Remote Patient Monitoring System Research Articles

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges.

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.

Optimum Placement of Relay Nodes in WBANs for Improving the QoS of Indoor RPM System

Pandemic situation such as COVID-19 boosted the demand for remote patient monitoring (RPM) system. The medical sensors attached to the human body in RPM system experience varying channel quality due to body movements. This paper analyzes the signal received from RPM sensors when a patient rotates by different angles while sitting on a chair as well as heed the use of a relay node placed on his/her body. Literature suggests many relay-based communication protocols to deliver physio-signals efficiently in an RPM application. However, limited studies have focused on the position of a relay node on the human body. In this paper, we empirically analyze the off-body communication path of sensor nodes by collecting data from different body orientations in a residential room. We estimate the path loss parameters for underweight, normal and overweight body mass index (BMI) categories. The estimated parameters are then used to simulate the physical layer of a home-based indoor RPM application. We inspect different relay node positions on the human body and allude an optimal position of the relay node that cover the transmission range of all sensors and provides an improved channel quality. We improve the Quality of Service (QoS) during non-line-of-sight (NLOS) situation and design an adaptive cross-layer communication protocol for WBANs.

Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities.

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.

Mobile App-Based Remote Patient Monitoring in Acute Medical Conditions: Prospective Feasibility Study Exploring Digital Health Solutions on Clinical Workload During the COVID Crisis.

BackgroundDigital remote patient monitoring can add value to virtual wards; this has become more apparent in the context of the COVID-19 pandemic. Health care providers are overwhelmed, resulting in clinical teams spread more thinly. We aimed to assess the impact of introducing an app-based remote patient monitoring system (Huma Therapeutics) on a clinician’s workload in the context of a COVID-19–specific virtual ward.ObjectiveThis prospective feasibility study aimed to evaluate the health economic effects (in terms of clinical workload) of a mobile app on a telephone-based virtual ward used in the monitoring of patients with COVID-19 who are clinically ready for discharge from the hospital.MethodsA prospective feasibility study was carried out over 1 month where clinician workload was monitored, and full-time equivalents savings were determined. An NHS hospital repurposed a telephone-based respiratory virtual ward for COVID-19. Patients with COVID-19 in the amber zone (according to the National Health Service definition) were monitored for 14 days postdischarge to help identify deteriorating patients earlier. A smartphone-based app was introduced to monitor data points submitted by the patients via communication over telephone calls. We then comparatively evaluated the clinical workload between patients monitored by telephone only (cohort 1) with those monitored via mobile app and telephone (cohort 2).ResultsIn all, 56 patients were enrolled in the app-based virtual ward (cohort 2). Digital remote patient monitoring resulted in a reduction in the number of phone calls from a mean total of 9 calls to 4 calls over the monitoring period. There was no change in the mean duration of phone calls (8.5 minutes) and no reports of readmission or mortality. These results equate to a mean saving of 47.60 working hours. Moreover, it translates to 3.30 fewer full-time equivalents (raw phone call data), resulting in 1.1 fewer full-time equivalents required to monitor 100 patients when adjusted for time spent reviewing app data. Individual clinicians spent an average of 10.9 minutes per day reviewing data.ConclusionsSmartphone-based remote patient monitoring technologies may offer tangible reductions in clinician workload at a time when service is severely strained. In this small-scale pilot study, we demonstrated the economic and operational impact that digital remote patient monitoring technology can have in improving working efficiency and reducing operational costs. Although this particular RPM solution was deployed for the COVID-19 pandemic, it may set a precedent for wider utilization of digital, remote patient monitoring solutions in other clinical scenarios where increased care delivery efficiency is sought.

Investigation of ECG signal suitability for key generation to secure body area sensor networks

Remote patient monitoring (RPM) systems help to continuously monitor the health condition of people affected by chronic diseases. The proposed research work analyses the suitability of ECG signals for 128-bit cryptographic key generation in RPM systems. The randomness and distinctiveness of keys generated from the ECG signal using the following three methods are evaluated. (a) The Inter Pulse Intervals (IPI) of the ECG signal captured from the patient's body are converted to a 128-bit binary sequence using modular encoding. (b) Short-time Fourier transform (STFT) is applied over the ECG signal and the key is generated using the features extracted from ECG signal. (c) The binary sequence obtained with the help of STFT features of ECG signal is partitioned into subkeys and transformed into a 128-bit binary sequence using Tiny Encryption Algorithm (TEA). The results illustrate that the ECG signal can generate a highly random and distinctive 128-bit cryptographic key.

One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments

Cardiovascular diseases are considered the number one cause of death across the globe which can be primarily identified by the abnormal heart rhythms of the patients. By generating electrocardiogram (ECG) signals, wearable Internet of Things (IoT) devices can consistently track the patient's heart rhythms. Although Cloud-based approaches for ECG analysis can achieve some levels of accuracy, they still have some limitations, such as high latency. Conversely, the Fog computing infrastructure is more powerful than edge devices but less capable than Cloud computing for executing compositionally intensive data analytic software. The Fog infrastructure can consist of Fog-based gateways directly connected with the wearable devices to offer many advanced benefits, including low latency and high quality of services. To address these issues, a modular one-dimensional convolution neural network (1D-CNN) approach is proposed in this work. The inference module of the proposed approach is deployable over the Fog infrastructure for analysing the ECG signals and initiating the emergency countermeasures within a minimum delay, whereas its training module is executable on the computationally enriched Cloud data centers. The proposed approach achieves the F1-measure score ≈1 on the MIT-BIH Arrhythmia database when applying GridSearch algorithm with the cross-validation method. This approach has also been implemented on a single-board computer and Google Colab-based hybrid Fog-Cloud infrastructure and embodied to a remote patient monitoring system that shows 25% improvement in the overall response time.

MedPlus - a cross-platform application that allows remote patient monitoring

Healthcare is a vital human need and always had an important role in our lives, especially in the last year when the pandemic context raised a call for innovative and safe delivery methods. Together with the enlargement in the use of wearable sensors and smartphones, remote healthcare monitoring applications have unfolded at a fast pace, helping with the prevention of spreading diseases as well as to strengthen, help, and assist the health of a patient when a doctor is not physically available. This paper proposes a remote patient monitoring system within an application whose purpose is to bring patients closer to vital medical care. Based on voice interaction and wearable sensors, our application congregates the health data through the Google Fit app from the patient’s phone, and then it conducts analysis and sends automatic data alerts for detected anomalies, providing an appealing interface and capabilities to make it engaging to all age groups. Moreover, every patient is under the supervision of a doctor who is constantly monitoring the patient’s profile, ensuring assistance through presumptive diagnosis, treatments, or pieces of advice for boosting the quality of lifestyle.

Undergraduate Students’ Stress, Anxiety and Depression Remote Health Monitoring System

Mental health such as stress, anxiety and depression have been on the rise in recent time among different groups of people in the emerging challenging society and especially among university students. University is a place where most adolescents would experience a change phase in life, discovering their identity as well as experiencing several challenges. All these pressures might cause students to suffer from anxiety or stress or in some cases severe depression, which could lead them to isolate themselves from other people, or to have poor academic performance or very low self-esteem as well as dropping out from the universities. This is quite a dangerous trend because if such students are not treated early, the chances for their situation to worsen or ultimately attempting suicide would be high. Several technologies have been utilised to help assist clinicians and patients to confront and help mitigate health problems. Technologies such as eHealth, or mHealth which utilises smartphones and remote patient monitoring systems (RPMS) have been widely used in the healthcare and are proven to be efficient and quite reliable to help those who suffer from health issues. In this project, a remote mental health monitoring system(RMHMS) has been proposed to enable lecturers as well as university administration and therapists/counsellors to track students’ level of stress, anxiety and depression and swiftly engage those who suffer from stress, anxiety and depressive symptoms. Students who suffer from severe stress, anxiety symptoms and depression, will be given more attention by the university’s administrations based on the information available from the remote mental health monitoring system. The proposed system will help facilitate early interventions and treatment such as referrer to counselling sessions or seeking psychiatric help. The system equally incorporates alert systems to students’ friends or families and their caregivers to intimate them of their situation too so that they may help the students to overcome their mental health issues before getting worsened.

How to Design a Remote Patient Monitoring System? A French Case Study

BackgroundRemote Patient Monitoring Systems (RPMS) based on e-health, Nurse Navigators (NNs) and patient engagement can improve patient follow-up and have a positive impact on quality of care (by limiting adverse events) and costs (by reducing readmissions). However, the extent of this impact depends on effective implementation which is often restricted. This is partly due to the lack of attention paid to the RPMS design phase prior to implementation. The content of the RPMS can be carefully designed at this stage and various obstacles anticipated. Our aim was to report on an RPMS design case to provide insights into the methodology required in order to manage this phase.MethodsThis study was carried out at Gustave Roussy, a comprehensive cancer centre, in France. A multidisciplinary team coordinated the CAPRI RPMS design process (2013–2015) that later produced positive outcomes. Data were collected during eight studies conducted according to the Medical Research Council (MRC) framework. This project was approved by the French National Data Protection Authorities.ResultsBased on the study results, the multidisciplinary team defined strategies for resolving obstacles prior to the implementation of CAPRI. Consequently, the final CAPRI design includes a web app with two interfaces (patient and health care professionals) and two NNs. The NNs provide regular follow-up via telephone or email to manage patients’ symptoms and toxicity, treatment compliance and care packages. Patients contact the NNs via a secure messaging system. Eighty clinical decision support tools enable NNs to prioritise and decide on the course of action to be taken.ConclusionIn our experience, the RPMS design process and, more generally, that of any complex intervention programme, is an important phase that requires a sound methodological basis. This study is also consistent with the notion that an RPMS is more than a technological innovation. This is indeed an organizational innovation, and principles identified during the design phase can help in the effective use of a RPMS (e.g. locating NNs if possible within the care organization; recruiting NNs with clinical and managerial skills; defining algorithms for clinical decision support tools for assessment, but also for patient decision and orientation).

Which factors influence the accuracy of knee ROM data of the BPMpathway e-rehabilitation system? A concurrent validity study with healthy subjects

Aims and Objectives:As the population in different countries ages, there is an expected increase in the demand for TKA procedures in the coming decades. This increasing demand will face the healthcare systems and health care providers with new challenges. After TKA physiotherapy and exercise lead to short term improvements in physical function. Physiotherapy exercise provided at home is an appealing approach with the possibility of wider acceptability and uptake in the near future. E-rehabilitation or telerehabilitation are the terms used to define medical rehabilitation programs that are implemented at home with the use of information and communication technologies (ICT). The BPMpathway (270 Vision Ltd., Andover, UK) is an internet home-based biofeedback and remote patient monitoring system. The system comprises a single high-performance wearable wireless sensor, a patient application that can be downloaded for free and a software application for the clinic.The aim of the present study was to test the concurrent validity of the ROM data displayed by the BPMpathway system for knee exercises.Materials and Methods:This is a concurrent validity study conducted in a laboratory setting. The ROM data obtained with the BPMpathway system was compared with the ROM data acquired simultaneously with a twin-axis digital goniometer (BIOPAC Systems, Inc., California USA).The effects of sensor positioning (position of the sensor on the limb) and ergonomic factors (chair height) on ROM were investigated.The sample consisted of healthy male subjects with different body compositions.The data acquisition was carried out in both systems at a sample rate of 100Hz. Data was analyzed with MATLAB. The Root Mean Squared Error (RMSE) was used to calculate the difference between the values measured by the two devices. Additionally the mean ± SD values for each system were plotted and the absolute difference of the mean curves was plotted as measurement error. A ROM mean difference of 5° between both systems was considered as clinically irrelevant.Results:A 20% increase or reduction in sitting height led to a RMSE increase. A ventral shift of the BPMpathway sensor by 45° and 90° caused significant measurement errors. A vertical shift was associated with a diminution of the measurement errors. The lowest RMSE (2.4°) was achieved when the sensor was placed directly below the knee. The effects of the manipulations were similar for all subjects. RMSE values were higher in subject A, which had a high body mass index (BMI).Conclusion:The knee ROM data measured by the BPMpathway system seems reliable and comparable to the data of the concurrent system used in this investigation, provided the instructions of the manufacturer are respected concerning the sitting position of the subject for knee exercises, and disregarding the same instructions for sensor positioning, by placing the sensor directly below the knee. Changes in seating position and rotation shifts of the sensor induced measurement errors.

Remote patient monitoring in dialysis patients: the "change of pace" for home dialysis.

Lockdown and self-isolation are to date the only solution to limit the spread of recent outbreak of coronavirus disease (CoViD-19), highlighting the great advantage of home dialysis in a patient otherwise forced to travel from / to the dialysis center to receive this "life-saving" treatment. Indeed, to prevent spreading of CoViD-19 infection among extremely fragile dialysis patients, as well as among dialysis workers, hemodialysis (HD) centers are adopting specific procedures ("dedicated" dialysis facilities, portable osmosis, etc.) with a great economic and organizational commitment. Peritoneal dialysis (PD) represents a type of home dialysis therapy not yet adequately implemented to date, in spite of safe and simple practice, as well as similar dialytic efficiency vs in-center hemodialysis. Remote patient monitoring (RPM) systems have been developed in automated PD (APD) cyclers in order to improve the acceptance of this dialysis method, to increase the compliance to the prescribed therapy and to control treatment adequacy. In this review we assess the potential advantages of RPM in APD, that are the chance for patients to acquire greater independence and safety in the home treatment, to allow better access to care for residents in remote areas, faster resolution of problems, reduction in hospitalizations and mortality rates, as well as time and cost saving for both the patient and the staff. The use of medical devices (sphygmomanometer, glucometer, balance, etc.), connected by wireless to the clinician's portal, might also allow a wider diffusion of incremental dialysis, an integrated therapy that combines conservative management of ESKD patients with a soft dialysis based on the residual kidney function and symptomatology, with potential prognosis and economic benefits. Although the majority of the studies are small and observational, a wider use of RPM systems is desirable to broaden the spread of home dialysis, as we learnt from Coronavirus pandemic.

Assessment of remote patient monitoring (RPM) systems for patients with type 2 diabetes: a systematic review and meta-analysis.

The objective of this study is to conduct an assessment of Remote Patient Monitoring (RPM) systems compared to usual care for controlling glycosylated hemoglobin in type 2 diabetes. The study was a systematic review with meta-analysis and meta-regression. A systematic search was performed via the most important electronic databases of medical resources, such as PubMed, Scopus and Cochrane library. The main outcome was HbA1C. The heterogeneity sources were examined using Chi-square (Q) and I2 tests. Meta-analyses were done using Stata version 11 software. Statistical significance was defined as P < 0.05. Random effects model was used in meta-analysis, and the heterogeneity more than 50% was considered as significant. The results of the systematic review and meta-analysis indicated that the effect size index (Difference of Pre-test/Post-test Control Design-2nd method "using pooled pretest SD" (DPPC2)) among users of RPM for type 2 diabetic patients was -0.32 with a confidence interval of 95% (from -0.45 to -0.19) as compared to the control group. The current study declared a vital role of RPM technology in reduction of hemoglobin glycogen levels. The results of the subgroup analysis showed that RPM is more effective for patients who are residents of cities, having intervention lengths less than 6months, getting the orders from the physician and using the websites as their intervention type. The current study indicted the efficacy of RPM in reducing HbA1c among type 2 diabetic patients, which could be a base for policymakers to decide on the introduction of this technology in Iran.

Impact on Readmission Reduction Among Heart Failure Patients Using Digital Health Monitoring: Feasibility and Adoptability Study.

BackgroundHeart failure (HF) is a condition that affects approximately 6.2 million people in the United States and has a 5-year mortality rate of approximately 42%. With the prevalence expected to exceed 8 million cases by 2030, projections estimate that total annual HF costs will increase to nearly US $70 billion. Recently, the advent of remote monitoring technology has significantly broadened the scope of the physician’s reach in chronic disease management.ObjectiveThe goal of our program, named the Heart Health Program, was to examine the feasibility of using digital health monitoring in real-world home settings, ascertain patient adoption, and evaluate impact on 30-day readmission rate.MethodsA digital medicine software platform developed at Mount Sinai Health System, called RxUniverse, was used to prescribe a digital care pathway including the HealthPROMISE digital therapeutic and iHealth mobile apps to patients’ personal smartphones. Vital sign data, including blood pressure (BP) and weight, were collected through an ambulatory remote monitoring system that comprised a mobile app and complementary consumer-grade Bluetooth-connected smart devices (BP cuff and digital scale) that send data to the provider care teams. Care teams were alerted via a Web-based dashboard of abnormal patient BP and weight change readings, and further action was taken at the clinicians’ discretion. We used statistical analyses to determine risk factors associated with 30-day all-cause readmission.ResultsOverall, the Heart Health Program included 58 patients admitted to the Mount Sinai Hospital for HF. The 30-day hospital readmission rate was 10% (6/58), compared with the national readmission rates of approximately 25% and the Mount Sinai Hospital’s average of approximately 23%. Single marital status (P=.06) and history of percutaneous coronary intervention (P=.08) were associated with readmission. Readmitted patients were also less likely to have been previously prescribed angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (P=.02). Notably, readmitted patients utilized the BP and weight monitors less than nonreadmitted patients, and patients aged younger than 70 years used the monitors more frequently on average than those aged over 70 years, though these trends did not reach statistical significance. The percentage of the 58 patients using the monitors at least once dropped from 83% (42/58) in the first week after discharge to 46% (23/58) in the fourth week.ConclusionsGiven the increasing burden of HF, there is a need for an effective and sustainable remote monitoring system for HF patients following hospital discharge. We identified clinical and social factors as well as remote monitoring usage trends that identify targetable patient populations that could benefit most from integration of daily remote monitoring. In addition, we demonstrated that interventions driven by real-time vital sign data may greatly aid in reducing hospital readmissions and costs while improving patient outcomes.

Topology design of remote patient monitoring system concerning qualitative and quantitative issues

Piecewise linear functions have been used to model and solve non-linear problems in science, social science, and technology fields since the 1950′s. Multi-choice goal programming (MCGP) has been widely used to solve multiple objective decision-making (MODM) problems in which each goal mapping with multiple aspiration levels is allowed, expanding the original feasible region to obtain better solutions in the MODM problems. This paper integrates the efficient S-shaped penalty method, arbitrary piecewise linear utility functions, trapezoidal utility functions, and MCGP to solve a topology design problem in a remote patient monitoring system (RPMS) by providing universal senior citizen coverage in which quantitative (e.g., patient satisfaction-related cost) and qualitative (e.g., satisfaction) issues are considered simultaneously. In addition, some novel utility functions such as the force utility function, indicator utility function, and arbitrary utility function are proposed to improve the usefulness of MCGP in the field of management science. These proposed utility functions can be easily used to model qualitative issues in real-world problems. A topology design problem for an RPMS is demonstrated to justify the feasibility, usefulness, and compatibility of the proposed methods. Further, sensitivity analysis and managerial implications are provided using an RPMS in Taiwan.

Novel Remote Patient Monitoring System Improves Key Outcomes

Introduction Remote Patient Monitoring (PRM) has recently been discouraged given overall neutral results of telehealth interventions. Nevertheless, many HF programs rely on some form of RPM, including our own program at HealthEast, Fairview where a vendor supplied external RPM system had been used for many years with favorable clinical outcomes and cost of care reductions; however, the RPM was costly, had limiting reporting, and minimal capacity for customization. As a result, an analogous RPM, MyHealthTracker (MHT) was created and piloted in 2017 using Epic, MyChart, and telephony capabilities. Methods An Epic database was used to identify HF patients taking an oral diuretic. Qualifying patients were enrolled, tracked, and provided protocol driven care administered by Population Health Beta Implementation Nurses. Care included adjustment of diuretics, laboratory testing, and clinic visits. Baseline data including hospitalizations, related 30 Day Readmissions, and ED visits were extracted from the years 2016-2017. The same data set was extracted for the enrollment year (2018). The data were partitioned into six month segments over that three year time span according to date of admission to the hospital or ED. Results A total of 128 patients met criteria and were enrolled in the pilot totaling 8602 patient days post enrollment in Q1 and 2 of 2018 and 17,999 patient days post enrollment in 2018 Q3-4. Seasonal variations were adjusted for comparing like quarters pre and post enrollment. Post-enrollment reductions in hospitalization, 30 Day Readmissions, and ED visits were evident. Post enrollment hospitalizations were reduced by 33% compared to prior period. Similarly, 30 Day Readmissions were reduced by 75% and ER visits were reduced by 52%. Based on a typical total cost of care of $13,000/admission and $2,300/ED visit, a projected $310,400 cost was avoided including previous vendor cost. Conclusions Despite inconsistent evidence to support the use of RPM, HF programs continue to utilize them, often at an operational expense with variable outcomes. An internally developed RPM system, MHT, allows for more robust reporting, can be modified without vendor costs, and scaled to support healthcare systems. Further, MHT has demonstrated reductions in hospitalizations, 30 Day Readmissions, and ED visits with projected cost savings exceeding $2400/patient enrolled. This RPM is in use and will be augmented with ongoing data collection, including stratifying patients based on HF stage or class, mortality, diuretic dosage, renal function, and variance reports.

Effect of remote monitoring on patient-reported outcomes in European heart failure patients with an implantable cardioverter-defibrillator: primary results of the REMOTE-CIED randomized trial.

The European REMOTE-CIED study is the first randomized trial primarily designed to evaluate the effect of remote patient monitoring (RPM) on patient-reported outcomes in the first 2 years after implantation of an implantable cardioverter-defibrillator (ICD). The sample consisted of 595 European heart failure patients implanted with an ICD compatible with the Boston Scientific LATITUDE® RPM system. Patients were randomized to RPM plus a yearly in-clinic ICD check-up vs. 3-6-month in-clinic check-ups alone. At five points during the 2-year follow-up, patients completed questionnaires including the Kansas City Cardiomyopathy Questionnaire and Florida Patient Acceptance Survey (FPAS) to assess their heart failure-specific health status and ICD acceptance, respectively. Information on clinical status was obtained from patients' medical records. Linear regression models were used to compare scores between groups over time. Intention-to-treat and per-protocol analyses showed no significant group differences in patients' health status and ICD acceptance (subscale) scores (all Ps > 0.05). Exploratory subgroup analyses indicated a temporary improvement in device acceptance (FPAS total score) at 6-month follow-up for secondary prophylactic in-clinic patients only (P < 0.001). No other significant subgroup differences were observed. Large clinical trials have indicated that RPM can safely and effectively replace most in-clinic check-ups of ICD patients. The REMOTE-CIED trial results show that patient-reported health status and ICD acceptance do not differ between patients on RPM and patients receiving in-clinic check-ups alone in the first 2 years after ICD implantation.ClinicalTrials.gov Identifier: NCT01691586.

A novel framework for a remote patient monitoring (RPM) system with abnormality detection

ObjectiveThis research aims to build an effective and automated RPM system based on an information communication technology (ICT) framework that automatically triggers medical alerts, hospital visits, medications, and social support in a timely and precise manner, with specific attention to aging people. The goal of the proposed framework is to construct a safe, flexible, and manageable system containing multi-agent, auto-notice, sensor management, and secure transmission of data to allow an autonomous functioning of RPM. MethodsThe literature between 2002 and 2018 that introduces healthcare platforms with the technology of the Internet of things (IoT) is reviewed and compared in compliance with various technical and functional features. Eleven healthcare-based IoT platforms are compared. Eight of them utilize the Representational State Transfer Application Programming Interface (REST API), while ten of them have Message Queue Telemetry Transport (MQTT), seven of them have device management, and nine of them have security control. In addition, Kepware's KEPServerEX communication platform containing up to 250 communication protocols is included. ResultsThe results confirm that the proposed framework reliably operates within the complex healthcare processes, taking into account the equipment management, security management, messaging and diversification of communication protocols. This study uses the concept of IoT 3-tier through which a complete sensor management mechanism is structured. Moreover, secure data connection protects the patient's privacy. Each sensor provides a distinctive notification to construct flawless authority management. ConclusionsThe proposed framework can not only reach the machine-to-machine (M2M) realm, but also provide support in emergency cases.

Mobile android-based remote patient monitoring system through wearable sensors

A Hypertension is the most crucial reasons for sudden death. As indicated by WHO (World Health Organization); in the modern world, 330 million individuals have hypertension. In 2025, it might be around 1.56 billion individuals living with high blood pressure. In this advanced world, cell phone and web becomes a typical spot to associate with the general population. Presently we can utilize it as wellbeing administration for general wellbeing monitoring. Blood pressure is a standout amongst the most widely recognized diseases. Once in a while pulse estimations are taken with the patients arm in the wrong position which can cause mistaken readings. Smart phones applications can tackle this issue. Cell phone applications that interface with individual sensors have demonstrated more successful for health-data group. Blood pressure is essential since hypertension is the higher danger of medical issue later on. At the point when pulse is high, it begins to harm the veins, heart, and kidney. It can prompt heart attack, stroke and other issue. It frequently has no notice signs or manifestations, and many people don’t understand about it. We present an incorporated, compact gadget to give an advantageous answer for remote observing pulse at the fingertip and body temperature utilizing Ethernet innovation and generally online methods. Presently diseases related to heart are increasing. The majority of the occasions in these cases, patients may not understand their genuine conditions and even there are no specialists close by, particularly in village zones, however at this point, a large portion of the diseases are treatable whenever recognized in time. We have endeavored to make a framework which may give data about one’s physical condition and help him/her to identify these dangerous however reparable illnesses. The framework gives data of heart rate while procured on the versatile side in realtime and transmits results to web. In this framework, the state of heart and body temperature can be observed from remote spots. In the long run, this gadget gives an ease, effectively open human wellbeing screen arrangement spanning the holes among patients and specialists.

Remote Patient Monitoring Using Mobile Health for Total Knee Arthroplasty: Validation of a Wearable and Machine Learning–Based Surveillance Platform

BackgroundRecent technologic advances capable of measuring outcomes after total knee arthroplasty (TKA) are critical in quantifying value-based care. Traditionally accomplished through office assessments and surveys with variable follow-up, this strategy lacks continuous and complete data. The primary objective of this study was to validate the feasibility of a remote patient monitoring (RPM) system in terms of the frequency of data interruptions and patient acceptance. Second, we report pilot data for (1) mobility; (2) knee range of motion, (3) patient-reported outcome measures (PROMs); (4) opioid use; and (5) home exercise program (HEP) compliance. MethodsA pilot cohort of 25 patients undergoing primary TKA for osteoarthritis was enrolled. Patients downloaded the RPM mobile application preoperatively to collect baseline activity and PROMs data, and the wearable knee sleeve was paired to the smartphone during admission. The following was collected up to 3 months postoperatively: mobility (step count), range of motion, PROMs, opioid consumption, and HEP compliance. Validation was determined by acquisition of continuous data and patient tolerance at semistructured interviews 3 months after operation. ResultsOf the 25 enrolled patients, 100% had uninterrupted passive data collection. Of the 22 available for follow-up interviews, all found the system motivating and engaging. Mean mobility returned to baseline within 6 weeks and exceeded preoperative baseline by 30% at 3 months. Mean knee flexion achieved was 119°, which did not differ from clinic measurements (P = .31). Mean KOOS improvement was 39.3 after 3 months (range: 3-60). Opioid use typically stopped by postoperative day 5. HEP compliance was 62% (range: 0%-99%). ConclusionsIn this pilot study, we established the ability to remotely acquire continuous data for patients undergoing TKA, who found the application to be engaging. RPM offers the newfound ability to more completely evaluate the patients undergoing TKA in terms of mobility and rehabilitation compliance. Study with more patients is required to establish clinical significance.

Fuzzy-Based Dynamic Time Slot Allocation forWireless Body Area Networks.

With the advancement in networking, information and communication technologies, wireless body area networks (WBANs) are becoming more popular in the field of medical and non-medical applications. Real-time patient monitoring applications generate periodic data in a short time period. In the case of life-critical applications, the data may be bursty. Hence the system needs a reliable energy efficient communication technique which has a limited delay. In such cases the fixed time slot assignment in medium access control standards results in low system performance. This paper deals with a dynamic time slot allocation scheme in a fog-assisted network for a real-time remote patient monitoring system. Fog computing is an extended version of the cloud computing paradigm, which is suitable for reliable, delay-sensitive life-critical applications. In addition, to enhance the performance of the network, an energy-efficient minimum cost parent selection algorithm has been proposed for routing data packets. The dynamic time slot allocation uses fuzzy logic with input variables as energy ratio, buffer ratio, and packet arrival rate. Dynamic slot allocation eliminates the time slot wastage, excess delay in the network and attributes a high level of reliability to the network with maximum channel utilization. The efficacy of the proposed scheme is proved in terms of packet delivery ratio, average end to end delay, and average energy consumption when compared with the conventional IEEE 802.15.4 standard and the tele-medicine protocol.