The Empirical Foundations of Telemedicine Interventions for Chronic Disease Management

The telemedicine intervention in chronic disease management promises to involve patients in their own care, provides continuous monitoring by their healthcare providers, identifies early symptoms, and responds promptly to exacerbations in their illnesses. This review set out to establish the evidence from the available literature on the impact of telemedicine for the management of three chronic diseases: congestive heart failure, stroke, and chronic obstructive pulmonary disease. By design, the review focuses on a limited set of representative chronic diseases because of their current and increasing importance relative to their prevalence, associated morbidity, mortality, and cost. Furthermore, these three diseases are amenable to timely interventions and secondary prevention through telemonitoring. The preponderance of evidence from studies using rigorous research methods points to beneficial results from telemonitoring in its various manifestations, albeit with a few exceptions. Generally, the benefits include reductions in use of service: hospital admissions/re-admissions, length of hospital stay, and emergency department visits typically declined. It is important that there often were reductions in mortality. Few studies reported neutral or mixed findings.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140284/1/tmj.2014.9981.pd

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

Spirituality And Spiritual Self-Care: Expanding Self-Care Deficit Nursing Theory

The purpose of this study was to extend the theory of self-care deficit nursing by including specific constructs of religion, spirituality, and spiritual self-care practices within the structure suggested by Orem\u27s self-care deficit nursing theory. Based on an extensive literature review, practice experience, and a discovery theory-building approach, a new mid-range theory called White\u27s theory of spirituality and spiritual self-care (WTSSSC) was developed. To begin to test this mid-range theory, empirical indices of many of the main concepts were identified from prior studies and one new instrument (the Spiritual Self-Care Practice Scale) was developed. Hypothesized relationships among the main concepts of the mid-range theory were examined and tested in a sample of 142 urban African American outpatients who had been previously diagnosed with heart failure. The results of this study provided support that White\u27s midrange theory of spirituality and spiritual self-care (WTSSSC) is a viable extension of Orem\u27s self-care deficit nursing theory (SCDNT). The relations between QOL and spirituality, spiritual self-care practices, chronic illness self-care for heart failure, and physical and mental health were statistically significant and in the expected directions. The midrange theory can be used to incorporate spirituality and spirituality self-care practices which can mitigate the effects of chronic disease related to overall QOL for African Americans who have been diagnosed with heart failure. Results of this study have provided additional support for the use of spiritual self-care practices to assist in managing chronic illness, specifically heart failure. Nurses who work with patients diagnosed with heart failure should provide instruction on self-care practices specifically for heart failure (weight and diet management, medication compliance, sleep, etc.) and then encourage the use of spiritual self-care practices to enhance the well-being and QOL for these individuals. Nursing education needs to include spirituality and the importance of spiritual self-care practices as part of teaching Orem\u27s theory of self-care to enhance patient health and QOL. This education could be presented in nursing education classes in colleges and universities; professional development programs; and presentations at state, regional, national and international conferences. Further research is needed to continue development of the WTSSSC

The PERMIT Project: Personalised Renal Function Monitoring via Information Technology

Patients with heart failure are typically elderly and are among those most at risk of renal failure due to both their condition and their medication. Regular monitoring of renal function may allow early detection of renal decline and appropriate intervention to prevent renal failure. However, clinical guidance on renal function monitoring in heart failure is sparse and based on anecdotal evidence. To reduce unnecessary admissions caused by renal impairment in heart failure due to inadequate monitoring, standardised practice for renal monitoring would be of benefit. Given that each patient has individual co-morbidities and rates of renal decline, general guidelines may have minimal impact and there may be a need for renal monitoring that is personalised case-by-case. The aim of the PERMIT project (Personalised Renal Function Monitoring via Information Technology) was to develop the framework for creating such personalised guidance by using machine-learning on large clinical datasets. The goal was to create a prediction model that could highlight which patients with heart failure were most at risk of renal decline, in order to intervene before they required hospital admission. In light of developing a future predictive algorithm for use in clinical care, patient and clinician engagement with heart failure-related remote healthcare technologies was investigated. The aim of this was to improve the knowledge base so that future technologies, such as remote renal monitoring, can improve upon their accessibility and acceptability in this patient cohort. Studies examining remote care in heart failure were thematically synthesised in a qualitative systematic review. This generated 5 core themes of engagement: Clinical Care, Convenience, Communication, Ease of use, and Education, with different perspectives from patients and healthcare staff. The themes which were generated were assessed prospectively via a discrete-choice questionnaire survey given to heart failure patients (n=93). Binary logit analysis showed that ‘Clinical care’ was most valued by patients with heart failure and was almost twice as important as ‘Communication’, the lowest ranked theme. The study provided important insights into the lived experiences of patients with heart failure that will allow the development of future interventions with greater acceptability and engagement rates. To create the predictive model for renal decline, retrospective primary care data was obtained from SIR (Salford Integrated Records). This data was processed into a longitudinal dataset which included 3800 adult patients with newly diagnosed heart failure, over an 8.5 year study window. The clinical parameters of each patient were mapped longitudinally with creatinine over time. A model-based clustering algorithm known as ‘flexmix’ was applied to the data. In order to select appropriate clinical variables to input into the clustering predictive model, pairwise mixed-model linear regression was used to determine correlation between each clinical parameter and log(creatinine). The most correlative covariates were serum urea and serum potassium, with urea showing the highest R-squared value for explaining variance in creatinine over time. The final clustering model therefore used the inputs of: age at heart failure diagnosis; time since heart failure diagnosis; gender; IMD decile; and serum urea. This process produced seven discrete clusters of renal change over time which were ranked by severity. Evaluation of the algorithm was made using the assigned cluster models to predict creatinine over time in patients with heart failure. The MAPE (mean absolute percentage error) of the creatinine prediction was between 17-33% depending on the cluster assigned. The work outlined in this thesis represents an important step towards developing personalised renal monitoring guidance. Important clinical correlates of renal function decline, identified in the process, can be used for prognostic research in future studies. The error of the prediction values was variable and will thus require further optimisation using additional datasets and clinical studies in the future