Real-time Patient Monitoring Research Articles

Patient and clinician perceptions of the pulse oximeter in a remote monitoring setting for COVID-19: Qualitative study.

As a response to the COVID-19 pandemic, the Royal Prince Alfred Hospital in New South Wales, Australia, launched the rpavirtual program, the first full-scale virtual hospital in Australia, to remotely monitor and follow-up stable COVID-19 patients. As part of the intervention, a pulse oximeter wearable device was delivered to patients to monitor their oxygen saturation levels, a critical indicator of COVID-19 patient deterioration. Understanding users' perceptions towards the device is fundamental to assessing its usability and acceptability and contributing to the effectiveness of the intervention, but no research to date has explored the user experience of the pulse oximeter for remote monitoring in this setting. To explore the utilization, performance, and acceptability of the pulse oximeter by clinicians and patients in rpavirtual during COVID-19. Semi-structured interviews and usability testing were conducted. Stable COVID-19 adult patients (≥18 years old) who used the pulse oximeter and were monitored by rpavirtual and rpavirtual clinicians monitoring these patients were interviewed. Clinicians could be nurses, doctors or staff part of the team who assisted patients with the use of the pulse oximeter. Usability testing was conducted with patients who had the pulse oximeter when they were contacted. Interviews were coded using the Theoretical Framework of Acceptability. Usability testing was conducted using a think-aloud protocol. Data was collected until saturation was reached. Twenty-one patients (average age: 51 years) and 15 clinicians (average age: 41 years) completed the interview. Eight patients (average age: 51 years) completed the usability testing. All participants liked the device and thought it was easy to use. They also had a good understanding of how to use the device and the device's purpose. Patient age and device-use related characteristics (e.g., the warmth of hands, hand steadiness) were identified by users as factors negatively impacting the accurate use of the pulse oximeter. Patients and clinicians had very positive perceptions of the pulse oximeter for COVID-19 remote monitoring, indicating high acceptability and usability of the device. However, factors that may impact the accuracy of the device should be considered when delivering interventions using the pulse oximeter for remote monitoring. Targeted instructions about the use of the device may be necessary for specific populations (e.g. elderly, patients unfamiliar with technology). Further research should focus on the integration of the pulse oximeter data into electronic medical records for real-time and secure patient monitoring.

Enhancing the Security: A Lightweight Authentication and Key Agreement Protocol for Smart Medical Services in the IoHT

The Internet of Things (IoT) has witnessed significant growth with advancements in Internet and wireless technologies. In the medical field, the Internet of Health Things (IoHT) has emerged as an extension of the IoT, enabling the exchange of remote data and real-time monitoring of patients’ health conditions. Through the IoHT, doctors can promptly provide diagnoses and treatment for patients. As patient data are transmitted over public channels, security issues may arise, necessitating security mechanisms. Recently, Amintoosi et al. proposed an authentication protocol for smart medical services in the IoHT. However, their protocol exhibited security weaknesses, including vulnerabilities to privileged insider attacks. To address the security concerns, we propose an enhanced authentication and key agreement protocol. The security of our protocol is rigorously analyzed using the Real-Or-Random model, informal security analysis, and the AVISPA tool. Finally, the results of our analysis demonstrate that our proposed protocol ensures sufficient security while maintaining a performance level similar to existing protocols.

Circulating epithelial cell as viral infection and tissue origin marker in patients with severe COVID-19

Liquid biopsy (LB) is a minimally invasive procedure that detects biomarkers in body fluids for real-time monitoring of patients. This study developed a new LB approach to analyze Circulating Epithelial Cells (CECs) in Intensive Care Unit (ICU) patients with severe COVID-19 and High-Exposure Negative Population to COVID-19 (HENPC) as the control group. The CECs were characterized by multispectral imaging flow cytometry, and an anti-SARS-CoV-2 Spike S1 protein (ProtS) antibody was used to detect infection. The results showed that CECs were present in most ICU patients (p = 0.0412), and their median number was significantly higher (p = 0.0004) than in controls. CEC clusters were only identified in patients, and high positive ProtS expression was observed in CECs from ICU patients compared to negative controls. In conclusion, LB could be a minimally invasive tool for detecting tissue damage caused by infectious agents and could provide real-time biological information about disease status and evolution. However, further validation in a larger population of patients is needed.

Objective wearable measures correlate with self-reported chronic pain levels in people with spinal cord stimulation systems

Spinal Cord Stimulation (SCS) is a well-established therapy for treating chronic pain. However, perceived treatment response to SCS therapy may vary among people with chronic pain due to diverse needs and backgrounds. Patient Reported Outcomes (PROs) from standard survey questions do not provide the full picture of what has happened to a patient since their last visit, and digital PROs require patients to visit an app or otherwise regularly engage with software. This study aims to assess the feasibility of using digital biomarkers collected from wearables during SCS treatment to predict pain and PRO outcomes. Twenty participants with chronic pain were recruited and implanted with SCS. During the six months of the study, activity and physiological metrics were collected and data from 15 participants was used to develop a machine learning pipeline to objectively predict pain levels and categories of PRO measures. The model reached an accuracy of 0.768 ± 0.012 in predicting the pain intensity of mild, moderate, and severe. Feature importance analysis showed that digital biomarkers from the smartwatch such as heart rate, heart rate variability, step count, and stand time can contribute to modeling different aspects of pain. The results of the study suggest that wearable biomarkers can be used to predict therapy outcomes in people with chronic pain, enabling continuous, real-time monitoring of patients during the use of implanted therapies.

Intelligent Digital Twins for Personalized Migraine Care.

Intelligent digital twins closely resemble their real-life counterparts. In health and medical care, they enable the real-time monitoring of patients, whereby large amounts of data can be collected to produce actionable information. These powerful tools are constructed with the aid of artificial intelligence, machine learning, and deep learning; the Internet of Things; and cloud computing to collect a diverse range of digital data (e.g., from digital patient journals, wearable sensors, and digitized monitoring equipment or processes), which can provide information on the health conditions and therapeutic responses of their physical twins. Intelligent digital twins can enable data-driven clinical decision making and advance the realization of personalized care. Migraines are a highly prevalent and complex neurological disorder affecting people of all ages, genders, and geographical locations. It is ranked among the top disabling diseases, with substantial negative personal and societal impacts, but the current treatment strategies are suboptimal. Personalized care for migraines has been suggested to optimize their treatment. The implementation of intelligent digital twins for migraine care can theoretically be beneficial in supporting patient-centric care management. It is also expected that the implementation of intelligent digital twins will reduce costs in the long run and enhance treatment effectiveness. This study briefly reviews the concept of digital twins and the available literature on digital twins for health disorders such as neurological diseases. Based on these, the potential construction and utility of digital twins for migraines will then be presented. The potential and challenges when implementing intelligent digital twins for the future management of migraines are also discussed.

Smart urban planning: Intelligent cognitive analysis of healthcare data in cloud-based IoT

In recent years, there has been a growing recognition among competent city developers and researchers regarding the critical importance of integrating healthcare data analysis into smart urban planning. This research study focuses on enhancing the healthcare system using a cognitive-based cloud framework. We employ the Internet of Things (IoT) to collect patient data and utilize a cloud-based cognitive framework for real-time patient monitoring. We aim to achieve cost-effective and high-quality healthcare. Deep learning techniques are applied to assess the health status and obtain experimental results. Specifically, we conduct pathology detection and classification using Electroencephalography (EEG). Multimodal patient health data, including EEG signals, are recorded using an EEG sensor. Intelligent IoT devices transmit the EEG signals from patients to the cloud, where they undergo processing and are forwarded to a cognitive module. The system tracks various sensor readings, such as facial expressions, speech, EEG, movements, and gestures, to determine the patient's condition. To classify the EEG signals as pathological or normal, our proposed method employs a Transfer Learning-based Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and the Kruskal-Wallis (KW) method. We demonstrate the effectiveness of our approach, outperforming existing methods on the same dataset, achieving an impressive accuracy of 95.13% in identifying EEG pathologies.

Introducere în utilizarea inteligenţei artificiale în pediatrie

Artificial intelligence (AI) is a fascinating field that has cap­tured the attention of scientists and researchers. The de­fi­ni­tions of AI have changed and evolved. The con­tem­po­rary definition focuses on the ability of artificial systems to learn from data and perform specific tasks, such as voice and vi­sual recognition or decision-making based on complex in­for­ma­tion. Artificial intelligence has evolved from rigidly pro­grammed systems to ones that can learn and adapt auto­no­mously. AI has represented a significant evolution in the medical field, bringing fundamental changes in di­sease diagnosis, treatment and management. There are se­veral ways in which it can be used in pediatrics: assisted diag­no­sis and prognosis, designing personalized treatment regi­mens, real-time monitoring of patients, assistance in con­sul­ta­tions and remote care, and medical education and training. Despite all the advantages that AI brings, doctors’ re­luc­tance remains an important obstacle to its adoption. Con­cerns about the ethical and legal aspects of using AI in medical practice may drive this reluctance. Ethical and le­gal issues include patient data privacy, accountability, trans­pa­rency of AI algorithms, and error detection. Clear re­gu­la­tions are needed to address these issues in medical prac­tice. Artificial intelligence should not and will never re­place the experience and expertise of doctors. AI in pe­dia­trics should always complement doctors based on a multidisciplinary approach involving human medical con­sul­ta­tion and decision-making in a wider context.

Artificial Intelligence (AI) and Physiology

Artificial Intelligence (AI) is threatening to pervade all domains and Physiology or Medicine is no exception. There are two ways that we can look at it: (i) AI applications being used to understand and modify physiological functions, and (ii) Understanding (neuro)physiology well to apply the principles for improving algorithms that power AI applications. Across multiple biological systems in human physiology, AI can integrate with traditional diagnostic tests in medicine, and AI can sometimes determine additional findings missed by traditional diagnostic tests. Many intelligent healthcare systems are now being used for automated and real-time patient monitoring. The obvious question is whether automated AI applications will replace human physiologists or not. The answer is that physiologists using AI will replace physiologists not using AI, since computer plus brain is greater than either alone. Also, we need to be concerned about the ethical issues related to the use of AI in physiology and medicine.

Modification of a Conventional Deep Learning Model to Classify Simulated Breathing Patterns: A Step toward Real-Time Monitoring of Patients with Respiratory Infectious Diseases

The emergence of the global coronavirus pandemic in 2019 (COVID-19 disease) created a need for remote methods to detect and continuously monitor patients with infectious respiratory diseases. Many different devices, including thermometers, pulse oximeters, smartwatches, and rings, were proposed to monitor the symptoms of infected individuals at home. However, these consumer-grade devices are typically not capable of automated monitoring during both day and night. This study aims to develop a method to classify and monitor breathing patterns in real-time using tissue hemodynamic responses and a deep convolutional neural network (CNN)-based classification algorithm. Tissue hemodynamic responses at the sternal manubrium were collected in 21 healthy volunteers using a wearable near-infrared spectroscopy (NIRS) device during three different breathing conditions. We developed a deep CNN-based classification algorithm to classify and monitor breathing patterns in real time. The classification method was designed by improving and modifying the pre-activation residual network (Pre-ResNet) previously developed to classify two-dimensional (2D) images. Three different one-dimensional CNN (1D-CNN) classification models based on Pre-ResNet were developed. By using these models, we were able to obtain an average classification accuracy of 88.79% (without Stage 1 (data size reducing convolutional layer)), 90.58% (with 1 × 3 Stage 1), and 91.77% (with 1 × 5 Stage 1).

A Spectrofluorometric Method for Real-Time Graft Assessment and Patient Monitoring.

Biomarkers are powerful clinical diagnostics and predictors of patient outcome. However, robust measurements often require time and expensive laboratory equipment, which is insufficient to track rapid changes and limits direct use in the operating room. Here, this study presents a portable spectrophotometric device for continuous real-time measurements of fluorescent and non-fluorescent biomarkers at the point of care. This study measures the mitochondrial damage biomarker flavin mononucleotide (FMN) in 26 extended criteria human liver grafts undergoing hypothermic oxygenated perfusion to guide clinical graft assessment. Real-time data identified seven organs unsuitable for transplant that are discarded. The remaining grafts are transplanted and FMN values correlated with post-transplant indicators of liver function and patient recovery. Further, this study shows how this device can be used to monitor dialysis patients by measuring creatinine in real-time. Ourapproach provides a simple method to monitor biomarkers directly within biological fluids to improve organ assessment, patient care, and biomarkerdiscovery.

Prognostic impact of baseline circulating extracellular vesicles (BCEVs) in the setting of patients with advanced non-small cell lung cancer (ANSCLC) eligible for first line treatment with osimertinib.

e14553 Background: In the liquid biopsy era, the interrogation of extracellular vesicles (EVs) content might be complementary for cancer prognostication and response assessment. In this explorative study, we aimed to describe whether the serial characterization of BCEVs plasma tumor-derived EVs could longitudinally reflect response and resistance to available first-line treatments, investigating the potential to predict clinical outcomes in patients with advanced NSCLC (aNSCLC). Methods: From 2020/02 to 2022/05, 27 treatment-naïve patients with aNSCLC (stageIIIB/IV), eligible for first-line treatment with an EGFRTKI (Osimertinib) or ICIs (e.g. Pembrolizumab ± platinum‐based chemotherapy) based on the predictive molecular pathology, were enrolled. Blood samples (∼5 mL) were collected into K2EDTA tubes early in the morning with fasting condition. We isolated EVs from 2 ml of plasma using exo-Easy Maxi Kit (Qiagen-USA). EVs’s size and distribution was determine by use of Dynamic Light Scattering method (DLS). Besides a Bradford assay (cfEV) for the indirect quantification of EV amount based on EVs proteins content was performed. According to the radiologic response, EV analyses were evaluated in patients with available plasma samples from baseline (T0) to the first radiologic evaluation of disease within 12 weeks (T1). Results: From 2020/02 to 2022/05, 27treatment-naïve patients with aNSCLC (stageIIIB/IV), eligible for first-line treatment with an EGFRTKI (Osimertinib) or ICIs (e.g. Pembrolizumab ± platinum‐based chemotherapy) based on the predictive molecular pathology, were enrolled. Blood samples (∼5 mL) were collected into K2EDTA tubes early in the morning with fasting condition. We isolated EVs from 2 ml of plasma using exo-Easy Maxi Kit (Qiagen-USA). EVs’s size and distribution was determine by use of Dynamic Light Scattering method (DLS). Besides a Bradford assay (cfEV) for the indirect quantification of EV amount based on EVs proteins content was performed. According to the radiologic response, EV analyses were evaluated in patients with available plasma samples from baseline (T0) to the first radiologic evaluation of disease within 12 weeks (T1). Conclusions: This real-world study confirmed the in vivo feasibility of isolating and characterizing BCEVs via DLS and Bradford assay from the plasma of patients with ANSCLC. The quantitative changes of the plasma EVs correlated with survival in the real-time monitoring of such first-line patients, mostly in the oncogene-addicted population receiving osimertinib.

Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System

With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient’s activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy.

Novel use of an irrigated ablation catheter to monitor real-time hemodynamics during ablation.

Hemodynamic decompensation during catheter ablation occurs due to prolonged procedure time and irrigant delivery directly into the cardiac chambers. Real-time hemodynamic monitoring of patients undergoing catheter ablation procedures may identify patients at risk of decompensation; we set out to assess the feasibility of a novel, real-time, intracardiac pressure monitoring system using a standard irrigated ablation catheter. We studied 13 consecutive who underwent pressure measurement of the left atrium (LA) and left ventricle (LV) via transeptal access with a Swan Ganz (SG) catheter followed by two commercially available irrigated ablation catheters. Pressure waveform data was extracted to compare LA peak pressure, LV peak systolic pressure, LV end-diastolic pressure, and waveform analysis. Comparison between the SG and ablation catheters (AblA; AblB) demonstrated that LV systolic pressure (0.61-16.8 mmHg; 1.32-18.2 mmHg), and LV end-diastolic pressure (-3.4 to 2.8 mmHg; -3.0 to 3.35 mmHg) were well correlated and had accepted repeatability. Ablation waveforms demonstrated an 89.9 ± 6.4% correlation compared to SG waveforms. Pressure measurements derived from an irrigated ablation catheter are accurate and reliable when compared to an SG catheter. Further studies are needed to determine how real-time pressure monitoring can improve outcomes during ablation procedures.

(612) Using the SCAI Classification for Early Identification and Real-Time Monitoring of Cardiogenic Shock Patients

(612) Using the SCAI Classification for Early Identification and Real-Time Monitoring of Cardiogenic Shock Patients

THE POTENTIAL OF TELECOMMUNICATIONS IN CANCER CLINICAL TRIALS: AN OVERVIEW

This review article explores the potential benets and challenges of using telecommunications technology, including telemedicine and mobile health apps, in cancer clinical trials. The author conducted a search of four electronic databases for studies published in English between 2010 and 2021 that evaluated the use of telecommunications technology in adult cancer patients and reported patient outcomes. The studies were grouped based on the type of telecommunications technology used, and the outcomes were synthesized to provide an overview of the potential impact of telecommunications technology on cancer clinical trials. Telecommunications technologies were found to improve patient access and recruitment, data collection, management, and analysis, and enable real-time monitoring of patients' health and adherence to treatment. Telecommunications technologies can also reduce trial costs by minimizing the need for inperson visits and other expenses. However, the implementation of telecommunications technologies in clinical trials is not without challenges, including technical issues, patient acceptance and adherence, regulatory and legal approvals, and variability in healthcare systems. The author suggests that addressing these challenges could lead to improved patient outcomes and better overall health outcomes for cancer patients.

Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction.

Achieving abstinence from drugs is a long journey and can be particularly challenging in the case of methamphetamine, which has a higher relapse rate than other drugs. Therefore, real-time monitoring of patients' physiological conditions before and when cravings arise to reduce the chance of relapse might help to improve clinical outcomes. Conventional treatments, such as behavior therapy and peer support, often cannot provide timely intervention, reducing the efficiency of these therapies. To more effectively treat methamphetamine addiction in real-time, we propose an intelligent closed-loop transcranial magnetic stimulation (TMS) neuromodulation system based on multimodal electroencephalogram-functional near-infrared spectroscopy (EEG-fNIRS) measurements. This review summarizes the essential modules required for a wearable system to treat addiction efficiently. First, the advantages of neuroimaging over conventional techniques such as analysis of sweat, saliva, or urine for addiction detection are discussed. The knowledge to implement wearable, compact, and user-friendly closed-loop systems with EEG and fNIRS are reviewed. The features of EEG and fNIRS signals in patients with methamphetamine use disorder are summarized. EEG biomarkers are categorized into frequency and time domain and topography-related parameters, whereas for fNIRS, hemoglobin concentration variation and functional connectivity of cortices are described. Following this, the applications of two commonly used neuromodulation technologies, transcranial direct current stimulation and TMS, in patients with methamphetamine use disorder are introduced. The challenges of implementing intelligent closed-loop TMS modulation based on multimodal EEG-fNIRS are summarized, followed by a discussion of potential research directions and the promising future of this approach, including potential applications to other substance use disorders.

The internet of things healthcare monitoring system based on MQTT protocol

In overpopulated nations, the need for medical treatment is increasing along with the population; healthcare difficulties are becoming more common. The population's need for high-quality care is growing despite decreasing treatment costs. Because of technological improvements, a machine may remotely monitor health, which is more reliable than manual monitoring. The time required for individualized training may be shortened, and the dependability of complicated equipment may be increased. This study recommends a real-time remote patient monitoring system based on the Internet of Things (IoT) to assure the accuracy of the vital real-time signal. The vital real-time signal is sent from the proposed method to the website using the Message Queuing Telemetry Transport (MQTT) protocol. This work aims to read and analyze patients’ vital signs and reduce the latency while transmitting the signals.

Permutation Entropy Analysis to Intracranial Hypertension from a Porcine Model

Intracranial pressure (ICP) monitoring is commonly used in the follow-up of patients in intensive care units, but only a small part of the information available in the ICP time series is exploited. One of the most important features to guide patient follow-up and treatment is intracranial compliance. We propose using permutation entropy (PE) as a method to extract non-obvious information from the ICP curve. We analyzed the results of a pig experiment with sliding windows of 3600 samples and 1000 displacement samples, and estimated their respective PEs, their associated probability distributions, and the number of missing patterns (NMP). We observed that the behavior of PE is inverse to that of ICP, in addition to the fact that NMP appears as a surrogate for intracranial compliance. In lesion-free periods, PE is usually greater than 0.3, and normalized NMP is less than 90% and p(s1)>p(s720). Any deviation from these values could be a possible warning of altered neurophysiology. In the terminal phases of the lesion, the normalized NMP is higher than 95%, and PE is not sensitive to changes in ICP and p(s720)>p(s1). The results show that it could be used for real-time patient monitoring or as input for a machine learning tool.

Medical errors and patient safety: Strategies for reducing errors using artificial intelligence

Background: Medical errors remain a significant challenge in healthcare, contributing to adverse patient outcomes, increased costs, and extended hospitalizations. These errors encompass diagnostic inaccuracies, medication mistakes, surgical errors, and communication breakdowns. The global prevalence of medical errors underscores the urgent need for effective strategies to enhance patient safety. Aim: This article explores the role of Artificial Intelligence (AI) in reducing medical errors and improving patient safety. It aims to evaluate how AI technologies can mitigate various types of medical errors, and the challenges associated with their implementation. Methods: The study reviews current literature on AI applications in healthcare, focusing on diagnostic support, medication safety, surgical precision, and patient monitoring. It analyzes the effectiveness of AI-driven systems in reducing errors across different medical disciplines and examines the integration challenges, including ethical and regulatory concerns. Results: AI technologies, including machine learning algorithms and decision support systems, have demonstrated significant potential in enhancing diagnostic accuracy, preventing medication errors, and improving surgical outcomes. AI-driven systems have shown promising results in real-time patient monitoring, early detection of adverse events, and optimizing healthcare management. However, challenges related to data privacy, algorithm transparency, and integration into clinical workflows persist.

Advances in pre-hospital emergency care

Background: Emergency Medical Services (EMS) in the United States have evolved significantly since the 1960s, leading to a complex and diverse system that varies across regions. Despite the critical role EMS plays in pre-hospital care, there remains substantial confusion about its operations, structure, and effectiveness. This complexity has driven the need for innovative practices and technology to enhance pre-hospital care and patient outcomes. Aim: This article reviews recent advancements in pre-hospital emergency care, focusing on innovations and technological developments designed to improve EMS efficiency and patient outcomes. Methods: A comprehensive review of literature and current practices in pre-hospital care was conducted, examining advancements in technology, EMS curricula, and the integration of new tools and techniques into emergency medical practices. Results: Key innovations in pre-hospital care include mobile health technology, telemedicine integration, advanced diagnostic devices, automated external defibrillators (AEDs), wearable health sensors, drones for medical delivery, enhanced trauma care equipment, electronic patient care reporting (ePCR), portable ventilators, and simulation training. These advancements have improved real-time data collection, patient monitoring, diagnostic accuracy, and overall efficiency of emergency responses. Conclusion: The integration of innovative technologies and practices into EMS has substantially enhanced pre-hospital care.