Patient Monitoring System Research Articles

Pilot trial of an electronic patient-reported outcome monitoring system in patients with metastatic breast cancer undergoing chemotherapy.

Electronic patient-reported outcomes monitoring (ePROM) is a useful communication tool for patients and healthcare providers in cancer chemotherapy. In this study, we examined the feasibility of our newly developed ePROM system, which we refer to as "Hibilog". An ePROM app was developed by extracting 18 items from the Patient-Reported Outcome-Common Terminology Criteria for Adverse Events (PRO-CTCAE). Symptom monitoring was conducted every two weeks for patients with metastatic breast cancer undergoing chemotherapy. The primary outcome was the response rate to the ePROM system. The secondary outcomes were response time, item missing rate, and distribution of responses for each symptom. A total of 71 cases (mean age 52.6years) were analyzed. Performance status was 0 in 76% of the cases and 1 or higher in 24%. First-line treatment was being administered in 30% of cases, second-line treatment in 17%, and third-line or higher treatment in 53%. The response rate to the ePROM system from registration to week 40 remained high at around 80%, indicating good compliance. The average response time was 5.5min and the missing rate for each item was below 0.4%. Among 1,093 responses, the top 3 symptoms causing interference with daily life were Fatigue (63%), Numbness and tingling (48%), and General pain (46%). Our developed ePROM system was able to capture symptoms accurately in patients with metastatic breast cancer undergoing chemotherapy while maintaining a high response compliance.

Privacy-preserving patient monitoring in healthcare IoT using attribute-based cryptography

Internet of Things (IoT) technology has revolutionized patient monitoring systems by providing real-time health data for better medical care. However, the rise of IoT devices raises privacy concerns about sensitive health data. This study uses Attribute-based Cryptography (ABC) in patient monitoring to address these concerns. The focus is on adapting attribute detail to IoT device features. Conventional encryption methods often struggle to adapt to IoT devices limited resources, which could compromise patient data privacy. This study suggests a tweak to ABC by adjusting the attribute granularity to better fit the limitations of IoT devices. These resource constraints present challenges, but the methodology proposes an innovative approach to handling and analyzing attributes while protecting patient confidentiality. A dynamic attribute granularity(DAG) model that adapts to IoT device capabilities ensures a data privacy-system performance trade-off in the study. The suggested approach optimizes attribute number and complexity to improve privacy-preserving patient monitoring system scalability and performance. The research thoroughly evaluates the modified ABC-DAG systems data privacy, scalability, and energy efficiency. The results show that the system can protect patient data in a healthcare IoT setting while reducing computational load on low-resource devices. The growing field of privacy-preserving healthcare Internet of Things benefits from this research. It addresses IoT attribute granularity challenges with a customized cryptographic method. The results improve ABC theory in healthcare and provide practical advice for secure and efficient IoT patient monitoring systems.

Wireless Patient Monitoring System Based on Smart Wristbands and Central user Interface Software.

In this article, a patient monitoring system is proposed that is able to obtain heart rate and oxygen saturation (SpO2) levels of patients, identify abnormal conditions, and inform emergency status to the nurses. The proposed monitoring system consists of smart patient wristbands, smart nurse wristbands, central monitoring user interface (UI) software, and a wireless communication network. In the proposed monitoring system, a unique smart wristband is dedicated to each of the patients and nurses. To measure heart rate and SpO2 level, a pulse oximeter sensor is used in the patient wristbands. The output of this sensor is transferred to the wristband's microcontroller where heart rate and SpO2 are calculated through advanced signal processing algorithms. Then, the calculated values are transmitted to central UI software through a wireless network. In the UI software, received values are compared with their normal values and a predefined message is sent to the nurses' wristband if an abnormal condition is identified. Whenever this message is received by a nurse's wristband, an acoustic alarm with vibration is generated to inform an emergency status to the nurse. By doing so, health services are delivered to the patients more quickly and as a result, the probability of the patient recovery is increased effectively.

Exploring the Initial Encounter with Continuous Glucose Monitoring among Individuals with Type 2 Diabetes: A Two-Week Trip.

This study aimed to explore the inaugural experience of using a continuous glucose monitoring (CGM) system in patients with type 2 diabetes. This study employed a qualitative design. Thirty-one patients with type 2 diabetes were recruited from a national university hospital and underwent CGM for two weeks. Individual interviews with 28 participants were conducted between August 1 and October 17, 2022, after the CGM. Thematic analysis was used to examine the data. The results revealed transformative shifts in aspects of participants' lives due to CGM use, including alterations in dietary management and interpersonal relationships. During the two-week journey with CGM, participants were able to visually observe exercise effects and other benefits, leading to the discovery of a new utility for this innovative medical device. However, unavoidable drawbacks such as high cost, inaccurate results, and skin irritation have been identified, prompting suggestions for improvement. This study determined that CGM is both feasible and valuable for facilitating lifestyle adjustments to manage diabetes. Nevertheless, the challenge of discomfort associated with CGM use should be addressed in the future. To ensure effective utilization and overcome potential obstacles, it is recommended that a comprehensive and user-friendly CGM education manual be created, with the scope of CGM insurance coverage extended to include this research in the future.

Enhancing postoperative anticoagulation therapy with remote patient monitoring: A pilot crossover trial study to evaluate portable coagulometers and chatbots in cardiac surgery follow-up.

Prior research has not assessed the value of remote patient monitoring (RPM) systems for patients undergoing anticoagulation therapy after cardiac surgery. This study aims to assess whether the clinical follow-up through RPM yields comparable outcomes with the standard protocol. A crossover trial assigned participants to SOC-RPM or RPM-SOC, starting with the standard of care (SOC) for the first 6 months after surgery and using RPM for the following 6 months, or vice-versa, respectively. During RPM, patients used the Coaguchek© to accurately measure International Normalized Ratio values and a mobile text-based chatbot to report PROs and adjust the therapeutic dosage. The study assessed patients' and clinicians' experience with RPM and compared direct costs. Twenty-seven patients participated. The median time in therapeutic range (TTR) levels during RPM were 72.2% and 50.6% for the SOC-RPM and RPM-SOC arms, respectively, and during SOC, they were 49.4% and 58.4% for SOC-RPM and RPM-SOC arms, respectively. Patients and the clinical team reported high trust and satisfaction with the proposed digital service. Statistically significant differences were only found in the cost of RPM in the RPM-SOC, which was higher than SOC in the SOC-RPM arm. Portable coagulometers and chatbots can enhance the remote management of patients undergoing anticoagulation therapy, improving patient experience. This presents a promising alternative to the current standard procedure. The results of this study seem to suggest that RPM may have a higher value when initiated after a SOC period rather than starting RPM immediately after surgery.Trial registration: ClinicalTrials.gov NCT06423521.

Telemedicine in the age of the pandemics: The prospects of web-based remote patient monitoring systems for orthopaedic ambulatory care management in the developing economies.

The goal of this research was to demonstrate the efficacy of telemedicine via design, implementation and evaluation of a web-based remote patient monitoring system (WB-RPMS) across the tertiary/university teaching hospitals in a developing country Nigeria, as a tool to continue to expand access to an affordable and resilient tertiary healthcare system through the challenging times of the COVID-19 pandemic or any future disruptions. This research employed an agile and human-centred design thinking philosophy, which saw the researchers iteratively collaborate with clinicians across the system development value chain. It also employed qualitative and quantitative research methods for new system evaluations. After the system's development, a 20-patient sample was randomly selected from members of the National Youth Service Corp to evaluate the WB-RPMS Patient Portal for usability and user experience through a survey based on the system usability scale. Again, the COREQ standards for reporting research result were adopted for this study. The evaluation of the WB-RPMS Patient Portal by a select patient sample showed that 95.0% of the respondents believed that they would like to use the system frequently. It was also discovered that 90.0% of all respondents also indicated that they found the Patient Portal to be simple; 85.0% of the respondents believed and indicated that the WB-RPMS Patient Portal was easy to use. The result of the usability evaluation of the developed WB-RPMS Patient Portal showed that it was well received by the select patient sample and by the clinicians who participated in the development process. In fact, the performance of the system shows that it has the potential to remotely support and sustain improved access to affordable healthcare for outpatients in developing countries even during times of uncertainties and disruptions as recently occasioned by COVID-19 pandemic.

Patient Monitoring System Based on Internet of Things: A Review and Related Challenges With Open Research Issues

Patient Monitoring System Based on Internet of Things: A Review and Related Challenges With Open Research Issues

Artificial Intelligence-Based Patient Monitoring System for Medical Support.

In this paper, we present the development of a monitoring system designed to aid in the management and prevention of conditions related to urination. The system features an artificial intelligence (AI)-based recognition technology that automatically records a user's urination activity. Additionally, we developed a technology that analyzes movements to prevent neurogenic bladder. Our approach included the creation of AI-based recognition technology that automatically logs users' urination activities, as well as the development of technology that analyzes movements to prevent neurogenic bladder. Initially, we employed a recurrent neural network model for the urination activity recognition technology. For predicting the risk of neurogenic bladder, we utilized convolutional neural network (CNN)-based AI technology. The performance of the proposed system was evaluated using a study population of 30 patients with urinary tract dysfunction, who collected data over a 60-day period. The results demonstrated an average accuracy of 94.2% in recognizing urinary tract activity, thereby confirming the effectiveness of the recognition technology. Furthermore, the motion analysis technology for preventing neurogenic bladder, which also employed CNN-based AI, showed promising results with an average accuracy of 83%. In this study, we developed a urination disease monitoring system aimed at predicting and managing risks for patients with urination issues. The system is designed to support the entire care cycle of a patient by leveraging AI technology that processes various image and signal data. We anticipate that this system will evolve into digital treatment products, ultimately providing therapeutic benefits to patients.

Design a patient monitoring system in health care using cluster-based hierarchical routing for green communication

To achieve a healthcare patient monitoring system, this paper presents a clustering-based hierarchical approach. A group-based controlled structured routing architecture is used in the proposed method to group the various health monitoring sensors. A merged center will select a leader for each cluster and delegate the management responsibilities to that individual. Collecting information, organizing, and communicating with leading platforms are all the responsibilities of the cluster leader. The goal of this rotation is to keep the system under a constant load and to optimize the efficiency of the support equipment. A large number of simulations were used to validate our quantitative approach, and we compared our results with other methods already in use. Our study confirms that our approach not only reduces the energy required to maintain the network in various configurations, but also significantly increases their lifetime. The QoD of our proposed method was 4.9 %, compared with the QoD of 7.45 % and 9.82 % for DEEC and LEACH, respectively. Demonstrated that cluster-based hierarchical roads can enable a healthcare patient monitoring system.

Machine learning in healthcare strategic management: a systematic literature review

PurposeThis study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.Design/methodology/approach The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.Findings Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.Research limitations/implications The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.Practical implications The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.Originality/value By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

Rationale and Design of a Wearable Cardiopulmonary Monitoring System for Improving the Efficiency of Critical Care Monitoring

Despite the recent development of wearable cardiopulmonary monitoring devices and their necessity in clinical settings, the evidence regarding their application in real-world intensive care units (ICUs) is limited. These devices have notable problems, such as inefficient manufacturing and cumbersome hardware for medical staff and patients. In this study, we propose a simplified cardiopulmonary monitoring system and present a protocol for a single-center prospective study to evaluate the efficacy of the proposed system compared with those from the conventional monitoring system. The system was designed to continuously measure electrocardiogram, respiration rate, and oxygen saturation in a stand-alone device with an intuitive data visualization platform and automatic data collection. The accuracy of the data measured from the proposed device will be pre-validated by comparing them with those from the reference device. Medical staff from the St. Vincent’s Hospital ICU will complete a five-point Likert-type scale questionnaire regarding their experience with conventional ICU monitoring systems. The result will be compared with the second questionnaire conducted after deploying the system. Since this is a study proposal paper, we do not have any data on this study yet. However, compared with the conventional patient monitoring system, the proposed device should be a promising method to relieve medical staff fatigue and that of the patients who must wear and attach the monitoring device for a long time.

Supporting model-based safety analysis for safety-critical IoT systems

Dependability is regarded as the ability of the system to provide services that can be trusted within a specific period. As the complexity and heterogeneity of Internet of Things (IoT) systems rise, so does the possibility of errors and failure. Early safety analysis not only reduces the cost of late failure but also makes it easier to trace and determine the source of the failure beforehand in case something goes wrong. In this paper, we present an early safety analysis approach based on Failure-Logic Analysis (FLA) and Fault-Tree Analysis (FTA) for safety-critical IoT systems. The safety analysis infrastructure, supported by the CHESSIoT tool, takes into account the system-level physical architecture model annotated with the component’s failure logic properties to perform different kinds of automated failure analyses. In addition to its ability to generate the system Fault-Trees (FTs), the new FTA analysis approach automatically performs qualitative and quantitative analyses which include the elimination of redundant events, unnecessary failure paths, as well as automatic probabilistic calculation of the undesired events. To assess the effectiveness of the approach, a comparative study between our propose approach with 19 existing approaches in both academia and industry was conducted showcasing its contribution to the state of the art. Finally, a Patient Monitoring System (PMS) use case has been developed to demonstrate the capabilities of the supporting CHESSIoT tool, and the results are thoroughly presented.

Real-Time Remote Patient Monitoring and Alarming System for Noncommunicable Lifestyle Diseases.

Telemedicine and remote patient monitoring (RPM) systems have been gaining interest and received adaptation in healthcare sectors since the COVID-19 pandemic due to their efficiency and capability to deliver timely healthcare services while containing COVID-19 transmission. These systems were developed using the latest technology in wireless sensors, medical devices, cloud computing, mobile computing, telecommunications, and machine learning technologies. In this article, a real-time remote patient monitoring system is proposed with an accessible, compact, accurate, and low-cost design. The implemented system is designed to an end-to-end communication interface between medical practitioners and patients. The objective of this study is to provide remote healthcare services to patients who need ongoing care or those who have been discharged from the hospital without affecting their daily routines. The developed monitoring system was then evaluated on 1177 records from MIMIC-III clinical dataset (aged between 19 and 99 years). The performance analysis of the proposed system achieved 88.7% accuracy in generating alerts with logistic regression classification algorithm. This result reflects positively on the quality and robustness of the proposed study. Since the processing time of the proposed system is less than 2 minutes, it can be stated that the system has a high computational speed and is convenient to use in real-time monitoring. Furthermore, the proposed system will fulfil to cover the lower doctor-to-patient ratio by monitoring patients from remote locations and aged people who reside in their residences.

Analysis of the epidemiological situation of community-acquired pneumonia in the Republic of Tatarstan for the period from 2012 to 2021

The incidence of community-acquired pneumonia is an urgent problem of modern healthcare due to its high prevalence, which creates an additional burden on intensively working healthcare systems. An analysis of the epidemiological situation with this nosology over a ten-year period is relevant, since it will allow developing or adjusting measures to reduce the incidence and prevent community-acquired pneumonia in the Republic of Tatarstan. The purpose of this study — to study the dynamics and trends in the incidence of community acquired pneumonia in the population of the Republic of Tatarstan for the period 2012–2021. The study used data from official statistical information of the Office of the Federal Service for Supervision of Consumer Rights Protection and Human Welfare in the Republic of Tatarstan, statistical reference books of the Center for Hygiene and Epidemiology in the Republic of Tatarstan for 2012–2021. The design of the study is a descriptive and evaluative epidemiological study as part of a retrospective epidemiological analysis of the incidence of CAP in the Republic of Tatarstan for 2012–2021. with an analysis of the long-term dynamics of the incidence of the population of the Republic of Tatarstan with the calculation of intensive incidence rates. When assessing the long-term dynamics of the incidence (2012–2021) of community-acquired pneumonia in the Republic of Tatarstan, a trend towards an increase in the incidence was revealed at an average rate of 27.4 ± 0.5 % per year (ρ = 0,748, р < 0,005) (2,3). The indicated trend rate increased the incidence of CAP over 10 years from 451.3 cases per 100 thousand us to 934.3 per 100 thousand us, or 2 times (2.3). The obtained results of assessing the incidence of community-acquired pneumonia over a ten-year period in the Republic of Tatarstan indicate the need for a detailed analysis of the monitoring system for patients with community-acquired pneumonia, as well as the need to improve it to increase the level of preventive measures.

Safety, usability, and performance of a wireless left atrial pressure monitoring system in patients with heart failure: the VECTOR-HF trial (final results)

Abstract Background In heart failure (HF), implantable hemodynamic monitoring devices have been shown to optimize therapy, anticipating clinical decompensation and preventing hospitalization. Direct left-sided hemodynamic sensors offer theoretical benefits beyond pulmonary artery pressure (PAP) monitoring systems. Aims We evaluated the safety, usability, and performance of a novel left atrial pressure (LAP) monitoring system in HF patients (V-LAP). Methods and Results The VECTOR-HF study was a first-in-human, prospective, multicenter, single-arm, clinical trial enrolling 30 patients with HF. The device consisted of an interatrial positioned leadless sensor, able to transmit LAP data wirelessly. After three months, a right heart catheterization (RHC) was performed to correlate mean pulmonary capillary wedge pressure (PCWP) with simultaneous mean LAP obtained from the device. Remote LAP measurements were then used to guide patient management. The miniaturized device was successfully implanted in all 30 patients, without acute Major Adverse Cardiac and Neurological Events (MACNE). At 3 months, freedom from short-term MACNE was 97%. Agreement between sensor-calculated LAP and PCWP was consistent, with a mean difference of -0.22±4.92mmHg, the correlation coefficient and the Lin’s Concordance Correlation Coefficient values were equal to 0.79 (P<0.0001) and 0.776 (95%CI=0.582-0.886), respectively. Preliminary experience with VLAP-based HF management was associated with significant improvements in NYHA functional class (32% of patients reached NYHA II class at 6 months, P<0.005; 60% of patients at 12 months, P<0.005) and 6-minute walk-test distance (from 244.59±119.59m at baseline to 311.78±129.88m after 6 months, P<0.05, and 343.95±146.15m after 12 months, P<0.05). Conclusion The V-LAP™ monitoring system proved to be safe and provided a good correlation with invasive PCWP. Initial evidence also suggests possible improvement in HF clinical symptoms that could be associated to a better outcome. Further studies are needed to test the efficacy of the device in heart failure patients.

Heart failure patients monitoring using IoT-based remote monitoring system

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.

Implementation of a Continuous Patient Monitoring System in the Hospital Setting: A Qualitative Study

Technology can improve care delivery, patient outcomes, and staff satisfaction, but integration into the clinical workflow remains challenging. To contribute to this knowledge area, this study examined the implementation continuum of a contact-free, continuous monitoring system (CFCM) in an inpatient setting. CFCM monitors vital signs and uses the information to alert clinicians of important changes, enabling early detection of patient deterioration. Data were collected throughout the entire implementation continuum at a community teaching hospital. Throughout the study, 3 group and 24 individual interviews and five process observations were conducted. Postimplementation alarm response data were collected. Analysis was conducted using triangulation of information sources and two-coder consensus. Preimplementation perceived barriers were alarm fatigue, questions about accuracy and trust, impact on patient experience, and challenges to the status quo. Stakeholders identified the value of CFCM as preventing deterioration and benefitting patients who are not good candidates for telemetry. Educational materials addressed each barrier and emphasized the shared CFCM values. Mean alarm response times were below the desired target of two minutes. Postimplementation interview analysis themes revealed lessened concerns of alarm fatigue and improved trust in CFCM than anticipated. Postimplementation challenges included insufficient training for secondary users and impact on patient experience. In addition to understanding the preimplementation anticipated barriers to implementation and establishing shared value before implementation, future recommendations include studying strategies for optimal tailoring of education to each user group, identifying and reinforcing positive process changes after implementation, and including patient experience as the overarching element in frameworks for digital tool implementation.

EFFECTIVENESS OF THE THERAPEUTIC COMMUNITY INTERVENTION PROGRAM OF THE DEPARTMENT OF HEALTH-TREATMENT AND REHABILITATION CENTER (DOH-TRC)

This study aimed to assess the effectiveness of the Therapeutic Community Intervention Program operated by the Department of Health-Treatment and Rehabilitation Center (DOH-TRC) in the Philippines. The research focused on four components of the program, namely the Residential, Outpatient, Aftercare, and Recovery Home programs. The study determined the areas of success and those requiring improvement to enhance the overall effectiveness of each program. Structured questionnaires and surveys were administered to two groups of respondents-program staff and patients-who had received the respective intervention programs. The survey questions were designed to evaluate the effectiveness of various aspects of the programs, such as treatment plan individualization, program implementation fidelity, program structure, family involvement, patient support systems, post-treatment monitoring, relapse prevention, and post-recovery support. The statistical analysis revealed significant insights into the effectiveness of each program. The Residential and Recovery Home programs received high mean scores, indicating that staff and patients perceived them as highly effective. However, the Outpatient and Aftercare programs demonstrated areas requiring attention and improvement. The Outpatient Program showed that family support, patient anxiety towards reintegration into the community, and the absence of a program for patient and family interaction were significant concerns. For the Aftercare Program, areas of concern included the lack of a structured patient monitoring system and financial support for staff assigned to monitor aftercare patients. The findings offered valuable insights into areas that needed enhancement to ensure optimal patient outcomes. Based on the data findings, a comprehensive action plan was developed to improve the effectiveness of the Therapeutic Community Intervention Program. The action plan involved specific key performance indicators, activities, budget allocations, timelines, personnel involvement, and continuous quality improvement efforts tailored to each program. The study provided information on the advantages and disadvantages of the Therapeutic Community Intervention Program implemented by DOH-TRC. By implementing the proposed action plan, the center can further improve the effectiveness of its Residential, Outpatient, Aftercare, and Recovery Home programs, ultimately leading to better patient outcomes and contributing to the programs overall success in combating substance abuse and promoting lasting recovery. Continuous evaluation and improvement will be essential to ensure the program remains responsive to the evolving needs of its participants and consistently delivers high-quality care. KEYWORDS: Rehabilitation, Residential Program, Outpatient Program, Aftercare Program, Recovery Home Program

A secure framework for IoT‐based healthcare using blockchain and IPFS

AbstractSmart healthcare, also known as IoT (Internet of Things) based healthcare, utilizes IoT technology to enhance the healthcare industry. The use of IoT‐enabled medical equipment enables remote monitoring of patients, allowing for in‐home care and alerting healthcare providers of any changes. This can lead to improved patient outcomes and better disease management. However, it is important to implement robust security measures to protect patient data when using IoT in healthcare. One potential solution is to use blockchain technology to secure data storage and sharing with medical providers. Blockchain's decentralized structure and cryptographic techniques make it difficult for hackers to access or tamper with patient information. Additionally, IPFS (InterPlanetary file system) can be used for efficient data storage and sharing with authorized medical professionals, while smart contract functionality can automate the process of granting and revoking access to patient data. This article proposes a blockchain‐based secure remote patient monitoring system, specifically for chronic disease patients. The system utilizes distributed blockchain for data security, IPFS for data storage and sharing, and DApp for data collection and connection to the blockchain, along with encryption for added security. The proposed approach is compared to existing solutions and found to be a superior option for IoT‐based healthcare.

Retracted: Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring.

[This retracts the article DOI: 10.1155/2022/8732213.].