes on RM of HF patients to give a coherent interpretation of the RM process studied. Results After identifying 64 relevant publications and undertaking a process-mapping exercise on initiation of RM and responding to an alert, interviews with 12 patients and seven healthcare professionals were undertaken. The key themes emerging from these data were incorporated into a model RM pathway and pathway-anchored assessment framework. Conclusions This is the first study to investigate barriers to, and facilitators of, high quality CIED-based RM in a UK HF clinic. The tools generated from this study will allow other NHS centres to assess the key organisational, educational and data processing requirements to ensure high quality RM.Open Acces

Computational intelligence contributions to readmisision risk prediction in Healthcare systems

136 p.The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures

Design and Analysis of A Dual Chamber Cardiac Pacemaker Using VHDL in Biomedical Application

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Computational intelligence contributions to readmisision risk prediction in Healthcare systems

136 p.The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures

eHealth in hypertension and cardiovascular disease:Opportunities and challenges

In this thesis we investigate different aspects of eHealth for hypertension and cardiovascular disease, with a focus on remote monitoring programs for chronic care. We use the Dutch HartWacht program for patients with hypertension, cardiac arrhythmias and heart failure as an example that has been implemented in routine clinical care. We first focus on hypertension and identify areas that are attractive for future implementation of eHealth because of poor hypertension control. In the following chapters we present economical, legal and technical challenges that accompany eHealth implementation, in each chapter followed by potential solutions and opportunities. We identify success factors for cost-effective eHealth, provide a roadmap for GDPR-compliant solutions, present a novel technique for heartbeat detection through a bracelet and describe a protocol for efficient data handling in remote monitoring programs. In the second part of this thesis, we zoom in on the patients participating in eHealth programs. We evaluate the impact on quality of life of patients participating in the HartWacht program for cardiac arrhythmias and demonstrate equivalence compared to usual care. We then describe the feasibility of the HartWacht program for patients with hypertension in reducing blood pressure and present rationale, design and cohort profile of the Effectiveness of home-Monitoring of blood pressure in PAtients with difficult to Treat HYpertension (EMPATHY) trial. We conclude with an evaluation of the impact of the COVID-19 pandemic on the uptake of eHealth in primary care in the Netherlands

Prediction Of Heart Failure Decompensations Using Artificial Intelligence - Machine Learning Techniques

Los apartados 4.41, 4.4.2 y 4.4.3 del capítulo 4 están sujetos a confidencialidad por la autora. 203 p.Heart failure (HF) is a major concern in public health. Its total impact is increased by its high incidence and prevalence and its unfavourable medium-term prognosis. In addition, HF leads to huge health care resource consumption. Moreover, efforts to develop a deterministic understanding of rehospitalization have been difficult, as no specific patient or hospital factors have been shown to consistently predict 30-day readmission after hospitalization for HF.Taking all these facts into account, we wanted to develop a project to improve the assistance care of patients with HF. Up to know, we were using telemonitoring with a codification system that generated alarms depending on the received values. However, these simple rules generated large number of false alerts being, hence, not trustworthy. The final aims of this work are: (i) asses the benefits of remote patient telemonitoring (RPT), (ii) improve the results obtained with RPT using ML techniques, detecting which parameters measured by telemonitoring best predict HF decompensations and creating predictive models that will reduce false alerts and detect early decompensations that otherwise will lead to hospital admissions and (iii) determine the influence of environmental factors on HF decompensations.All in all, the conclusions of this study are:1. Asses the benefits of RPT: Telemonitoring has not shown a statistically significant reduction in the number of HF-related hospital admissions. Nevertheless, we have observed a statistically significant reduction in mortality in the intervention group with a considerable percentage of deaths from non-cardiovascular causes. Moreover, patients have considered the RPT programme as a tool that can help them in the control of their chronic disease and in the relationship with health professionals.2. Improve the results obtained with RPT using machine learning techniques: Significant weight increases, desaturation below 90%, perception of clinical worsening, including development of oedema, worsening of functional class and orthopnoea are good predictors of heart failure decompensation. In addition, machine learning techniques have improved the current alerts system implemented in our hospital. The system reduces the number of false alerts notably although it entails a decrement on sensitivity values. The best results are achieved with the predictive model built by applying NB with Bernoulli to the combination of telemonitoring alerts and questionnaire alerts (Weight + Ankle + well-being plus the yellow alerts of systolic blood pressure, diastolic blood pressure, O2Sat and heart rate). 3. Determine the influence of environmental factors on HF decompensations: Air temperature is the most significant environmental factor (negative correlation) in our study, although some other attributes, such as precipitation, are also relevant. This work also shows a consistent association between increasing levels SO2 and NOX air and HF hospitalizations