Remote Patient Research Articles

Oncology Clinicians' Perspectives of a Remote Patient Monitoring Program: A Multi-Modal Case Study Approach.

Remote patient monitoring (RPM) aims to improve patient access to care and communication with clinical providers. Overall, understanding the usability of RPM applications and their influence on clinical care workflows is limited from the perspectives of clinician end users at a cancer center in the Northeast, United States. Explore the usability and functionality of RPM and elicit the perceptions and experiences of oncology clinicians using RPM for oncology patients after hospital discharge. The sample included 30 of 98 clinicians (31% response rate) managing at least five patients in the RPM program and responding to the m-Health Usability between March 2021- October 2021. Overall, clinicians responded positively to the survey. Item responses with the highest proportion of disagreement were explored further. A nested sample of five clinicians who responded to the study survey (30% response rate) participated in interview sessions conducted from November 2021 to February 2022, and averaged 60 minutes each. Survey responses highlighted that RPM was easy to use and learn and verified symptom alerts during follow-up phone calls. Areas to improve identified practice changes from reporting RPM alerts through digital portals and its influence on clinicians' workload burden. Interview sessions revealed three main themes: clinician understanding and usability constraints, patient constraints, and suggestions for improving the program. Subthemes for each theme were explored, characterizing technical and functional limitations that could be addressed to enhance efficiency, workflow, and user experience. Clinicians support the value of RPM for improving symptom management and engaging with providers. Functional changes to enhance the program's utility, such as input from patients about temporal changes in their symptoms and technical resources for home monitoring devices.

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

Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine.

Remote patient management may improve prognosis in heart failure. Daily review of transmitted data for early recognition of patients at risk requires substantial resources that represent a major barrier to wide implementation. An automated analysis of incoming data for detection of risk for imminent events would allow focusing on patients requiring prompt medical intervention. We analysed data of the Telemedical Interventional Management in Heart Failure II (TIM-HF2) randomized trial that were collected during quarterly in-patient visits and daily transmissions from non-invasive monitoring devices. By application of machine learning, we developed and internally validated a risk score for heart failure hospitalisation within seven days following data transmission as estimate of short-term patient risk for adverse heart failure events. Score performance was assessed by the area under the receiver-operating characteristic (ROCAUC) and compared with a conventional algorithm, a heuristic rule set originally applied in the randomized trial. The machine learning model significantly outperformed the conventional algorithm (ROCAUC 0.855 vs. 0.727, p < 0.001). On average, the machine learning risk score increased continuously in the three weeks preceding heart failure hospitalisations, indicating potential for early detection of risk. In a simulated one-year scenario, daily review of only the one third of patients with the highest machine learning risk score would have led to detection of 95% of HF hospitalisations occurring within the following seven days. A machine learning model allowed automated analysis of incoming remote monitoring data and reliable identification of patients at risk of heart failure hospitalisation requiring immediate medical intervention. This approach may significantly reduce the need for manual data review.

Improving the Accuracy of mmWave Radar for Ethical Patient Monitoring in Mental Health Settings.

Monitoring patient safety in high-risk mental health environments is a challenge for clinical staff. There has been a recent increase in the adoption of contactless sensing solutions for remote patient monitoring. mmWave radar is a technology that has high potential in this field due it its low cost and protection of privacy; however, it is prone to multipath reflections and other sources of environmental noise. This paper discusses some of the challenges in mmWave remote sensing applications for patient safety in mental health wards. In line with these challenges, we propose a novel low-data solution to mitigate the impact of multipath reflections and other sources of noise in mmWave sensing. Our solution uses an unscented Kalman filter for target tracking over time and analyses features of movement to determine whether targets are human or not. We chose a commercial off-the-shelf radar and compared the accuracy and reliability of sensor measurements before and after applying our solution. Our results show a marked decrease in false positives and false negatives during human target tracking, as well as an improvement in spatial location detection in a two-dimensional space. These improvements demonstrate how a simple low-data solution can improve existing mmWave sensors, making them more suitable for patient safety solutions in high-risk environments.

Increasing Trends of Artificial Intelligence With Robotic Process Automation in Health Care: A Narrative Review.

This review explores the fast-growing importance of artificial intelligence (AI) with robotic process automation (RPA) in healthcare. AI uses intelligent algorithms to analyze data, while RPA automates repetitive tasks to improve efficiency and accuracy. These technologies are swiftly revolutionizing health care by improving diagnostic precision, accelerating administrative tasks, reducing operation timing, and improving patient care. Application of these technologies requires good technical understanding, preparedness for continuous learning, and adaptability to new challenges. This review aims to provide an in-depth study of the potential applications, present implementations, challenges, and future scope of AI with RPA in healthcare. It can provide information to researchers, professionals, and decision-makers regarding the application of the technologies under consideration for better productivity, increased security and accuracy of data, cost reduction, and personalization of healthcare provided to patients. The main results are that AI and RPA can ensure greater data security, provide supporting work in administration, like scheduling appointments and medical billing, make better decisions, enable telehealth and remote patient monitoring, reduce human error, and increase overall health outcomes. This review overviews the challenges in implementing robotics technology, focusing mainly on secondary sourcejournals, scholarly articles, and reference books. Key findings indicate that this study reveals how robotics could alleviate healthcare professionals. Further research, investment, and collaboration will be needed to enable these technologies to reach their full potential for healthcare delivery. However, challenges such as data privacy and security concerns, high implementation costs, and regulatory and ethical considerations must be addressed. The conclusion emphasizes that while these technologies are revolutionizing healthcare by increasing efficiency and personalizing patient care, ongoing research, investment, and collaboration are essential for their successful adoption.

A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT

AbstractInternet of Medical Things have vastly increased the potential for remote patient monitoring, data‐driven care, and networked healthcare delivery. However, the connectedness lays sensitive patient data and fragile medical devices open to security threats that need robust intrusion detection solutions within cloud‐edge services. Current approaches need modification to be able to handle the practical challenges that result from problems with data quality. This paper presents a hybrid intrusion detection framework that enhances the security of IoMT networks. There are three modules in the design. First, an XGBoost‐based noise detection model is used to identify data anomalies. Second, adaptive resampling with ADASYN is done to fine‐tune the class distribution to address class imbalance. Third, ensemble learning performs intrusion detection through a Random Forest classifier. This stacked model coordinates techniques that filter noise and preprocess imbalanced data, identifying threats with high accuracy and reliability. These results are then experimentally validated on the UNSW‐NB15 benchmark to demonstrate effective detection under realistically noisy conditions. The novel contributions of the work are a new hybrid structural paradigm coupled with integrated noise filtering, and ensemble learning. The proposed advanced oversampling with ADASYN gives a performance that surpasses all others with a reported 92.23% accuracy.

Willingness to use remote patient monitoring among cardiovascular patients in a resource-limited setting: a cross-sectional study

IntroductionCurrently, mortality by non-communicable diseases is increasing alarmingly. They account for approximately 35 million deaths each year, of which 14% are due to cardiovascular disease and 9.2% occur in Africa. Patients do not have access to healthcare services outside the healthcare setting, resulting in missed follow-ups and appointments and adverse outcomes. This study aimed to assess the willingness to use remote monitoring among cardiovascular patients in a resource-limited setting in Ethiopia.MethodAn institution-based cross-sectional study was conducted from April to June 2021 among cardiovascular patients at referral hospitals in Ethiopia. A structured interview questionnaire was used to collect the data. A systematic random sampling technique was used to select 397 study participants. Binary and multivariable logistic regression analyses were employed and a 95% confidence level with a p-value &amp;lt;0.05 was used to determine the level of association between variables.ResultIn total, 81.61% of the study participants were willing to use remote patient monitoring [95% confidence interval (CI) = 77.4%–85.1%]. Age [adjusted odds ratio (AOR) = 0.94; 95% CI: 0.90–0.98], having a mobile phone (AOR = 5.70; 95% CI: 1.86–17.22), and perceived usefulness (AOR = 1.50; 95% CI: 1.18–1.82) were significantly associated with willingness to use remote patient monitoring among cardiovascular patients.ConclusionCardiovascular patients had a high willingness to use remote patient monitoring. Age, perceived usefulness of remote patient monitoring, and having a mobile phone were significantly associated with a willingness to use remote patient monitoring.

Update on Patient Self-Testing with Portable and Wearable Devices: Advantages and Limitations.

Laboratory medicine has undergone a deep and multifaceted revolution in the course of human history, in both organizational and technical terms. Over the past century, there has been a growing recognition of the need to centralize numerous diagnostic activities, often similar or identical but located in different clinical departments, into a common environment (i.e., the medical laboratory service), followed by a progressive centralization of tests from smaller laboratories into larger diagnostic facilities. Nevertheless, the numerous technological advances that emerged at the beginning of the new millennium have helped to create a new testing culture characterized by a countervailing trend of decentralization of some tests closer to patients and caregivers. The forces that have driven this (centripetal) counter-revolution essentially include a few key concepts, namely "home testing", "portable or even wearable devices" and "remote patient monitoring". By their very nature, laboratory medical services and remote patient testing/monitoring are not contradictory, but may well coexist, with the choice of one or the other depending on the demographic and clinical characteristics of the patient, the type of analytical procedure and the logistics and local organization of the care system. Therefore, this article aims to provide a general overview of patient self-testing, with a particular focus on portable and wearable (including implantable) devices.

Remote patient monitoring in patients with COVID-19 at home: literature review.

During the pandemic healthcare providers implemented remote patient monitoring (RPM) for patients suffering from COVID-19. RPM is an interaction between healthcare professionals and patients who are in different locations, in which a certain number of patient's functioning parameters is assessed and followed up for a certain duration of time. By implementing RPM for these patients they obtained to reduce the strain on hospitals and primary care. With this literature review we aim at describing the characteristics of the RPM interventions, reporting on the patients with COVID-19 included in RPM, and providing an overview of outcome variables such as length of stay (LOS), hospital (re)admissions, and mortality. A combination of different searches in several database types (traditional databases, trial registers, daily (google) searches and daily Pubmed alerts) were run daily from March 2020 till December 2021. A search update for randomized clinical trials (RCT's) was done in April 2022. The initial search yielded more than 4448 articles (not including daily searches). After deduplication and assessment for eligibility, 241 articles were retained describing 164 telemonitoring studies from 160 centres. None of the 164 studies covering 248,431 included patients reported on the presence of a randomized control group. Studies described a 'prehosp' group (96 studies) with patients who had a suspected or confirmed COVID-19 diagnosis and for whom it was decided not to hospitalize them yet, but closely monitor them at home, or a 'posthosp' group (32 studies) with patients who were monitored at home after hospitalization for COVID-19; 34 studies described both groups, in 2 studies it was unclear. There is a large variety in number of emergency department (ED) visits (0-36% and 0-16%) and no convincing evidence that RPM leads to less or more ED-visits as well as hospital (re)admissions (0-30% and 0-22%) in prehosp and posthosp, respectively. Mortality was generally low, and there is weak to no evidence that RPM is associated with lower mortality. There is neither evidence that RPM shortens previous LOS. A literature update detected three small scale RCT's which could not demonstrate statistically significant differences in these outcomes. Most papers claim savings, however the scientific base for these claims is doubtful. The overall patient experiences with RPM were positive, as patients felt more reassured, although many patients declined RPM for several reasons (eg, technological embarrassment, digital literacy, etc.). Based on these results, there is no convincing evidence that RPM in COVID-19 patients could avoid ED-visits or hospital (re)admissions, could shorten LOS or reduce mortality, but neither is there evidence that RPM has adverse outcomes. Further research should focus on developing, implementing, and evaluating an RPM framework.

A vision to the future: value-based laboratory medicine.

The ultimate goal of value-based laboratory medicine is maximizing the effectiveness of laboratory tests in improving patient outcomes, optimizing resources and minimizing unnecessary costs. This approach abandons the oversimplified notion of test volume and cost, in favor of emphasizing the clinical utility and quality of diagnostic tests in the clinical decision-making. Several key elements characterize value-based laboratory medicine, which can be summarized in some basic concepts, such as organization of invitro diagnostics (including appropriateness, integrated diagnostics, networking, remote patient monitoring, disruptive innovations), translation of laboratory data into clinical information and measurable outcomes, sustainability, reimbursement, ethics (e.g., patient empowerment and safety, data protection, analysis of big data, scientific publishing). Education and training are also crucial, along with considerations for the future of the profession, which will be largely influenced by advances in automation, information technology, artificial intelligence, and regulations concerning invitro diagnostics. This collective opinion paper, composed of summaries from presentations given at the two-day European Federation of Laboratory Medicine (EFLM) Strategic Conference "A vision to the future: value-based laboratory medicine" (Padova, Italy; September 23-24, 2024), aims to provide a comprehensive overview of value-based laboratory medicine, projecting the profession into a more clinically effective and sustainable future.

Escalation Pathways of Remote Patient Monitoring Programs for COVID-19 Patients in Canada and the United States: A Rapid Review.

Introduction: During the COVID-19 pandemic, hospitals in North America were overwhelmed with COVID-19 patients and had limited capacity to admit patients. Remote patient monitoring (RPM) programs were developed to monitor COVID-19 patients at home and reduce disease transmission and the demand on hospitals. A critical component of RPM programs is effective escalation pathways. The purpose of this review is to synthesize the implementation of escalation pathways of RPM programs for COVID-19 patients in Canada and the United States. Methods: The search identified 563 articles from Embase, PubMed, and Scopus. Following title and abstract screening, 131 were selected for full-text review, and 26 articles were included. Data were extracted on study location, patient eligibility and program size, data collection, monitoring team, escalation criteria, and escalation response. Results: The included studies were published between 2020 and 2022; 3 in Canada and 23 in the United States. The RPM programs collected physiological vital signs and symptom data, which were inputted manually by patients and health care workers or synced automatically. Escalations were triggered automatically or following manual review by nurses and physicians when signs and symptoms were concerning or reached a specific threshold. Escalations included emergency department referrals, physician appointments, and increased monitoring. Conclusion: Many decisions are required when designing RPM escalation pathways for patients with COVID-19, which is crucial to promptly address patients' changing health statuses and clinical needs. Future research is needed to evaluate the effectiveness of escalation pathways for COVID-19 patients through performance metrics and patient and health care worker experience.

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.

PROSPECTIVE TRIAL OF HOME OPTICAL COHERENCE TOMOGRAPHY-GUIDED MANAGEMENT OF TREATMENT EXPERIENCED NEOVASCULAR AGE-RELATED MACULAR DEGENERATION PATIENTS.

To evaluate the impact of home optical coherence tomography (OCT)-guided patient management on treatment burden and visual outcomes. An interventional trial was conducted to compare frequency of treatment and visual acuity for the neovascular age-related macular degeneration patients before and during use of home optical coherence tomography over a period of 6 months. Patient adherence to regular scanning was measured by the number of scans performed per week. The characteristics of episodes of fluid recurrence and classification of typical fluid volume trajectories were performed. Twenty-seven eyes (21 with diagnosis of neovascular age-related macular degeneration and one converted during the study), of 15 patients were monitored for 6 months, scanning at 6.2 times/week per eye and yielding 4,435 scans of which 91.2% were eligible for artificial intelligence-based fluid volume quantification. Total number of monitoring weeks before and during the study were 1,555 and 509. The mean (SD) number of weeks per injection before and during home OCT management were 8.0 (4.7) and 15.3 (8.5) (P = 0.004), respectively. The mean (SD) visual acuity change before and during home OCT-based management was 3.5 (12.0) letters and 0.0 (9.5) letters (P = 0.45), respectively, showing no significant impact on visual acuity. For the first time, remote patient monitoring with a home OCT allowed personalized management of neovascular age-related macular degeneration. This study showed significant reduction in treatment burden while maintaining stable visual acuity.

Development and Validation of a Mobile Phone Application for Measuring Knee Range of Motion.

Knee range of motion (ROM) is an important indicator of knee function. Outside the clinical setting, patients may not be able to accurately assess knee ROM, which may impair recovery following trauma or surgery. This study aims to validate a smartphone mobile application developed to measure knee ROM compared to visual and goniometer ROM measurements. A knee ROM Android mobile application was developed to measure knee ROM. Patients ≥ 18 years old presenting to an orthopaedic clinic with native knee complaints were approached to participate. Knee ROM was measured bilaterally by an arthroplasty-trained surgeon using (1) vision, (2) goniometer, and (3) the mobile application. Measurements were compared in flexion and extension using a one-way analysis of variance with post hoc Tukey test (alpha = 0.05). Eighty-four knee ROM measurements (40 left, 44 right) were obtained in 47 patients. Median Kellgren-Lawrence grade from available radiographs was grade 3. In flexion, mobile application (117.6 ± 14.7 degrees) measurements were not significantly different from visual (116.1 ± 13.6 degrees) or goniometer (116.2 ± 13.6 degrees) measurements. In extension, mobile application (4.8 ± 7.3 degrees) measurements were significantly different from visual (1.9 ± 4.1 degrees) measurements on post hoc analysis (p < 0.01), while no differences were present compared to goniometer (3.1 ± 5.8 degrees) measurements. Our study found that a mobile application for evaluating knee ROM was noninferior to goniometer-based measurements performed by an arthroplasty-trained surgeon. Future studies will investigate this application's utility in (1) remote patient care, (2) accelerating recovery during rehabilitation, (3) detecting early postoperative complications including arthrofibrosis, and (4) adding additional functionalities to the application to provide more detail-oriented descriptive analyses of patient knee function.

Video-Guided Optimization of Stimulation Settings in Patients with Parkinson's Disease and Deep Brain Stimulation.

Deep brain stimulation (DBS) for Parkinson's disease (PD) often necessitates frequent clinic visits for stimulation program optimization, with limited experience in remote patient management. Due to the resource-intensive nature of these procedures, we investigated a way to simplify stimulation optimization for these patients that allows for the continuous monitoring of symptoms while also reducing patient burden and travel distances. To this end, we prospectively recruited ten patients treated with DBS for PD to evaluate the feasibility of telemedicinal optimization in a home-based setting. Patients recorded daily videos of a modified Unified Parkinson's Disease Rating Scale (UPDRS) III, which experienced DBS physicians located at the clinic assessed to provide instructions on adjusting stimulation settings using a handheld programmer with previously set programs as well as patient amplitude control. This study concluded with significant improvements in participants' motor status as measured by the UPDRS-III (p = 0.0313) compared to baseline values. These findings suggest that remote video-guided optimization of DBS settings is feasible and may enhance motor outcomes for patients.

A DATA DRIVEN APPROACH THROUGH IOMT BASED PATIENT HEALTHCARE MONITORING SYSTEM

The current circumstances underscore the pressing need to accelerate the adoption of Internet of Things platforms in realm of the medical field to improve the health of mankind. Given the present challenges, I propose the development of an Advancing healthcare monitoring system built on the foundation of Internet of Medical Things (IOMT). Advancing patients healthcare IOMT system serves as a communication conduit between patients and doctors, facilitated by the IoMT platform. This platform utilizes various medical sensors connected to a server through technologies such as WiFi and Bluetooth. The data collected and stored in the server is then leveraged to analyse patients' health conditions, guiding subsequent treatment protocols. In this proposed system, the microcontroller serves as an intermediary interface between the sensors and the server. This cutting-edge technology enables remote patient monitoring, eliminating the need for physical presence. Within the prototype, patients' summaries and information is supplied using an online platform. Through the adoption of Advancing Healthcare through Data-Driven IOMT Patient Monitoring, lives can be safeguarded from critical conditions, underscoring the vital importance of embracing such advanced technologies in healthcare.

Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to prevent or reduce the severity of AECOPD are urgently needed. Internationally, this has prompted increased interest in the potential of remote patient monitoring (RPM) and digital medicine. RPM refers to the direct transmission of patient-reported outcomes, physiological, and functional data, including heart rate, weight, blood pressure, oxygen saturation, physical activity, and lung function (spirometry), directly to health care professionals through automation, web-based data entry, or phone-based data entry. Machine learning has the potential to enhance RPM in chronic obstructive pulmonary disease by increasing the accuracy and precision of AECOPD prediction systems. This study aimed to conduct a dual systematic review. The first review focuses on randomized controlled trials where RPM was used as an intervention to treat or improve AECOPD. The second review examines studies that combined machine learning with RPM to predict AECOPD. We review the evidence and concepts behind RPM and machine learning and discuss the strengths, limitations, and clinical use of available systems. We have generated a list of recommendations needed to deliver patient and health care system benefits. A comprehensive search strategy, encompassing the Scopus and Web of Science databases, was used to identify relevant studies. A total of 2 independent reviewers (HMGG and CM) conducted study selection, data extraction, and quality assessment, with discrepancies resolved through consensus. Data synthesis involved evidence assessment using a Critical Appraisal Skills Programme checklist and a narrative synthesis. Reporting followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. These narrative syntheses suggest that 57% (16/28) of the randomized controlled trials for RPM interventions fail to achieve the required level of evidence for better outcomes in AECOPD. However, the integration of machine learning into RPM demonstrates promise for increasing the predictive accuracy of AECOPD and, therefore, early intervention. This review suggests a transition toward the integration of machine learning into RPM for predicting AECOPD. We discuss particular RPM indices that have the potential to improve AECOPD prediction and highlight research gaps concerning patient factors and the maintained adoption of RPM. Furthermore, we emphasize the importance of a more comprehensive examination of patient and health care burdens associated with RPM, along with the development of practical solutions.

Implementing Autonomous Control in the Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring.

Conventional patient monitoring methods require skin-to-skin contact, continuous observation, and long working shifts, causing physical and mental stress for medical professionals. Remote patient monitoring (RPM) assists healthcare workers in monitoring patients distantly using various wearable sensors, reducing stress and infection risk. RPM can be enabled by using the Digital Twins (DTs)-based Internet of Robotic Things (IoRT) that merges robotics with the Internet of Things (IoT) and creates a virtual twin (VT) that acquires sensor data from the physical twin (PT) during operation to reflect its behavior. However, manual navigation of PT causes cognitive fatigue for the operator, affecting trust dynamics, satisfaction, and task performance. Also, operating manual systems requires proper training and long-term experience. This research implements autonomous control in the DTs-based IoRT to remotely monitor patients with chronic or contagious diseases. This work extends our previous paper that required the user to manually operate the PT using its VT to collect patient data for medical inspection. The proposed decision-making algorithm enables the PT to autonomously navigate towards the patient's room, collect and transmit health data, and return to the base station while avoiding various obstacles. Rather than manually navigating, the medical personnel direct the PT to a specific target position using the Menu buttons. The medical staff can monitor the PT and the received sensor information in the pre-built virtual environment (VE). Based on the operator's preference, manual control of the PT is also achievable. The experimental outcomes and comparative analysis verify the efficiency of the proposed system.

Place of telemedicine in the organization of emergency care: feasibility and benefits

IntroductionTelemedicine encompasses all medical practices that allow patients to be remotely cared for through new information and communication technologies. This study aims to assess the remote management of patients consulting emergency services and not requiring in-hospital care during both pre-pandemic and pandemic periods of COVID-19.MethodsThis was a prospective, randomized, controlled study. The telemedicine group received follow-up at home after emergency room discharge according to a predefined protocol, with telephone calls on days 2, 7, 15, and 30. The control group received standard care without regular telephone follow-up (only a call on day 30). The study was conducted with patients consulting the emergency department of FarhatHached Hospital in Sousse. Patient inclusion occurred between November 1, 2019, and April 30, 2020. The primary outcome measures were the re-consultation rate and treatment adherence. The secondary outcome measure was patient satisfaction.SPSS version 23.0 for Windows was used for data analysis. Descriptive statistics calculated frequencies, percentages, means, standard deviations, medians, and range. Analytical study involved Student’s t-test and Pearson chi-square test for mean and frequency comparisons, respectively. Significance threshold (p) for all tests was set at 0.05.ResultsA total of 400 patients were included. The average age of patients was 40 years. Both groups were comparable in terms of demographics and clinical characteristics. Diagnoses included mainly benign infectious diseases, trauma, mild decompensations of chronic conditions (asthma, COPD, heart failure), and suspected COVID cases. Patients in the telemedicine group tended to reconsult less in the month following their initial emergency room visit (14% versus 26.5%) (p = 0.004). There was a significant difference in treatment adherence between the telemedicine group and the control group (97.5% versus 92%; p = 0.014). The satisfaction with telemedicine was higher than satisfaction with regard to an in-person consultation at the emergency department (90% versus 37.5%).ConclusionIt is necessary to implement telemedicine in Tunisia, especially in emergency services. It ensures better remote patient care by reducing re-consultation rates, increasing treatment adherence, and improving patient satisfaction.

A Clinician and Electronic Health Record Wearable Device Intervention to Increase Physical Activity in Patients With Obesity: Formative Qualitative Study.

The number of individuals using digital health devices has grown in recent years. A higher rate of use in patients suggests that primary care providers (PCPs) may be able to leverage these tools to effectively guide and monitor physical activity (PA) for their patients. Despite evidence that remote patient monitoring (RPM) may enhance obesity interventions, few primary care practices have implemented programs that use commercial digital health tools to promote health or reduce complications of the disease. This formative study aimed to assess the perceptions, needs, and challenges of implementation of an electronic health record (EHR)-integrated RPM program using wearable devices to promote patient PA at a large urban primary care practice to prepare for future intervention. Our team identified existing workflows to upload wearable data to the EHR (Epic Systems), which included direct Fitbit (Google) integration that allowed for patient PA data to be uploaded to the EHR. We identified pictorial job aids describing the clinical workflow to PCPs. We then performed semistructured interviews with PCPs (n=10) and patients with obesity (n=8) at a large urban primary care clinic regarding their preferences and barriers to the program. We presented previously developed pictorial aids with instructions for (1) providers to complete an order set, set step-count goals, and receive feedback and (2) patients to set up their wearable devices and connect them to their patient portal account. We used rapid qualitative analysis during and after the interviews to code and develop key themes for both patients and providers that addressed our research objective. In total, 3 themes were identified from provider interviews: (1) providers' knowledge of PA prescription is focused on general guidelines with limited knowledge on how to tailor guidance to patients, (2) providers were open to receiving PA data but were worried about being overburdened by additional patient data, and (3) providers were concerned about patients being able to equitably access and participate in digital health interventions. In addition, 3 themes were also identified from patient interviews: (1) patients received limited or nonspecific guidance regarding PA from providers and other resources, (2) patients want to share exercise metrics with the health care team and receive tailored PA guidance at regular intervals, and (3) patients need written resources to support setting up an RPM program with access to live assistance on an as-needed basis. Implementation of an EHR-based RPM program and associated workflow is acceptable to PCPs and patients but will require attention to provider concerns of added burdensome patient data and patient concerns of receiving tailored PA guidance. Our ongoing work will pilot the RPM program and evaluate feasibility and acceptability within a primary care setting.