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Remote Patient Monitoring Systems (RPMS) are vital for tracking patients’ health outside clinical settings, such as at home or in long-term care facilities. Wearable sensors play a crucial role in these systems by continuously collecting and transmitting health data. However, selecting the optimal sensor is challenging due to the wide variety of available options and diverse patient needs. To address this paper, introduce score and accuracy functions for Triangular Fermatean Fuzzy Numbers (TFFNs) and propose a novel Triangular Fermatean Fuzzy Sugeno–Weber Weighted Average (TFFSWWA) aggregation operator. In this paper establish key properties of TFFSWWA, confirming its ability to manage fuzzy uncertainty effectively. Using TFFSWWA, we develop an improved Evaluation based on Distance from Average Solution (EDAS) method for multi-criteria group decision-making (MCGDM) under TFFN settings. A case study on wearable sensor selection demonstrates the proposed model’s efficiency. We present an algorithm and a flowchart to guide the decision-making process, alongside a computational example that verifies the method’s reliability. Sensitivity analysis and comparison with existing methods show that the proposed approach improves decision accuracy and stability, highlighting its practical utility in healthcare decision-making.

Remote patient monitoring should reduce mortality rates, improve care, and reduce costs. We present an overview of the available technologies for the remote monitoring of chronic obstructive pulmonary disease (COPD) patients, together with the most important medical information regarding COPD in a language that is adapted for engineers. Our aim is to bridge the gap between the technical and medical worlds and to facilitate and motivate future research in the field. We also present a justification, motivation, and explanation of how to monitor the most important parameters for COPD patients, together with pointers for the challenges that remain. Additionally, we propose and justify the importance of electrocardiograms (ECGs) and the arterial carbon dioxide partial pressure (PaCO2) as two crucial physiological parameters that have not been used so far to any great extent in the monitoring of COPD patients. We cover four possibilities for the remote monitoring of COPD patients: continuous monitoring during normal daily activities for the prediction and early detection of exacerbations and life-threatening events, monitoring during the home treatment of mild exacerbations, monitoring oxygen therapy applications, and monitoring exercise. We also present and discuss the current approaches to decision support at remote locations and list the normal and pathological values/ranges for all the relevant physiological parameters. The paper concludes with our insights into the future developments and remaining challenges for improvements to continuous remote monitoring systems.

Effectively managing risk is essential for fostering innovation in healthcare, especially with advancements like artificial intelligence (AI) and machine learning (ML). These technologies aim to enhance accessibility, efficiency, and equity in healthcare delivery. To assess the practical utility of AI-enabled remote patient monitoring (RPM) devices, it is crucial to identify and evaluate associated risks while distinguishing between acceptable risk, which society tolerates, and optimal risk, which balances risk reduction costs with benefits. This paper outlines how policymakers should adopt the framework of optimal risk to ensure patient safety while maximizing the advantages of these technologies.

Wearable technologies represent a strong development axis for various medical applications and these devices are increasingly used in daily life as illustrated by smart watches’ popularisation. Combined with new data processing methods, it constitutes a promising opportunity for telemonitoring, triage in mass casualty situations, or early diagnosis after a traffic or sport accident. An approach to processing the physiological data is to develop severity scoring systems to quantify the critical level of an individual’s health status. However, the existing severity scores require a human evaluation. A first version of a severity scoring system adapted to continuous and real-time wearable monitoring is proposed in this article. The focus is made on three physiological parameters straightforwardly measurable with wrist-wearables: heart rate, respiratory rate, and SpO 2 , which may be enough to characterise continuously hemodynamic and respiratory status. Intermediate score functions corresponding to each physiological parameter have been established using a sigmoid model. The boundary conditions have been defined based on a survey conducted among 54 health professionals. An adapted function has also been developed to merge the three intermediate scores into a global score. The scores are associated with a triage tricolour code: green for a low-priority casualty, orange for a delayable one, red for an urgent one. Preliminary confrontation of the new severity scoring system with real data has been carried out using a database of 84 subjects admitted to the intensive care unit. Colour classification by the new scoring system was compared with independent physicians’ direct evaluation as a reference. The prediction success rate values 74% over the entire database. Two examples of continuous monitoring over time are also given. The new score has turned out to be consistent, and may be easily upgraded with the integration of additional vital signs monitoring or medical information.

IntroductionHypertension affects 5–10% of pregnancies in the United States. Chronic hypertension during pregnancy can have a significant impact on maternal and neonatal outcomes, especially in rural populations. Pregnancies complicated by hypertension are currently managed through frequent clinic visits or extended hospital stays. Cellular-enabled remote patient monitoring devices provide an alternative treatment method for women in rural areas.Research AimThis study aimed to measure the feasibility of and patient satisfaction with using an integrated model of cellular-enabled remote patient monitoring devices for blood pressure supported by a 24/7 nurse call center.MethodsIn a mixed methods pilot study, twelve women with chronic hypertension during pregnancy were given cellular-enabled BodyTrace™ blood pressure cuffs and weight scales. Participants’ blood pressures were continuously monitored by a nurse call center. Participants completed a survey and a brief semi-structured interview after two weeks.ResultsParticipants scored low on stress and anxiety with mean scores of 5.45 (SD = 3.56) and 8.09 (SD 3.62), respectively. Participants scored high on behavioral intention, system usability, and perceived benefits with mean scores of 8.73 (SD = 2.53), 75.91 (SD = 23.70), and 19.64 (SD = 5.92), respectively. Participants perceived benefits to using the device, including increased monitoring by health professionals, increased self-awareness, decreased number of clinic visits, and convenience of use. Perceived disadvantages included higher readings when compared to clinical readings.DiscussionCellular-enabled remote patient monitoring devices for blood pressure are a valuable tool for managing treatment of pregnancies complicated by hypertension.

Background: Heart failure (HF) is a prevalent chronic disease and is associated with increases in mortality and morbidity. HF is a leading cause of hospitalizations and readmissions in the United States. A potentially promising area for preventing HF readmissions is continuous remote patient monitoring (CRPM). Objective: The primary aim of this study is to determine the feasibility and preliminary efficacy of a CRPM solution in patients with HF at NorthShore University HealthSystem. Methods: This study is a feasibility study and uses a wearable biosensor to continuously remotely monitor patients with HF for 30 days after discharge. Eligible patients admitted with an HF exacerbation at NorthShore University HealthSystem are being recruited, and the wearable biosensor is placed before discharge. The biosensor collects physiological ambulatory data, which are analyzed for signs of patient deterioration. Participants are also completing a daily survey through a dedicated study smartphone. If prespecified criteria from the physiological data and survey results are met, a notification is triggered, and a predetermined electronic health record–based pathway of telephonic management is completed. In phase 1, which has already been completed, 5 patients were enrolled and monitored for 30 days after discharge. The results of phase 1 were analyzed, and modifications to the program were made to optimize it. After analysis of the phase 1 results, 15 patients are being enrolled for phase 2, which is a calibration and testing period to enable further adjustments to be made. After phase 2, we will enroll 45 patients for phase 3. The combined results of phases 1, 2, and 3 will be analyzed to determine the feasibility of a CRPM program in patients with HF. Semistructured interviews are being conducted with key stakeholders, including patients, and these results will be analyzed using the affective adaptation of the technology acceptance model. Results: During phase 1, of the 5 patients, 2 (40%) were readmitted during the study period. The study completion rate for phase 1 was 80% (4/5), and the study attrition rate was 20% (1/5). There were 57 protocol deviations out of 150 patient days in phase 1 of the study. The results of phase 1 were analyzed, and the study protocol was adjusted to optimize it for phases 2 and 3. Phase 2 and phase 3 results will be available by the end of 2022. Conclusions: A CRPM program may offer a low-risk solution to improve care of patients with HF after hospital discharge and may help to decrease readmission of patients with HF to the hospital. This protocol may also lay the groundwork for the use of CRPM solutions in other groups of patients considered to be at high risk. International Registered Report Identifier (IRRID): DERR1-10.2196/36741

During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate.

As Internet of Things (IoT) devices and other remote patient monitoring systems increase in popularity, security concerns about the transfer and logging of data transactions arise. In order to handle the protected health information (PHI) generated by these devices, we propose utilizing blockchain-based smart contracts to facilitate secure analysis and management of medical sensors. Using a private blockchain based on the Ethereum protocol, we created a system where the sensors communicate with a smart device that calls smart contracts and writes records of all events on the blockchain. This smart contract system would support real-time patient monitoring and medical interventions by sending notifications to patients and medical professionals, while also maintaining a secure record of who has initiated these activities. This would resolve many security vulnerabilities associated with remote patient monitoring and automate the delivery of notifications to all involved parties in a HIPAA compliant manner.

Remote physiologic monitoring (RPM) and remote therapeutic monitoring (RTM) are growing digital health applications. The use of RPM, focusing on objective physiologic data, has been supported by evidence for managing chronic conditions. RTM, which collects subjective patient-reported data, is newer, but offers great potential in musculoskeletal (MSK) rehabilitation and chronic pain management that has already been recognized clinically and by reimbursement frameworks. However, in comparison with RPM, there is limited evidence directly assessing whether RTM contributes to improved clinical outcomes. This review compares the quality of current evidence for improved clinical outcomes from RTM in MSK and pain-related conditions with RPM in chronic disease management. A systematic search of PubMed, Scopus, and the Cochrane Library was conducted through March 2025 using keywords related to remote monitoring, digital health, and MSK conditions. Only reviews and meta-analyses were included for RPM, while both primary and review studies were considered for RTM, restricted to MSK or pain-related outcomes. Three reviewers independently screened and read all articles to reduce risk of bias. 22 studies met inclusion criteria (9 RPM, 13 RTM). RPM reviews consistently demonstrated clinical benefits, including reduced blood pressure, HbA1c, and hospitalizations. Across RTM studies, feasibility, patient satisfaction, and engagement were consistently high, although studies were heterogeneous, with some yielding improved pain and activity levels, while others found no difference relative to usual care. RTM consistently demonstrates strong feasibility and patient engagement in MSK rehabilitation and chronic pain management, though evidence for clinical superiority compared to standard care remains limited. Future studies should emphasize larger randomized trials with standardized functional outcomes, therapy adherence, and integration into rehabilitation and pain management workflows.

Healthcare is a field that is rapidly developing in technology and services. A recent development in this area is remote monitoring of patients which has many advantages in a fast aging world population with increasing health complications. With relatively simple applications to monitor patients inside hospital rooms, the technology has developed to the extent that the patient can be allowed normal daily activities at home while still being monitored with the use of modern communication and sensor technologies. Sensors for monitoring essential vital signs such as electrocardiogram reading, heart rate, respiration rate, blood pressure, temperature, blood glucose levels and neural system activity are available today. Range of remote healthcare varies from monitoring chronically ill patients, elders, premature children to victims of accidents. These new technologies can monitor patients based on the illness or based on the situation. The technology varies from sensors attached to body to ambient sensors attached to the environment and new breakthroughs show contactless monitoring which requires only the patient to be present within a few meters from the sensor. Fall detection systems and applications to monitor chronical ill patients have already become familiar to many. This study provides a review of the recent advances in remote healthcare and monitoring in both with-contact and contactless methods. With the review, the authors discuss some issues available in most systems. The paper also includes some directions for future research.