Quantifying beliefs regarding telehealth: Development of the Whole Systems Demonstrator Service User Technology Acceptability Questionnaire

Introduction: Telehealth (TH) is a potential solution to the increased incidence of chronic illness in an ageing population.The extent to which older people and users with chronic conditions accept and adhere to using assistive technologies is a potential barrier to mainstreaming the service. This study reports the development and validation of the Whole Systems Demonstrator (WSD) Service User Technology Acceptability Questionnaire (SUTAQ). Methods: Questionnaires measuring the acceptability of TH, quality of life, well-being and psychological processes were completed by 478 users of TH. The 22 acceptability items were subject to principal components analysis (PCA) to determine sub-scales. Scale scores, relationships between scales and other patient reported outcome measures (PROMs), and group differences on scales were utilised to check the reliability and validity of the measure. Results: PCAs of SUTAQ items produced 6 TH acceptability scales: enhanced care, increased accessibility, privacy & discomfort, care personnel concerns, kit as substitution, and satisfaction. Scale scores indicated, individuals with long term conditions held beliefs. Significant correlations within these beliefs and between these scales and additional PROMs were coherent and the SUTAQ subscales were able to predict those more likely to refuse TH. Discussion: The SUTAQ is an instrument that can be used to measure user beliefs about the acceptability of TH, and has the ability to discriminate and predict individual differences in beliefs and behaviour. Measuring acceptability beliefs of TH users can provide valuable information to direct and target provision of services to increase uptake and maintain use of TH

Effekte von Telemedizin auf depressive Symptome und Lebensqualität bei Patienten mit chronischer Herzinsuffizienz – eine prästratifizierte Subgruppenanalyse der Telemedical Interventional Monitoring in Heart Failure (TIM-HF) study

Aims: Depression is a frequent comorbidity in patients with chronic heart failure (CHF). Telemonitoring has emerged as a novel option in CHF care. However, patients with depression have been excluded in most telemedicine studies. This pre-specified subgroup analysis of the Telemedical Interventional Monitoring in Heart Failure (TIM-HF) trial investigates the effect of telemonitoring on depressive symptoms over a period of 12months. Methods and results: The TIM-HF study randomly assigned 710 patients with CHF to either usual care (UC) or a telemedical intervention (TM) using non-invasive devices for daily monitoring electrocardiogram, blood pressure and body weight. Depression was evaluated by the 9-item Patient Health Questionnaire (PHQ-9) with scores ≥10 defining clinically relevant depressive symptoms. Mixed model repeated measures were performed to calculate changes in PHQ-9 score. Quality of life was measured by the Short Form-36. At baseline, 156 patients had a PHQ-9 score ≥10 points (TM: 79, UC: 77) with a mean of 13.2 points indicating moderate depressiveness. Patients randomized to telemedicine showed an improvement of their PHQ-9 scores, whereas UC patients remained constant (P = 0.004). Quality of life parameters were improved in the TM group compared to UC. Adjustment was performed for follow-up, New York Heart Association class, medication, age, current living status, number of hospitalizations within the last 12months and serum creatinine. In the study population without depression, the PHQ-9 score was similar at baseline and follow-up. Conclusion Telemedical care improved depressive symptoms and had a positive influence on quality of life in patients with CHF and moderate depression

Decision Support for Reducing 30-Day Readmissions: General Medicine Patients in Community Hospitals

Health expenditures in United States have experienced a gradual increase in spending with no indication of slowing down. Addressing this problem has been a major area of concern for policy makers, and as a result more consideration has been placed on decreasing health spending and increasing affordability. One major area recognized as being effective in decreasing these financial burdens has been inpatient thirty-day adult readmissions, currently costing $26 billion annually. Centers for Medicare & Medicaid Services (CMS) have determined readmissions to be an indicator of the quality and efficiency of patient care. This research provides a prediction model for patients at `high-risk\u27 of 30-day readmissions patients in rural and urban hospital settings. These results are integrated into a decision support tool that combines the mathematical design, published discharge interventions, and financial model for use by hospital administrators. This tool was created to give `control\u27 back to hospital managers and improve the decision making process in reducing hospital readmission rates. Through this work we show the mathematical model, intervention process work flow, and decision support tool

A Remote Patient Monitoring System for Congestive Heart Failure

Congestive heart failure (CHF) is a leading cause of death in the United States affecting approximately 670,000 individuals. Due to the prevalence of CHF related issues, it is prudent to seek out methodologies that would facilitate the prevention, monitoring, and treatment of heart disease on a daily basis. This paper describes WANDA (Weight and Activity with Blood Pressure Monitoring System); a study that leverages sensor technologies and wireless communications to monitor the health related measurements of patients with CHF. The WANDA system is a three-tier architecture consisting of sensors, web servers, and back-end databases. The system was developed in conjunction with the UCLA School of Nursing and the UCLA Wireless Health Institute to enable early detection of key clinical symptoms indicative of CHF-related decompensation. This study shows that CHF patients monitored by WANDA are less likely to have readings fall outside a healthy range. In addition, WANDA provides a useful feedback system for regulating readings of CHF patients

Northern Territory Heart Failure Initiative–Clinical Audit (NTHFI–CA)–a prospective database on the quality of care and outcomes for acute decompensated heart failure admission in the Northern Territory: study design and rationale

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/Introduction Congestive heart failure is a significant cause of morbidity and mortality in Australia. Accurate data for the Northern Territory and Indigenous Australians are not presently available. The economic burden of this chronic cardiovascular disease is felt by all funding bodies and it still remains unclear what impact current measures have on preventing the ongoing disease burden and how much of this filters down to more remote areas. Clear differentials also exist in rural areas including a larger Indigenous community, greater disease burden, differing aetiologies for heart failure as well as service and infrastructure discrepancies. It is becoming increasingly clear that urban solutions will not affect regional outcomes. To understand regional issues relevant to heart failure management, an understanding of the key performance indicators in that setting is critical. Methods and analysis The Northern Territory Heart Failure Initiative—Clinical Audit (NTHFI-CA) is a prospective registry of acute heart failure admissions over a 12-month period across the two main Northern Territory tertiary hospitals. The study collects information across six domains and five dimensions of healthcare. The study aims to set in place an evidenced and reproducible audit system for heart failure and inform the developing heart failure disease management programme. The findings, is believed, will assist the development of solutions to narrow the outcomes divide between remote and urban Australia and between Indigenous and Non-Indigenous Australians, in case they exist. A combination of descriptive statistics and mixed effects modelling will be used to analyse the data. Ethics and dissemination This study has been approved by respective ethics committees of both the admitting institutions. All participants will be provided a written informed consent which will be completed prior to enrolment in the study. The study results will be disseminated through local and international health conferences and peer reviewed manuscripts

Quantifying beliefs regarding telehealth: Development of the Whole Systems Demonstrator Service User Technology Acceptability Questionnaire

Introduction: Telehealth (TH) is a potential solution to the increased incidence of chronic illness in an ageing population. The extent to which older people and users with chronic conditions accept and adhere to using assistive technologies is a potential barrier to mainstreaming the service. This study reports the development and validation of the Whole Systems Demonstrator (WSD) Service User Technology Acceptability Questionnaire (SUTAQ). / Methods: Questionnaires measuring the acceptability of TH, quality of life, well-being and psychological processes were completed by 478 users of TH. The 22 acceptability items were subject to principal components analysis (PCA) to determine sub-scales. Scale scores, relationships between scales and other patient-reported outcome measures (PROMs), and group differences on scales were utilised to check the reliability and validity of the measure. / Results: PCAs of SUTAQ items produced six TH acceptability scales: enhanced care, increased accessibility, privacy and discomfort, care personnel concerns, kit as substitution and satisfaction. Significant correlations within these beliefs and between these scales and additional PROMs were coherent, and the SUTAQ sub-scales were able to predict those more likely to refuse TH. / Discussion: The SUTAQ is an instrument that can be used to measure user beliefs about the acceptability of TH, and has the ability to discriminate between groups and predict individual differences in beliefs and behaviour. Measuring acceptability beliefs of TH users can provide valuable information to direct and target provision of services to increase uptake and maintain use of TH

The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database.

Background: Many intensive care units (ICUs) utilize telemedicine in response to an expanding critical care patient population, off-hours coverage, and intensivist shortages, particularly in rural facilities. Advances in digital health technologies, among other reasons, have led to the integration of active, well-networked critical care telemedicine (tele-ICU) systems across the United States, which in turn, provide the ability to generate large-scale remote monitoring data from critically ill patients. Objective: The objective of this study was to explore opportunities and challenges of utilizing multisite, multimodal data acquired through critical care telemedicine. Using a publicly available tele-ICU, or electronic ICU (eICU), database, we illustrated the quality and potential uses of remote monitoring data, including cohort discovery for secondary research. Methods: Exploratory analyses were performed on the eICU Collaborative Research Database that includes deidentified clinical data collected from adult patients admitted to ICUs between 2014 and 2015. Patient and ICU characteristics, top admission diagnoses, and predictions from clinical scoring systems were extracted and analyzed. Additionally, a case study on respiratory failure patients was conducted to demonstrate research prospects using tele-ICU data. Results: The eICU database spans more than 200 hospitals and over 139,000 ICU patients across the United States with wide-ranging clinical data and diagnoses. Although mixed medical-surgical ICU was the most common critical care setting, patients with cardiovascular conditions accounted for more than 20% of ICU stays, and those with neurological or respiratory illness accounted for nearly 15% of ICU unit stays. The case study on respiratory failure patients showed that cohort discovery using the eICU database can be highly specific, albeit potentially limiting in terms of data provenance and sparsity for certain types of clinical questions. Conclusions: Large-scale remote monitoring data sources, such as the eICU database, have a strong potential to advance the role of critical care telemedicine by serving as a testbed for secondary research as well as for developing and testing tools, including predictive and prescriptive analytical solutions and decision support systems. The resulting tools will also inform coordination of care for critically ill patients, intensivist coverage, and the overall process of critical care telemedicine.Office of Research, Discovery, Innovation at the University of Arizona; National Science Foundation [1838745]; National Heart, Lung, and Blood Institute of the National Institutes of Health [5T32HL007955]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Big Data Applications & Risk Stratification in Cardiovascular Disease

This thesis aimed to explore potential tools for improving cardiovascular (CVD) management, focusing on heart failure (HF) and acute coronary syndrome (ACS). The first part examined big data applications, ranging from subgroup identification to monitoring. Using retrospective health insurance claims data, risk factors for adverse outcomes in chronic HF were investigated, revealing sex-specific differences in comorbidities but not in medication adherence, further denoting the value of such databases. Innovative machine learning techniques were then deployed, demonstrating their superiority in predictive value for adverse outcomes compared to traditional methods. Additionally, a meta-analysis on home telemonitoring systems (hTMS) showed a significant reduction in adverse outcomes, particularly in non-invasive hTMS studies, advocating their integration into outpatient management. Furthermore, a study protocol for a randomized controlled trial (RCT) aimed to promote physical activity in HF patients was developed. Lastly, PCKS9 inhibitors were found to be well-tolerated in real-world populations, with an adverse events profile comparable to RCTs. The second part of this thesis focused on risk stratification primarily through the analysis of serial measurements of blood biomarkers in both HF and post-ACS patients. The prognostic value of growth differentiation factor 15 (GDF-15) and other biomarkers was explored. Serial measurements of GDF-15 emerged as a strong predictor of adverse outcomes. Interestingly, concentrations rose before an adverse outcome during follow-up. Additionally, the prognostic value of iron deficiency in post-ACS patients was investigated, highlighting its association with an increased risk for adverse outcomes and its potential as a target in post-ACS management. Lastly, in a heart transplantation database, pre-transplant chronic kidney disease was identified as a significant risk factor for the incidence of malignancy post-transplantation, emphasizing strategies to mitigate these risks pre-transplantation. Overall, this thesis provides valuable insights into utilizing big data analysis and serial biomarker measurements to enhance clinical decision-making in CVD, specifically focusing on HF and ACS. These findings contribute to advancing personalized medicine approaches that could revolutionize CVD management and mitigate the growing healthcare burden associated with this condition.<br/

Early indication of decompensated heart failure in patients on home-telemonitoring: a comparison of prediction algorithms based on daily weight and noninvasive transthoracic bio-impedance

Background: Heart Failure (HF) is a common reason for hospitalization. Admissions might be prevented by early detection of and intervention for decompensation. Conventionally, changes in weight, a possible measure of fluid accumulation, have been used to detect deterioration. Transthoracic impedance may be a more sensitive and accurate measure of fluid accumulation. Objective: In this study, we review previously proposed predictive algorithms using body weight and noninvasive transthoracic bio-impedance (NITTI) to predict HF decompensations. Methods: We monitored 91 patients with chronic HF for an average of 10 months using a weight scale and a wearable bio-impedance vest. Three algorithms were tested using either simple rule-of-thumb differences (RoT), moving averages (MACD), or cumulative sums (CUSUM). Results: Algorithms using NITTI in the 2 weeks preceding decompensation predicted events (P&lt;.001); however, using weight alone did not. Cross-validation showed that NITTI improved sensitivity of all algorithms tested and that trend algorithms provided the best performance for either measurement (Weight-MACD: 33%, NITTI-CUSUM: 60%) in contrast to the simpler rules-of-thumb (Weight-RoT: 20%, NITTI-RoT: 33%) as proposed in HF guidelines. Conclusions: NITTI measurements decrease before decompensations, and combined with trend algorithms, improve the detection of HF decompensation over current guideline rules; however, many alerts are not associated with clinically overt decompensation