Development and implementation of a nurse-based remote patient monitoring program for ambulatory disease management.

Introduction: Numerous factors are intersecting in healthcare resulting in an increased focus on new tools and methods for managing care in patients' homes. Remote patient monitoring (RPM) is an option to provide care at home and maintain a connection between patients and providers to address ongoing medical issues. Methods: Mayo Clinic developed a nurse-led RPM program for disease and post-procedural management to improve patient experience, clinical outcomes, and reduce health care utilization by more directly engaging patients in their health care. Enrolled patients are sent a technology package that includes a digital tablet and peripheral devices for the collection of symptoms and vital signs. The data are transmitted from to a hub integrated within the electronic health record. Care team members coordinate patient needs, respond to vital sign alerts, and utilize the data to inform and provide individualized patient assessment, patient education, medication management, goal setting, and clinical care planning. Results: Since its inception, the RPM program has supported nearly 22,000 patients across 17 programs. Patients who engaged in the COVID-19 RPM program experienced a significantly lower rate of 30-day, all-cause hospitalization (13.7% vs. 18.0%, P = 0.01), prolonged hospitalization >7 days (3.5% vs. 6.7%, P = 0.001), intensive care unit (ICU) admission (2.3% vs. 4.2%, P = 0.01), and mortality (0.5% vs. 1.7%, P = 0.01) when compared with those enrolled and unengaged with the technology. Patients with chronic conditions who were monitored with RPM upon hospital discharge were significantly less likely to experience 30-day readmissions (18.2% vs. 23.7%, P = 0.03) compared with those unmonitored. Ninety-five percent of patients strongly agreed or agreed they were likely to recommend RPM to a friend or family member. Conclusions: The Mayo Clinic RPM program has generated positive clinical outcomes and is satisfying for patients. As technology advances, there are greater opportunities to enhance this clinical care model and it should be extended and expanded to support patients across a broader spectrum of needs. This report can serve as a framework for health care organizations to implement and enhance their RPM programs in addition to identifying areas for further evolution and exploration in developing RPM programs of the future.

Sensorimotor training at home in elderly people with Parkinson's Disease during the period of confinement due to COVID-19: A pilot study

The pandemic caused those therapeutic activities associated with people with Parkinson's disease to be suspended (PD) with the aim of preventing COVID-19 infections by increasing motor and non-motor symptoms, due to social isolation and stress. Therefore, there was a rise of care based on telemedicine to continue the treatment in the context of a health emergency, promoting training at home. The objective of this experimental research was to document the effects of a sensorimotor training program (NM-FITT) in people with PD through weekly telematic follow-up during the COVID-19 pandemic. A quasi-experimental study was carried out with pre, inter and post test evaluations that characterized the physical, functional condition and quality of life, in an intervention group (n=5) and a control group (n=5) of elderly people (69.5 +/-4.5 years) with PD in Hoehn & Yahr stages 1 to 3, for 18 weeks, through an exercise manual, video calls and telephone follow-up. Favorable and significant effects were demonstrated on all the parameters studied after the application of the intervention in detriment of the health condition of the persons who did not participate in the home training program. Copyright © 2022 Fundacion para la difusion neurologica en Ecuador - FUNDINE. All rights reserved.

Design and Implementation of Remote Health Monitoring System and Application for Priority Recognition Using Machine Learning

This work demonstrates a remote health monitoring system that provides a holistic perspective of cases and their health conditions. Remote Patient Monitoring (RPM) systems will play a conspicuous role in the millennium of medical management. In this paper, to monitor covid patients during their quarantine days to keep track of chronic circumstances. For that, the model of a non-reactive preference grading independently in a single device to collect the essential parameters like blood Oxygen level, temperature and pulse rate. To predict and conduct the priority division using supervised machine learning algorithm for the received medical packets and relay them according to their priorities. This hitch results in transmitting advanced significance data packets of high importance in an advanced average waiting time. In this design, to acknowledge a vital criterion distinguishing the priority of health-info carried by a file and other low-ranking digital data parcels of different cases. The stored data then given for the supervised machine learning classification algorithms. In that the better accuracy of priority classification of 93.5% obtained from support vector machine (SVM) algorithm outperforms than the other machine learning classifiers and are 91%, 88%, 89% with respect to Multilayer Perception(MLP), Baysian Network (BN) and Logisitic Regression(LR). © 2022 IEEE.

12-Lead ECG Platform for Real-time Monitoring and Early Anomaly Detection

In response to the rapid digital revolution and the COVID-19 pandemic, the healthcare landscape has significantly shifted from physical to virtual care and telemedicine. As a result, healthcare providers and patients have shown increased interest and adoption for up-to-date technologies to monitor ongoing health conditions, including cardiovascular diseases. Driven by the importance of an efficient remote cardiovascular monitor for virtual care, we present a platform that enables remote ECG testing and provides ubiquitous data access to patients and their healthcare providers. A patent-pending 12-lead data acquisition ECG patch is attached to the patient's body to simultaneously collect heart signals, perform binary classification, and transmit the data to healthcare providers for further analysis at a high rate of up to 480 samples per second. As a preliminary classification phase, the presented platform introduces a machine learning technique to classify ECG signals near the ECG patch. The classification function is optimized for power-constrained devices using machine learning techniques. Moreover, the preliminary results of the energy consumption profile show that the ECG patch provides up to 37 hours of continuous 12-lead ECG streaming. © 2022 IEEE.

Resting 12­lead ECG tests performed by patients at home amid the COVID-19 pandemic - Results from the first 1000 patients.

BACKGROUND: There were surges in the demand for telehealth and home care in the COVID-19 pandemic. A new home ECG testing model was developed and used in the real-world clinical practice. METHODS: Since June 2020, QT Medical, Inc. (Diamond Bard, California) has been providing home ECG testing service by mail. Upon receiving the order from a clinician, an ECG testing kit was sent to the patient by mail. The kit included an ECG recorder, a prepositioned electrode strip of proper size for the patient (determined by the ordering clinician), printed instructions for performing the test, and a return envelope. We reviewed and analyzed the de-identified administrative dataset of the first 1000 ECG tests ordered by 37 medical practices. RESULTS: Of the 1000 patients served by this mail delivery home ECG testing service, 77.3% were female and 22.7% were male. Their ages ranged from 1 year old to 96 years old, mean 49.5 ± 13.4 years (median 52). 92.9% patients completed their tests with clinical quality ECGs uploaded to their ordering clinician's online accounts. Of those who did not complete the tests, the main reason was they "no longer needed the test". Failure to complete the test due to technical issues was 1.4%. Only one patient had to repeat the test due to inadequate ECG quality as judged by the ordering physician. The median turnaround time, from the kit being mailed out to the recorder being returned, was 10 days. Overall, 2.2% of the ECG devices were lost in shipping or unreturned by patients. CONCLUSION: Of the first 1000 patients who had their ECG tests at homes, it was found that this home ECG testing platform and care model could be reliably used by patients with no training to acquire clinical grade ECG. The current study proved that medical standard, resting 12­lead ECG can be performed by the majority of patients at home.

IoT based Resuscitation and Hypoxemia Diagnosis System using Embedded Microcontroller Architecture and IFTTT Implementation

In recent times, the World has taken a critical blow from the novel Coronavirus. Moreover, with its research ongoing and multiple variants emerging, uncertainty is on the rise. Lung damage and cardiac issues occur during and post recovery which is why there is a requirement to constantly monitor the patient’s vitals. This also applies to patients suffering from other lung diseases and heart conditions. They need immediate attention to avoid complications and further deterioration in their health. The proposed system aims to do so by observing the health parameters such as oxygen saturation in blood and heartbeat constantly and making the data available for the user by displaying it. In case of abnormality, it alerts the person related to the patient about the developments and relays the required information to help like symptom details and the patient’s location, this ensures that actions are taken quickly. Simultaneously, it provides assistance in breathing during the interlude till professional help arrives and the patient receives the necessary treatment. This is achieved with a combination of sensors, modules and other components integrated with a microcontroller as well as the implementation of Internet of Things (IoT) using a web service.

Parkinson's Disease Remote Patient Monitoring During the COVID-19 Lockdown.

Objective: To evaluate the feasibility of a smartphone remote patient monitoring approach in a real-life Parkinson's disease (PD) cohort during the Italian COVID-19 lockdown. Methods: Fifty-four non-demented PD patients who were supposed to attend the outpatient March clinic were recruited for a prospective study. All patients had a known UPDRS-III and a modified Hoehn and Yahr (H&Y) score and were provided with a smartphone application capable of providing indicators of gait, tapping, tremor, memory and executive functions. Different questionnaires exploring non-motor symptoms and quality of life were administered through phone-calls. Patients were asked to run the app at least twice per week (i.e., full compliance). Subjects were phone-checked weekly throughout a 3-week period for compliance and final satisfaction questionnaires. Results: Forty-five patients (83.3%) ran the app at least once; Twenty-nine (53.7%) subjects were half-compliant, while 16 (29.6%) were fully compliant. Adherence was hindered by technical issues or digital illiteracy (38.7%), demotivation (24%) and health-related issues (7.4%). Ten patients (18.5%) underwent PD therapy changes. The main factors related to lack of compliance included loss of interest, sadness, anxiety, the absence of a caregiver, the presence of falls and higher H&Y. Gait, tapping, tremor and cognitive application outcomes were correlated to disease duration, UPDRS-III and H&Y. Discussion: The majority of patients were compliant and satisfied by the provided monitoring program. Some of the application outcomes were statistically correlated to clinical parameters, but further validation is required. Our pilot study suggested that the available technologies could be readily implemented even with the current population's technical and intellectual resources.