Holistic System Design for Distributed National eHealth Services

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Exacerbations in Chronic Obstructive Pulmonary Disease:Identification and Prediction Using a Digital Health System

Background: Chronic obstructive pulmonary disease (COPD) is a progressive, chronic respiratory disease with a significant socioeconomic burden. Exacerbations, the sudden and sustained worsening of symptoms, can lead to hospitalization and reduce quality of life. Major limitations of previous telemonitoring interventions for COPD include low compliance, lack of consensus on what constitutes an exacerbation, limited numbers of patients, and short monitoring periods. We developed a telemonitoring system based on a digital health platform that was used to collect data from the 1-year EDGE (Self Management and Support Programme) COPD clinical trial aiming at daily monitoring in a heterogeneous group of patients with moderate to severe COPD. Objective: The objectives of the study were as follows: first, to develop a systematic and reproducible approach to exacerbation identification and to track the progression of patient condition during remote monitoring; and second, to develop a robust algorithm able to predict COPD exacerbation, based on vital signs acquired from a pulse oximeter. Methods: We used data from 110 patients, with a combined monitoring period of more than 35,000 days. We propose a finite-state machine–based approach for modeling COPD exacerbation to gain a deeper insight into COPD patient condition during home monitoring to take account of the time course of symptoms. A robust algorithm based on short-period trend analysis and logistic regression using vital signs derived from a pulse oximeter is also developed to predict exacerbations. Results: On the basis of 27,260 sessions recorded during the clinical trial (average usage of 5.3 times per week for 12 months), there were 361 exacerbation events. There was considerable variation in the length of exacerbation events, with a mean length of 8.8 days. The mean value of oxygen saturation was lower, and both the pulse rate and respiratory rate were higher before an impending exacerbation episode, compared with stable periods. On the basis of the classifier developed in this work, prediction of COPD exacerbation episodes with 60%-80% sensitivity will result in 68%-36% specificity. Conclusions: All 3 vital signs acquired from a pulse oximeter (pulse rate, oxygen saturation, and respiratory rate) are predictive of COPD exacerbation events, with oxygen saturation being the most predictive, followed by respiratory rate and pulse rate. Combination of these vital signs with a robust algorithm based on machine learning leads to further improvement in positive predictive accuracy.</p

Home monitoring of physiology and symptoms to detect Interstitial Lung Disease exacerbations and progression: a systematic review

BACKGROUND: Acute exacerbations and disease progression in interstitial lung disease (AE-ILD) pose important challenges to clinicians and patients. AE-ILD are variable in presentation but may result in rapid progression of ILD, respiratory failure and death. However, in many cases AE-ILD may go unrecognised so that their true impact and response to therapy is unknown. The potential for home monitoring to facilitate early, and accurate, identification of AE and/or ILD progression has gained interest. With increasing evidence available, there is a need for a systematic review on home monitoring of patients with ILD to summarise the existing data. AIMS: To systematically evaluate the evidence for use of home monitoring for early detection of exacerbations and/or progression of ILD. METHOD: We searched Ovid-EMBASE, MEDLINE, and CINAHL using MeSH terms in accordance with the PRISMA guidelines. PROSPERO registration number (CRD42020215166). RESULTS: Thirteen studies comprising 968 patients have demonstrated that home monitoring is feasible and of potential benefit in patients with ILD. Nine studies reported that mean adherence to home monitoring was greater than 75%, and where spirometry was performed there was a significant correlation (r=0.72–0.98, p<0.001) between home and hospital-based readings. Two studies suggested that home monitoring of Forced Vital Capacity (FVC) might facilitate detection of progression in idiopathic pulmonary fibrosis (IPF). CONCLUSION: Despite the fact that individual studies in this systematic review provide supportive evidence suggesting the feasibility and utility of home monitoring in ILD, further studies are necessary to quantify the potential of home monitoring to detect disease progression and/or acute exacerbations

Feasibility and Acute Care Utilization Outcomes of a Post-Acute Transitional Telemonitoring Program for Underserved Chronic Disease Patients

Background: Chronic obstructive pulmonary disease (COPD) and heart failure (HF) are chronic diseases that impart significant health and care costs to the patient and health system. Limited access to health services affects disease severity and functional status. Telemonitoring has shown promise in reducing acute care utilization for chronic disease patients, but the benefit for the underserved has not been determined. We evaluated acute care utilization outcomes following an acute event of a 90-day transitional care program integrating telemonitoring technology and home visits for underserved COPD and HF patients. Materials and Methods: Patients were enrolled into the program between October 2010 and August 2012. Primary outcomes included rates of emergency department (ED) visits and all-cause re-admission at 30, 90, and 180 days postdischarge. Program and functional status at enrollment and discharge and satisfaction with telemonitoring at discharge were measured. Telemonitoring included daily symptomatology recording and was removed at 90 days. A control cohort was identified through electronic health records and propensity-matched via 15 variables to achieve a sample size with balanced baseline characteristics. Results: Program patients showed 50% reduction in 30-day re-admission and 13–19% reduction in 180-day re-admission compared with control patients. There was no significant difference in ED utilization. Patients were satisfied with telemonitoring services, and functional status improved by program end. Conclusions: This feasibility study suggests telemonitoring in the context of a transitional care model following an acute event may reduce all-cause 30-day re-admissions by up to 50% and has the potential to reduce longterm acute care utilization and thus care costs. More rigorous and long-term investigation is warranted

Reducing Home Health COPD-Related 30-Day Hospital Readmissions Using Telehealth Technology

Chronic obstructive pulmonary disease (COPD) is a collection of chronic conditions that results in irreparable lung damage and stress to patients. COPD also has considerable financial impacts on health care entities due to frequent hospital readmissions of COPD patients. The Centers for Medicare and Medicaid Services penalize care entities for 30-day hospital readmissions. Many rehospitalizations attributed to COPD are due to exacerbations, often preceded by physiologic and emotional changes that can be monitored, allowing action to be taken to prevent readmissions. The practice problem for this quality improvement project explored whether the use of remote home monitoring of COPD patients discharged to home health care, coupled with the use of a medication rescue pack, would reduce rehospitalizations within 30 days after discharge. The purpose of the project was to evaluate the effectiveness of telehealth remote monitoring and initiation of a medication rescue pack in decreasing 30-day readmissions of COPD patients. The self-efficacy model was used to encourage health-promoting actions that are necessary for chronic disease management. Data from the project agency\u27s records of COPD patients were evaluated for readmission rates. Analysis of the data from 8 preintervention patients showed that 3 (38%) were readmitted. Postintervention data showed that of the 9 participants, only 1 was readmitted (11%). Comparison of the data showed a 27% decrease in readmissions because of the intervention. The results of this project have the potential to bring about positive social change by improving care management remotely in real time, thus decreasing rehospitalization in COPD patients

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

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

Home monitoring of physiology and symptoms to detect interstitial lung disease exacerbations and progression:a systematic review

Background: Acute exacerbations (AEs) and disease progression in interstitial lung disease (ILD) pose important challenges to clinicians and patients. AEs of ILD are variable in presentation but may result in rapid progression of ILD, respiratory failure and death. However, in many cases AEs of ILD may go unrecognised so that their true impact and response to therapy is unknown. The potential for home monitoring to facilitate early, and accurate, identification of AE and/or ILD progression has gained interest. With increasing evidence available, there is a need for a systematic review on home monitoring of patients with ILD to summarise the existing data. The aim of this review was to systematically evaluate the evidence for use of home monitoring for early detection of exacerbations and/or progression of ILD. Method: We searched Ovid-EMBASE, MEDLINE and CINAHL using Medical Subject Headings (MeSH) terms in accordance with the PRISMA guidelines (PROSPERO registration number CRD42020215166). Results: 13 studies involving 968 patients have demonstrated that home monitoring is feasible and of potential benefit in patients with ILD. Nine studies reported that mean adherence to home monitoring was &gt;75%, and where spirometry was performed there was a significant correlation (r=0.72–0.98, p&lt;0.001) between home and hospital-based readings. Two studies suggested that home monitoring of forced vital capacity might facilitate detection of progression in idiopathic pulmonary fibrosis. Conclusion: Despite the fact that individual studies in this systematic review provide supportive evidence suggesting the feasibility and utility of home monitoring in ILD, further studies are necessary to quantify the potential of home monitoring to detect disease progression and/or AEs.</p

Role of a digital clinical decision-support system in management of chronic obstructive pulmonary disease

Postponed access: the file will be accessible after 2022-05-18M.Phil. in Global Health - ThesisINTH395AMAMD-GLO

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALTH

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALT