Individual Perception of Telehealth: Validation of a German Translation of the Telemedicine Perception Questionnaire and a Derived Short Version

Telehealth is a growing domain with particular relevance for remote patient monitoring. With respect to the biopsychosocial model of health, it is important to evaluate perception and satisfaction with new methods in telehealth as part of an integrative approach. The Telemedicine Perception Questionnaire (TMPQ) is a 17-item questionnaire measuring patients’ perception of and satisfaction with telecare. We translated this survey into German and determined its validity and reliability in 32 adolescents and adults. Furthermore, we derived a short version of the TMPQ, named Patient and Physician Satisfaction with Monitoring (PPSM), which is a 5-item questionnaire that can be administered to both patients and physicians. Validity and reliability were tested in 32 patients and 32 physicians. Crohnbach’s α for the translated TMPQ was 0.76, and the German version yielded high validity (intraclass correlation coefficient (ICC) 0.995). We tested the PPSM in both patients and physicians and found acceptable values for Crohnbach’s α (0.72 and 0.78) with excellent validity (ICC 0.965). We therefore concluded from this small study that both German versions of the TMPQ and PPSM can be used to investigate the acceptance of telehealth applications.

A Retrospective Study on “Effectiveness of Remote Patient Monitoring in Covid-19 patients"

Corona virus disease19 has spread over the world, affecting millions of people. It has put enormous strain on the global healthcare system. Due to frequent mutations, the pandemic is spreading rapidly. The world requires a technology that will facilitate the effective diagnosis, treatment, and discharge of COVID19 patients. A model like remote patient monitoring [RPM] makes it easier to handle Covid 19 patients. RPM helps in remotely diagnosis, treatment, as well as allowing for prompt interventions. The RPM makes use of mobile technology and IoT platforms to take clinical interventions. In this study out of 151 covid19 positive subjects 91% of them were shifted to home monitoring within 5 days of MVM monitoring with few readmissions. The study investigated the effectiveness of RPM in the Indian healthcare system, as well as the performance and usability of the Vigocare mobile application by patients and doctors.

Remote Patient Monitoring in Diabetes: How to Acquire, Manage, and Use All of The Data

The ability of patients and health care providers to use various forms of technology for general health has significantly increased in the past several years with the expansion of telehealth, digital applications, personal digital devices, smartphones, and other Internet-connected platforms and devices. For individuals with diabetes, this also includes connected blood glucose meters, continuous glucose monitoring devices, and insulin delivery systems. In this article, the authors outline several steps to facilitate the acquisition, management, and meaningful use of digital diabetes data that can enable successful implementation of both diabetes technology and telehealth services in primary care clinics.

Secured Healthcare Data Analytics on the Cloud Using Blockchain-Based Techniques

Blockchain and the internet of things (IoT) are progressive technologies that are changing the world with additional special care within the healthcare system. In healthcare, IoT is a remote patient monitoring system that allows IoT devices to collect patient information such as remote monitoring, test results, pharmacy detailsm and medical insurance details, and allows doctors to provide excellent care. In order to facilitate data sharing among different hospitals and other organizations, it is necessary to secure data with caution. Blockchain is a decentralized, distributed, and an immutable digital ledger that records healthcare transactions using peer-to-peer technology in an extremely secure manner. It uses the cloud environment to store the huge amount of data on healthcare. The data generated from IoT devices uses blockchain technology to share medical information being analyzed by healthcare professionals in different hospitals in a secure manner. The objective is to benefit patient monitoring remotely and overcome the problem of information blocking.

Remote Patient Monitoring for Healthcare

Remote patient monitoring involves the collection of data from wearable sensors that typically requires analysis in real time. The real-time analysis of data streaming continuously to a server challenges data mining algorithms that have mostly been developed for static data residing in central repositories. Remote patient monitoring also generates huge data sets that present storage and management problems. Although virtual records of every health event throughout an individual's lifespan known as the electronic health record are rapidly emerging, few electronic records accommodate data from continuous remote patient monitoring. These factors combine to make data analytics with continuous patient data very challenging. In this chapter, benefits for data analytics inherent in the use of standards for clinical concepts for remote patient monitoring is presented. The openEHR standard that describes the way in which concepts are used in clinical practice is well suited to be adopted as the standard required to record meta-data about remote monitoring. The claim is advanced that this is likely to facilitate meaningful real time analyses with big remote patient monitoring data. The point is made by drawing on a case study involving the transmission of patient vital sign data collected from wearable sensors in an Indian hospital.

Feasibility of Wearable-Based Remote Monitoring in Patients During Intensive Treatment for Aggressive Hematologic Malignancies

PURPOSE Intensive treatment protocols for aggressive hematologic malignancies harbor a high risk of serious clinical complications, such as infections. Current techniques of monitoring vital signs to detect such complications are cumbersome and often fail to diagnose them early. Continuous monitoring of vital signs and physical activity by means of an upper arm medical wearable allowing 24/7 streaming of such parameters may be a promising alternative. METHODS This single-arm, single-center observational trial evaluated symptom-related patient-reported outcomes and feasibility of a wearable-based remote patient monitoring. All wearable data were reviewed retrospectively and were not available to the patient or clinical staff. A total of 79 patients (54 inpatients and 25 outpatients) participated and received standard-of-care treatment for a hematologic malignancy. In addition, the wearable was continuously worn and self-managed by the patient to record multiple parameters such as heart rate, oxygen saturation, and physical activity. RESULTS Fifty-one patients (94.4%) in the inpatient cohort and 16 (64.0%) in the outpatient cohort reported gastrointestinal symptoms (diarrhea, nausea, and emesis), pain, dyspnea, or shivering in at least one visit. With the wearable, vital signs and physical activity were recorded for a total of 1,304.8 days. Recordings accounted for 78.0% (63.0-88.5; median [interquartile range]) of the potential recording time for the inpatient cohort and 84.6% (76.3-90.2) for the outpatient cohort. Adherence to the wearable was comparable in both cohorts, but decreased moderately over time during the trial. CONCLUSION A high adherence to the wearable was observed in patients on intensive treatment protocols for a hematologic malignancy who experience high symptom burden. Remote patient monitoring of vital signs and physical activity was demonstrated to be feasible and of primarily sufficient quality.

Design a Remote Patient Health Monitoring System (RPHMS) Using IoT

Healthcare has become one of the principal issue with the rise in human population and medical expenditure. For a healthy life, it is essential to follow human body’s vital signals. Continuous Monitoring of patient’s vital signals cannot be provided outside hospital. As it is hard to monitor the patient’s condition for 24 hours, it was proposed in this paper to observe continuously the condition of patient despite the patient being busy with his routine and to screen the health status to the doctors through Internet of Things. This paper proposes health monitoring system using non-intrusive biomedical sensors that measure five parameters like ECG, SPO2, toxic gas, temperature and blood pressure. Proposed method makes use of Arduino Uno Controller to which non-invasive biomedical sensors are connected. The output is displayed on any digital monitoring system using Arduino Uno. The data obtained from the sensors is uploaded to the Thing Speak cloud to store and to access patient’s information by their doctors or by the concerned for necessary follow-ups in real-time. IoT is a powerful domain where sensors can connect and data is viewed over the Internet.