Experiences of Patients with Covid-19 from Home Care: A Qualitative Study

Introduction: The demand for home care services has increased not only due to the increase in the elderly population but also due to consumer preference and technological advances that allow for the provision of sophisticated home care. Home healthcare services aim to help people improve their performance and live a more independent life, improve their well-being, and help them stay home, avoiding hospitalization. This study aimed to study the Experiences of patients with COVID-19 from home care in Qazvin City.Methods: A qualitative study with a conventional content analysis method was used. Ten participants were selected using purposeful sampling from 5 October to 25 May 2020. In‐depth structured interviews were used to collect data. Data were analyzed by continuous comparative analysis using MAXQDA 10 software.Results: Data analysis on patients' experiences with COVID‐19 specified twelve mean units, eight subthemes, and four themes, including Economic, Emotional, Reliability, and Support.Conclusions: All patients have common experiences in the fields of Economic, Emotional, Reliability, and Support. In future waves of COVID‐19, or a new pandemic, home care holds the potential to serve as a source of overflow care when acute care settings are overburdened and patients are discharged from acute and long‐term care settings

INTERNET OF MEDICAL THINGS (IOMT) AND INTEGRATED HOME ASSISTANCE

This article furnishes an overview of the actual IoT technology used in integrated home assistance. It delineates how the IoMT devices are improving the implementation of integrated home assistance services, and how the IoT technology can influence the global healthcare assistance in upcoming years aiding healthcare systems by supplying secure and effective cures in a complementary or alternative way, even during periods of crisis or health epidemics, like that of "COVID-19." Healthcare assistance based on IoT and the use of deep machine learning can in fact help healthcare workers by giving them new and improved diagnostic capabilities. The combination of machines and clinical experience improves the reliability of the services of integrated home assistance. Artificial intelligence and deep learning can also optimize disease management, provide large amounts of data, and generate analytics from IoMT devices. Transforming the delivery of integrated home assistance healthcare services in this way, thanks to IoT, is essential for improving self-management for people with chronic illnesses and providing specialized care for people located far away or at home

IT Tools and Performance Indicators: A Qualitative Overview of Managerial, Organizational, Financial Strategies within Healthcare Sector

open1The work examines the different healthcare contexts in which innovation has been applied, or could be applied, resulting in cost containment and increased quality and efficiency of medical care services. In addition, the different factors influencing the adoption of information technologies in the national healthcare systems of the European Union are discussed, in particular as regards the existence of structural barriers. Innovation is defined as the creation of something still not existing, to be uses for new products and services or for more efficient processes and is therefore linked to change, because innovation requires change. Information technology (IT) is described as the acquisition, processing and storage of data by a computing product. This work qualitatively analyses use cases, which are in turn based on quantitative research methodologies (i.e. performance indicators), commonly based on the manipulation of independent variables to generate statistically analyzable data, which guarantees objectivity and provides greater data reliability. Studies have been conducted to observe current trends in access to information technology across different age groups, to detect the existence of correlations between Internet users and online healthcare information searches. In this work, several Italian initiatives for the diffusion of IT applications in the healthcare sector have been analyzed. Some of the ongoing pilot projects include the collaboration of the Politecnico di Milano, through the establishment of the Laboratory of Biomedical Technologies (TBMLab), and the Scuola Superiore Sant'Anna of Pisa, to carry out research on eHealth activities and to promote the development of home automation systems for patients with disabilities. The HHC-MOTES model should also be noted, which aims to analyze the implementation of IT in the healthcare (HHC) sector from the point of view of sustainability in the management, organizational, technological, environmental and social fields (MOTES).openRemondino, MarcoRemondino, Marc

Mobihealth: mobile health services based on body area networks

In this chapter we describe the concept of MobiHealth and the approach developed during the MobiHealth project (MobiHealth, 2002). The concept was to bring together the technologies of Body Area Networks (BANs), wireless broadband communications and wearable medical devices to provide mobile healthcare services for patients and health professionals. These technologies enable remote patient care services such as management of chronic conditions and detection of health emergencies. Because the patient is free to move anywhere whilst wearing the MobiHealth BAN, patient mobility is maximised. The vision is that patients can enjoy enhanced freedom and quality of life through avoidance or reduction of hospital stays. For the health services it means that pressure on overstretched hospital services can be alleviated

Validation of the Registered Nurse Assessment of Readiness for Hospital Discharge Scale

Background Statistical models for predicting readmissions have been published for high-risk patient populations but typically focus on patient characteristics; nurse judgment is rarely considered in a formalized way to supplement prediction models. Objectives The purpose of this study was to determine psychometric properties of long and short forms of the Registered Nurse Readiness for Hospital Discharge Scale (RN-RHDS), including reliability, factor structure, and predictive validity. Methods Data were aggregated from two studies conducted at four hospitals in the Midwestern United States. The RN-RHDS was completed within 4 hours before hospital discharge by the discharging nurse. Data on readmissions and emergency department visits within 30 days were extracted from electronic medical records. Results The RN-RHDS, both long and short forms, demonstrate acceptable reliability (Cronbach’s alphas of .90 and .73, respectively). Confirmatory factor analysis demonstrated less than adequate fit with the same four-factor structure observed in the patient version. Exploratory factor analysis identified three factors, explaining 60.2% of the variance. When nurses rate patients as less ready to go home (\u3c7 out of 10), patients are 6.4–9.3 times more likely to return to the hospital within 30 days, in adjusted models. Discussion The RN-RHDS, long and short forms, can be used to identify medical-surgical patients at risk for potential unplanned return to hospital within 30 days, allowing nurses to use their clinical judgment to implement interventions prior to discharge. Use of the RN-RHDS could enhance current readmission risk prediction models

Design of a randomized superiority trial of a brief couple treatment for PTSD.

Interpersonal difficulties are common among veterans with posttraumatic stress disorder (PTSD) and are associated with poorer treatment response. Treatment outcomes for PTSD, including relationship functioning, improve when partners are included and engaged in the therapy process. Cognitive-behavioral conjoint therapy for PTSD (CBCT) is a manualized 15-session intervention designed for couples in which one partner has PTSD. CBCT was developed specifically to treat PTSD, engage a partner in treatment, and improve interpersonal functioning. However, recent research suggests that an abbreviated CBCT protocol may lead to sufficient gains in PTSD and relationship functioning, and yield lower dropout rates. Likewise, many veterans report a preference for receiving psychological treatments through clinical videoteleconferencing (CVT) rather than traditional face-to-face modalities that require travel to VA clinics. This manuscript describes the development and implementation of a novel randomized controlled trial (RCT) that examines the efficacy of an abbreviated 8-session version of CBCT ("brief CBCT," or B-CBCT), and compares the efficacy of this intervention delivered via CVT to traditional in-person platforms. Veterans and their partners were randomized to receive B-CBCT in a traditional Veterans Affairs office-based setting (B-CBCT-Office), CBCT through CVT with the veteran and partner at home (B-CBCT-Home), or an in office-delivered, couple-based psychoeducation control condition (PTSD Family Education). This study is the first RCT designed to investigate the delivery of B-CBCT specifically to veterans with PTSD and their partners, as well as to examine the delivery of B-CBCT over a CVT modality; findings could increase access to care to veterans with PTSD and their partners

Perceived Readiness for Hospital Discharge in Adult Medical-Surgical Patients

Purpose: The purpose of the study was to identify predictors and outcomes of adult medical-surgical patients\u27 perceptions of their readiness for hospital discharge. Design: A correlational, prospective, longitudinal design with path analyses was used to explore relationships among transition theory-related variables. Setting: Midwestern tertiary medical center. Sample: 147 adult medical-surgical patients. Methods: Predictor variables included patient characteristics, hospitalization factors, and nursing practices that were measured prior to hospital discharge using a study enrollment form, the Quality of Discharge Teaching Scale, and the Care Coordination Scale. Discharge readiness was measured using the Readiness for Hospital Discharge Scale administered within 4 hours prior to discharge. Outcomes were measured 3 weeks postdischarge with the Post-Discharge Coping Difficulty Scale and self-reported utilization of health services. Findings: Living alone, discharge teaching (amount of content received and nurses\u27 skill in teaching delivery), and care coordination explained 51% of readiness for discharge score variance. Patient age and discharge readiness explained 16% of variance in postdischarge coping difficulty. Greater readiness for discharge was predictive of fewer readmissions. Conclusions: Quality of the delivery of discharge teaching was the strongest predictor of discharge readiness. Study results provided support for Meleis\u27 transitions theory as a useful model for conceptualizing and investigating the discharge transition. Implications for Practice: The study results have implications for the CNS role in patient and staff education, system building for the postdischarge transition, and measurement of clinical care outcomes

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution