Patient Monitoring System Research Articles

Evaluation of the Design of Hemodialysis Patient Monitoring System at Lestari Kidney and Hypertension Clinic Semarang

Introduction: Patients diagnosed with kidney disease are very difficult to accept and are not ready to undergo haemodialysis treatment. Objective: Effectiveness is needed to make patients with chronic kidney disease more obedient to treatment and to ask about kidney treatment and important health information for kidney failure patients. Method: The aim of this research is to evaluate the design of a patient's haemodialysis monitoring system in the Gingival Clinic and the Pressure of Current Lester, by conducting research based on methods and qualitative descriptive types of research. Result: Research has shown that Clinical Kidney and Concurrent Lestari Hypertension require a haemodialysis patient monitoring system to facilitate healthcare in patient monitoring and facilitate physicians in decision making.

Safety of electrophysiological study in atrioventricular block risk stratification in new-onset persistent left bundle brunch block following transcathether aortic valve implantation

Abstract Background A significant percentage of patients develops a persistent complete left bundle branch block (LBBB) following TAVI. Electrophysiological (EP) study could be useful to stratify the risk of atrioventricular block (AVB) in these patients, guiding the need of follow-up and the indication of definite pacemaker. Purpose The primary objective of the study was describing the incidence of bradyarrhythmic events in the 30-day follow-up of patients with new-onset LBBB: AV block with ECG long-term monitoring system (Nuubo) in patients with negative EP study, and ≥ 5 % of ventricular stimulation in patients with positive EP study and pacemaker implantation. Methodology Observational prospective study of consecutive patients without previous permanent pacemaker implantation where transfemoral TAVI was implanted between 2021 and 2023. In case new-onset complete LBBB was present after TAVI, an EP study was performed. EP study was considered positive when His-Ventricule interval was more than 70ms, or after procainamide infusion at 10mg/kg, more than 100ms or more than double the basal HV interval. In case EP was positive, a permanent pacemaker was implanted. In case EP was negative, a 30-day follow-up with ECG long-term monitoring system (Nuubo) was performed. Results In this cohort, 266 patients without pacemaker following TAVI were included. Persistent new-onset left bundle branch block was present in 10,15% of patients (n=27). EP study was positive in 29.62% of patients (n=8), following a definite pacemaker implantation. During follow-up, only one patient (12.5%) was dependent of ventricular stimulation. The group with negative EP study (n=19) were monitored with a 30-day ECG long-term monitoring system (Nuubo). No bradyarrhythmic events were registered in this group. Conclusions Approximately one tenth of patients develop persistent new-onset LBBB after TAVI. Events were seldom in both study groups. Positive EP study patients were dependent of ventricular stimulation in 12.5% of cases, and there was no bradyarrhythmic event registered in EP negative group. EP study could be a safe tool to stratify AVB risk in new-onset LBBB following TAVI.

ACGAN for Addressing the Security Challenges in IoT-Based Healthcare System

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people’s lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results.

Smart Hospital Patient Monitoring System Aided by Edge-Fog-Cloud Continuum: A Performability Evaluation Focusing on Distinct Sensor Sources

Smart Hospital Patient Monitoring System Aided by Edge-Fog-Cloud Continuum: A Performability Evaluation Focusing on Distinct Sensor Sources

Remote Healthcare: Development of a Centralized Patient Monitoring System for Ward Patients.

Patient monitoring is an essential practice in healthcare settings to assess well-being and medical conditions. This study proposes a central patient monitoring system enabling healthcare personnel to have a comprehensive perspective of multiple patients' health state from a central location. The proposed prototype enables healthcare providers to detect and respond to changes in patients' condition efficiently. The project contains multiple patient monitoring units with each measuring temperature, ECG, SpO2, and heart rate along with a display on a centralized screen. The hardware of monitors is placed at each patient bed connected with all the patients and the result of each patient can be seen from the general ward counter. Different sensors are used to design monitoring units and are connected with node MCU through different pin connections. Node MCU sends the information gathered from sensors to the Raspberry pi unit using Wi-Fi. The real-time data of patients are being displayed and saved using server “thing speak”. There are different options on the server window to switch between different patients’ real time results to monitor them. The proposed prototype uses computer technology to monitor the vital signs of multiple patients on a single monitor from a centralized location using internet which may eventually reduce the time and workload of medical staff leading to improved patient care.

Actual problems of remote patient monitoring

Remote patient monitoring (RPM) is an innovative technology that provides continuous monitoring of patients' health from a distance. The RPM system is versatile because it can be used in the treatment of acute and chronic conditions, helping doctors keep their patients under control between ward rounds or the absence of personal care. However, the implementation of this system is accompanied by numerous problems: from technical to hospital staff management problems. The object of this study is the technical challenges associated with the use of the RPM system. The problem lies in the imperfection of many elements of RPM, including: data management, sensor accuracy, compatibility and user acceptance of the technology. The research found that these technical challenges arise from insufficient standardization, poor sensor data quality, and interoperability issues between medical platforms. These results are explained by the complexity of the technological infrastructure and the need to improve the qualifications of medical workers. The main goal of the current article is to solve the scientific and technical problem of RPM through the proposal of an effective practical development - an automated system for remote monitoring of patients. Since most processes are moving towards their automation, by using the latest scientific developments, among which we can recall machine learning and neural networks and Internet of Things (IoT) technology, it was decided to consider the factors of their application in remote patient monitoring systems. The results of this study can be used to optimize RPM systems in hospitals, as well as to improve the reliability and efficiency of new automated monitoring systems, which is extremely important in the context of war, which causes a shortage of medical personnel and an increase in the number of potential patients per doctor.

The effect and implementation of the COVID Box, a remote patient monitoring system for patients with a COVID-19 infection in primary care: a matched cohort study

The effect and implementation of the COVID Box, a remote patient monitoring system for patients with a COVID-19 infection in primary care: a matched cohort study

A Remote Patient Monitoring System with Feedback Mechanisms using a Smartwatch: Concept, Implementation and Evaluation based on the activeDCM Randomized Controlled Trial.

Technological advances allow recording and sharing of health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable a dense management of individual health trajectories, monitoring of risk factors and asynchronous feedback. However, most Remote Patient Monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack the patient-centric design. The objective was to conceptualize and implement a user-friendly, reusable, interoperable and secure RPM system incorporating asynchronous feedback mechanisms, using a broadly available consumer wearable (Apple Watch). Additionally, the study sought to evaluate factors influencing patient acceptance of such systems. The RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach, ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was employed as part of the activeDCM prospective, randomized, interventional study focusing on Dilated Cardiomyopathy (DCM). Each patient used the system for at least 12 months. The System Usability Scale (SUS) was employed to measure usability from a subjective patient perspective. Additionally, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data, classified into Sensor-based Health Data (SHD) and Patient Reported Outcome Measures (PROM). Descriptive statistics using boxplots, along bootstrapped multiple linear regression with a 95% confidence interval (CI) were utilized for evaluation, analyzing the influence of age, sex, device experience and intervention group membership. The RPM system consists of four interoperable components: patient-devices, data-server, data-viewer and notification-service. The evaluation of the system was conducted with 95 consecutive DCM patients (female: 28 of 95 (29%), age: 50±12 years) completing the activeDCM study protocol. The wearable/ smartphone application of the system achieved a mean SUS score of 78±17, which was most influenced by device experience. 83 of 95 patients (87%) could integrate the wearable application (very) well into their daily routine and 67 of 95 (70%) saw a benefit of the RPM system for management of their health condition. Patients interacted on average with the wearable on 61%±26% of days enrolled in the study, corresponding to 239±99 of 396±39 days. SHD was available on average for 78%±23% of days and PROM data 64%±27% of weeks enrolled in the study, corresponding to 307±87 of 396±39 days and 35±15 of 56±5 weeks, respectively. Wearable interaction frequency, SHD and PROM completeness were most influenced by intervention group membership. Our results mark a first step towards integrating RPM systems, based on a consumer wearable device for primary patient input, into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable and secure RPM system, that can be integrated into patients' daily routines. ClinicalTrials.gov-Identifier: NCT04359238.

Alarm Management in Provisional COVID-19 Intensive Care Units: Retrospective Analysis and Recommendations for Future Pandemics.

In response to the high patient admission rates during the COVID-19 pandemic, provisional intensive care units (ICUs) were set up, equipped with temporary monitoring and alarm systems. We sought to find out whether the provisional ICU setting led to a higher alarm burden and more staff with alarm fatigue. We aimed to compare alarm situations between provisional COVID-19 ICUs and non-COVID-19 ICUs during the second COVID-19 wave in Berlin, Germany. The study focused on measuring alarms per bed per day, identifying medical devices with higher alarm frequencies in COVID-19 settings, evaluating the median duration of alarms in both types of ICUs, and assessing the level of alarm fatigue experienced by health care staff. Our approach involved a comparative analysis of alarm data from 2 provisional COVID-19 ICUs and 2 standard non-COVID-19 ICUs. Through interviews with medical experts, we formulated hypotheses about potential differences in alarm load, alarm duration, alarm types, and staff alarm fatigue between the 2 ICU types. We analyzed alarm log data from the patient monitoring systems of all 4 ICUs to inferentially assess the differences. In addition, we assessed staff alarm fatigue with a questionnaire, aiming to comprehensively understand the impact of the alarm situation on health care personnel. COVID-19 ICUs had significantly more alarms per bed per day than non-COVID-19 ICUs (P<.001), and the majority of the staff lacked experience with the alarm system. The overall median alarm duration was similar in both ICU types. We found no COVID-19-specific alarm patterns. The alarm fatigue questionnaire results suggest that staff in both types of ICUs experienced alarm fatigue. However, physicians and nurses who were working in COVID-19 ICUs reported a significantly higher level of alarm fatigue (P=.04). Staff in COVID-19 ICUs were exposed to a higher alarm load, and the majority lacked experience with alarm management and the alarm system. We recommend training and educating ICU staff in alarm management, emphasizing the importance of alarm management training as part of the preparations for future pandemics. However, the limitations of our study design and the specific pandemic conditions warrant further studies to confirm these findings and to explore effective alarm management strategies in different ICU settings.

A Novel Design and Performance Assessment of a Blockchain-Powered Remote Patient Monitoring System

A Novel Design and Performance Assessment of a Blockchain-Powered Remote Patient Monitoring System

The Use of AI in Enhancing Patient Safety

The integration of Artificial Intelligence (AI) into patient monitoring systems has marked a transformative shift in healthcare, offering enhanced capabilities for tracking and analyzing patient health data. This paper investigates the application of AI in patient monitoring systems, emphasizing its role in overcoming challenges related to physiological signal capturing and health status estimation. It provides an overview of how AI methodologies, including deep learning and machine learning, are leveraged to improve real-time monitoring, predictive accuracy, and response time in both hospital and home settings. The paper also addresses the current limitations of AI technologies and the potential for future advancements. Also, by examining various applications and discussing the benefits and constraints of AI in this field, the paper aims to highlight the significant impact AI has on improving patient outcomes and optimizing healthcare delivery. Keywords: Artificial Intelligence, Patient Monitoring Systems, Biosignal Analysis, Deep Learning, Machine Learning.

Application of a specialist nurse–led wireless smart monitoring system in elderly patients with persistent atrial fibrillation

Background: With the increase in the elderly population, the prevalence of persistent atrial fibrillation (AF) among older adults has significantly risen, becoming one of the crucial cardiovascular diseases affecting the health and quality of life of the elderly. Patients with persistent AF require continuous cardiac function monitoring to prevent possible complications such as stroke and heart failure. Traditional monitoring methods often necessitate frequent hospital visits, increasing the economic burden and psychological stress on patients. With advancements in medical technology, wireless smart monitoring systems have emerged as a new remote health care technology, offering a more convenient and effective monitoring option for patients with persistent AF. Specialist nurses play a pivotal role in monitoring and managing patients using these systems. Their professional knowledge and skills are vital for improving monitoring efficiency and patient compliance. Therefore, this study aims to explore the application of wireless smart monitoring systems led by specialist nurses in elderly patients with persistent AF, in hopes of providing a scientific basis for improving the monitoring and management of this patient group. Methods: A total of 88 elderly patients with persistent atrial fibrillation admitted to the Cardiology Ward of a tertiary hospital in Weifang City from October 2021 to April 2023 were selected as research subjects. They were divided into an intervention group and a control group with conventional care, with 44 cases in each group, according to the order of admission. The control group received conventional hospitalization guidance, while the intervention group utilized a wireless smart monitoring system led by specialist nurses. The effects before and after the intervention in both groups were compared. Results: The intervention group patients had better scores in terms of mental stress, sleep quality, and comfort compared with the control group, and the nursing shift change time during hospitalization for the intervention group was shorter than that of the control group (P&lt;0.05). Conclusions: The wireless smart monitoring system led by specialist nurses can improve the mental stress and sleep conditions of elderly patients with persistent atrial fibrillation, increase comfort, and enhance the efficiency of nursing shift changes.

Economic impact of a vision-based patient monitoring system across five NHS mental health trusts.

A vision-based patient monitoring system (VBPMS), Oxevision, has been introduced in approximately half of National Health Service (NHS) mental health trusts in England. A VBPMS is an assistive tool that supports patient safety by enabling non-contact physiological and physical monitoring. The system aims to help staff deliver safer, higher-quality and more efficient care. This paper summarises the potential health economic impact of using a VBPMS to support clinical practice in two inpatient settings: acute mental health and older adult mental health services. The economic model used a cost calculator approach to evaluate the potential impact of introducing a VBPMS into clinical practice, compared with clinical practice without a VBPMS. The analysis captured the cost differences in night-time observations, one-to-one continuous observations, self-harm incidents, and bedroom falls at night, including those resulting in A&E visits and emergency service callouts. The analysis is based on before and after studies conducted at five mental health NHS trusts, including acute mental health and older adult mental health services. Our findings indicate that the use of a VBPMS results in more efficient night-time observations and reductions in one-to-one observations, self-harm incidents, bedroom falls at night, and A&E visits and emergency service callouts from night-time falls. Substantial staff time in acute mental health and older adult mental health services is spent performing night-time observations, one-to-one observations, and managing incidents. The use of a VBPMS could lead to cost savings and a positive return on investment for NHS mental health trusts. The results do not incorporate all of the potential benefits associated with the use of a VBPMS, such as reductions in medication and length of hospital stay, plus the potential to avoid adverse events which would otherwise have a detrimental impact on a patient's quality of life.

Design of an Improved Method for P-cycle Protection and Dynamic Bandwidth Allocation in Elastic Optical Networks for Healthcare Applications

The acceptance of a modern-day health care system with telemedicine and remote patient monitoring requires extremely reliable and flexible network infrastructures. Thus, the need for very changeable Elastic Optical Networks, with dynamic bandwidth allocation for extremely diversified health care-oriented information, such as real-time video consultation, continuous patient monitoring, and huge medical imaging files. However, the current network infrastructures struggle very often with the optimization of reliability against resource utilization, particularly for unanticipated failures, which will bring service disruption to the healthcare services and damage patient outcomes. To address these limitations, this paper proposes an advanced network protection model using P-cycle protection in EONs to enhance network resilience by minimizing resource consumption. P-cycles refer to backup loops that are preconfigured such that, upon failure of a link, the traffic could be rerouted instantaneously. Unlike the conventionally used protection schemes, the restoration of P-cycles can occur in much faster duration with less resource loss. In this model, we propose an intelligent adaptation of P-cycles consistent with the dynamic bandwidth requirements of healthcare applications. It does manage to achieve these functionalities through the inclusion of algorithms for traffic-aware P-cycle design to optimize the capabilities of the utilized network for recovery through the least wastage of resources. There are huge implications of P-cycle protection introduced in EONs for healthcare networks. It further increases the reliability of systems for real-time patient monitoring by ensuring non-stop data transmission to decrease system latency in the event of a loss of packets caused by a network failure. The principal beneficiaries of telemedicine are uninterrupted services for high-quality video consultation as well as reliable, massive transmission of medical data in the form of MRI and CT images. This is because of the positive results that are beginning to come from health care, which is becoming more reliable and expedient, especially in rural and critical scenarios.

Integrated health monitoring system for checking patients’ vitals

The increasing demand for efficient healthcare services has propelled the development of efficient health monitoring systems. This paper presents a comprehensive system designed to monitor the vital signs of patients in real-time, aimed at enhancing patient care and reducing the burden on healthcare providers. The system integrates various sensors to Data collected from these sensors are transmitted wirelessly to a central database, where it is processed and analyses using sophisticated algorithms. The results are accessible through a user-friendly interface for both healthcare professionals and patients. This health monitoring system offers several critical benefits. it provides real-time alerts for any abnormal vital signs, enabling prompt medical intervention. Secondly, the continuous monitoring allows for the early detection of potential health issues, which can significantly improve patient outcomes. The methodology involves is the analytical and systematic application of Arduino Nano Boards as microcontrollers, such as DS18B20, DHT11 and with WIFI Modula; which are driven- real time-based temperature frameworks for optimization and abstraction of accurate patient’s health data result. Results demonstrated the system's accuracy, reliability, and user satisfaction.

CLINICAL SIGNIFICANCE OF FLASH MONITORING GLYCEMIA INDICATORS IN CHILDREN WITH TYPE 1 DIABETES MELLITUS

One of the basic principles of the treatment of type 1 diabetes mellitus, along with insulin therapy and diet therapy, is regular self-monitoring of glycemia. Glucose meters are most often used for daily self-monitoring, and in recent years devices for continuous and flash monitoring of glycemia have been used. The article discusses the clinical aspects of the application of a glycemic monitoring system with periodic scanning (flash monitoring). The principle of operation of the system is described, as well as the main parameters obtained when using this system – time in the target range, time above and below the target range and their sub-ranges, an indicator of glycemic variability and an indicator of glycemic control, their normative levels, as well as their practical significance and relationship with the clinical aspects of the course of diabetes mellitus and the prognosis of chronic complications. The results of major international and domestic studies on the effectiveness of the flash monitoring system in different age groups are presented. The advantages of the second generation of flash glycemia monitoring sensors are described, and the results of clinical studies of the accuracy of this monitoring system in both children and adult patients with diabetes mellitus are presented.

Challenges associated with follow-up care after implementation of an HPV screen-and-treat program with ablative therapy for cervical cancer prevention

BackgroundCervical cancer is a preventable cancer; however, decreasing its prevalence requires early detection and treatment strategies that reduce rates of loss to follow-up. This study explores factors associated with loss to follow-up among HPV-positive women after implementation of a new HPV-based screen-and-treat approach for cervical cancer prevention in Iquitos, Peru.MethodsWe conducted semi-structured interviews with “obstetras” (i.e., midwives) (n = 15) working in cervical cancer prevention and women (n = 24) who were recorded as lost to follow-up after positive HPV results. We used the Health Care Access Barriers Model to guide analyses. We utilized manifest content analysis to describe barriers to follow-up according to the obstetras and thematic analysis to report themes from the women’s perspectives. We also report the steps and time taken to contact women.ResultsWe found an incomplete and fragmented patient monitoring system. This incomplete system, in conjunction with challenges in contacting some of the women, led to structural barriers for the obstetras when attempting to deliver positive results. Women in this study expressed a desire to receive treatment, however, faced cognitive barriers including a lack of understanding about HPV results and treatment procedures, fear or anxiety about HPV or treatment, and confusion about the follow-up process. Women also reported having important work matters as a barrier and reported frequently using natural medicine. Reported financial barriers were minimal.ConclusionThis study highlights the barriers to follow-up after implementation of a primary-level HPV-based screen-and-treat approach. While some barriers that have previously been associated with loss to follow-up were not as prominently observed in this study (e.g., financial), we emphasize the need for screen-and-treat programs to focus on strategies that can address incomplete registry systems, structural challenges in results delivery, cognitive barriers in understanding results and treatment, and work-related barriers.

Secure Patient Data Monitoring and Efficient Routing Optimization using a Hyperelliptic Curve Cryptography with Fuzzy-based Priority in WBSN

Aims and Background: Wireless Body Sensor Network (WBSN) technology is one of the major research areas in the medical and entertainment industries. A wireless sensor network (WSN) is a dense sensor network that senses environmental conditions, processes, and outgoing data at the sink node. A WBSN develops patient monitoring systems that provide the flexibility and mobility needed to monitor patient health. In data communications, it is difficult to find flexible optical routing paths, switching capabilities, and packet processing in the composition of optical networks. Information-centric networks (ICNs) are a new network model and are different from information- centric models. The priority of the information-centric model is the communication network. Objectives: In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. Methodology: A fuzzy–based Priority Aware Data Sharing (FPADS) method is introduced to schedule the priority data and monitor the transmission length. The child node adjusts the transmission speed of the cluster head with the help of a fuzzy logic controller (FLC). Results: The proposed model estimated the traffic load of the child node and the priority of the different amounts of data to be transmitted. The principle of scheduling data packets to be developed is based on the precedence of the data with the lowest transmit length in the network. Conclusion: The proposed FPADS performance increases in terms of scheduling time utilisation, traffic distribution, and mean delay. Simulations have been done using NS2, and the outcomes have shown that the proposed methodology is efficient and improves the overall QoS of the system.

요양병원 입원환자 환자안전사고 위해정도에 영향을 미치는 요인

Purpose: This study aimed to identify the characteristics of patient safety incidents among patients admitted to long-term care hospitals and the factors affecting the severity of these incidents, and provide essential data for developing strategies to prevent and manage patient safety incidents in long-term care hospitals. Method: The study utilized 7,594 patient safety report data collected from 2017 to 2022, based on Patient Safety Reporting and Learning System. Descriptive statistics and cross-analysis were conducted to identify the characteristics of patient safety incidents among inpatients of long-term care hospitals. Multinomial logistic regression analysis was performed to analyze the related factors. All statistical analyses were carried out using the SPSS statistical program. Results: Factors identified as affecting the severity of patient safety incidents in long-term care hospital inpatients included gender, age, primary diagnosis, the impact of COVID-19, the number of beds, the location of the incident, and the type of incident. Conclusion: As the aging population leads to an increase in patient safety incidents in long-term care hospitals, it is crucial to prevent these incidents and ensure their severity does not escalate. This requires systematic safety education for all healthcare workers and efforts such as implementing patient safety incident prevention programs and monitoring systems within long-term care hospitals.

Deployment and validation of a smart bed architecture for untethered patients with wireless biomonitoring stickers.

Conventional patient monitoring in healthcare has limitations such as delayed identification of deteriorating conditions, disruptions to patient routines, and discomfort due to extensive wiring for bed-bound patients. To address these, we have recently developed an innovative IoT-based healthcare system for real-time wireless patient monitoring. This system includes a flexible epidermal patch that collects vital signs using low power electronics and transmits the data to IoT nodes in hospital beds. The nodes connect to a smart gateway that aggregates the information and interfaces with the hospital information system (HIS), facilitating the exchange of electronic health records (EHR) and enhancing access to patient vital signs for healthcare professionals. Our study validates the proposed smart bed architecture in a clinical setting, assessing its ability to meet healthcare personnel needs, patient comfort, and data transmission reliability. Technical performance assessment involves analyzing key performance indicators for communication across various interfaces, including the wearable device and the smart box, and the link between the gateway and the HIS. Also, a comparative analysis is conducted on data from our architecture and traditional hospital equipment. Usability evaluation involves questionnaires completed by patients and healthcare professionals. Results demonstrate the robustness of the architecture proposed, exhibiting reliable and efficient information flow, while offering significant improvements in patient monitoring over conventional wired methods, including unrestricted mobility and improved comfort to enhance healthcare delivery.