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.

Aripiprazole in young people with early psychosis: a systematic review and meta-analysis of weight gain

Objective: Aripiprazole is often prescribed to young people, although there remain unanswered questions about its effects on weight gain. This study undertook a meta-analysis of weight gain occurring in young people with early psychosis who were prescribed aripiprazole. Method: A systematic search was conducted for studies reporting on aripiprazole and weight change in young people with a psychotic disorder. A meta-analysis integrated the data into an estimate of effect size. Results: Eleven studies met the inclusion criteria amounting to 886 participants (mean age 18 years). The results showed significant weight gain averaging 2.7 kg. These increases were associated with a longer duration of exposure to aripiprazole but not a higher dosage. Conclusions: The results highlight the importance of regular patient monitoring and the early implementation of interventions to manage antipsychotic-related weight gain.

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.

Smart Medical Kit in Chronic Kidney Disease Management

Internet of things (IoT)-enabled devices perform remote monitoring of patients and keep them healthy. They also facilitate physicians to provide high-quality care to their patients with accurate data. Chronic disease involves a wide range of health issues like diabetics, asthma, heart disease, kidney disease, and other disorders. To avoid disease progression, the IoT-based smart medical kit helps in episodic patient monitoring, continuous patient monitoring in acute conditions, and patient alarm monitoring. The chapter focuses on the deployment of interconnected devices (sensors, actuators, monitors, detectors, and camera systems) to collect data from heterogeneous systems. The output is connected to a think speak dashboard for monitoring the variation over the period. The smart kit provides more accurate and reliable recommendations to assist patients in controlling their chronic disease and assists in remote monitoring of a patient's health conditions.

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.