Pearls of wisdom from the past 5 years of working in telehealth

Over the past several years the perception of telehealth – and its role in healthcare delivery – has changed dramatically. Previously limited to just a few use cases including low-acuity virtual urgent care and chronic outpatient disease management, telehealth now plays some role in virtually every medical specialty and has seen considerable growth in technologies beyond the simple video visit. In this chapter, we highlight the forces that have driven telehealth's rapid growth and adoption. First, we discuss the evolution of the telehealth landscape in the years leading up to the COVID‐19 pandemic, including increasing consumer demand for virtual services, the emergence of new payment models that promote telehealth use, advancements in technical capabilities, and new structures that enabled reimbursement of digital health activities. Then we cover advancements in telehealth directly related to the pandemic and important considerations for continued growth including provider workflow integration, accessibility and equity, and clarity around reimbursement. Finally, we discuss technological innovations and new modes of care delivery – such as digital therapeutics and virtual-first health plans – that are likely to enhance the sophistication and expand the role of telehealth services over the coming years. © 2023 Elsevier Inc. All rights reserved.

The rise of AI in telehealth

Since its introduction in 1955, artificial intelligence (AI) has continued its growth and expansion across all industries and societal sectors. It took the COVID-19 pandemic for AI and its subsets to take the center stage in medicine and health care. AI is a broad discipline and encompasses machine learning (ML), deep learning (DL), and other techniques. Advancements in AI enabled, facilitated, and accelerated the expansion of telehealth. Telehealth describes the wide array of digital information and communication technologies and systems that allow the delivery of health and health-related services. There are three distinct subtypes of telehealth: synchronous, asynchronous, and remote (tele) monitoring. The overarching goal of telehealth is to break down barriers in delivery of high value care by overcoming challenges resulting from time or location constraints. The end goal is not to replace in-person care, rather to commoditize and democratize high quality, high value care. On the other hand, there remain significant limitations and pitfalls, particularly regulatory and technological. Examples include best practice guidelines on the adaptation of standards regulating data exchange, expansion of reimbursement and importantly ethical challenges. The latter include critical issues such as data privacy, security, and governance, AI-introduced bias, the black box nature of some AI/ML algorithms and the impact of AI technologies/algorithms on health disparities and inequities. Disparities in access to and use of tele-health were already known but highlighted during the COVID-19 pandemic. Recognition of this hurdle led to the emerging and rapidly growing field of digital determinants of health, which comprise factors like digital literacy, access to AI/technology, and community infrastructure like access to WiFi/broadband internet. © 2023 Elsevier Inc. All rights reserved.

A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges.

Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies. Numerous attempts have been made to fight Covid-19-like pandemics using technologies such as the Internet of Things, wireless body area network, blockchain, and machine learning. Since the disease is highly contagious, novel research in patients' health monitoring system is essential for the constant monitoring of pandemic patients with minimal or no human intervention. With the ongoing pandemic of SARS-CoV-2, popularly known as Covid-19, innovations for monitoring of patients' vitals and storing them securely have risen more than ever. Analyzing the stored patients' data can further assist healthcare workers in their decision-making process. In this paper, we surveyed the research works on remote monitoring of pandemic patients admitted in hospitals or quarantined at home. First, an overview of pandemic patient monitoring is given followed by a brief introduction of enabling technologies i.e. Internet of Things, blockchain, and machine learning to implement the system. The reviewed works have been classified into three categories; remote monitoring of pandemic patients using IoT, blockchain-based storage or sharing platforms for patients' data, and processing/analyzing the stored patients' data using machine learning for prognosis and diagnosis. We also identified several open research issues to set directions for future research.

Towards A Framework for Implementing Remote Patient Monitoring From an Integrated Care Perspective: A Scoping Review

Background: Remote patient monitoring (RPM) has been increasingly adopted over the last decade, with the COVID-19 pandemic fostering its rapid development. As RPM implementation is recognised as complex and highly demanding in terms of resources and processes, there are multiple challenges in providing RPM in an integrated logic. Methods: To examine the structural elements that are relevant for implementing RPM integrated care, a scoping review was conducted in PubMed, Scopus, and Web of Science, leveraging a search strategy that combines terms relative to (1) conceptual models and real-life initiatives;(2) RPM;and (3) care integration. Results: 28 articles were included, covering nine conceptual models and 19 real-life initiatives. Eighteen structural elements of RPM integrated care implementation were identified among conceptual models, defining a structure for assessing real-life initiatives. 78.9% of those initiatives referred to at least ten structural elements, with patient education and self-monitoring promotion, multidisciplinary core workforce, ICTs (information and communications technologies) and telemonitoring (TM) devices, and health indicators measurement being present in all studies, and therefore being core elements to the design of RPM initiatives. Conclusion: RPM goes far beyond technology, with underlying processes and involved actors playing a central role in care provision. The structural elements identified can guide RPM implementation and promote maturity in adoption. Future research may focus on assessing design completeness, evaluating impacts, and analysing related financial arrangements.

The Pi-CON Methodology Applied: Operator Errors and Preference Tracking of a Novel Ubiquitous Vital Signs Sensor and Its User Interface

Remote Patient Monitoring has enjoyed strong growth to new heights driven by several factors, such as the COVID-19 pandemic or advances in technology, allowing consumers and patients to continuously record health data by themselves. This does not come without its challenges, however. A literature review was completed and highlights usability gaps when using wearables or home use medical devices in a virtual environment. Based on these findings, the Pi-CON methodology was applied to close these gaps by utilizing a novel sensor that allows the acquisition of vital signs at a distance, without any sensors touching the patient. Pi-CON stands for passive, continuous and non-contact, and describes the ability to acquire vital signs continuously and passively, with limited user interaction. The preference of vital sign acquisition with a newly developed sensor was tested and compared to vital sign tests taken with patient generated health-data devices (ear thermometer, pulse oximeter) measuring heart rate, respiratory rate and body temperature. In addition, the amount of operator errors and the user interfaces were tested and compared. Results show that participants preferred vital signs acquisition with the novel sensor and the developed user interface of the sensor. Results also revealed that participants had a mean error of .85 per vital sign measurement with the patient-generated health data devices and .33 with the developed sensor, confirming the beneficial impact available when using the developed sensor based on the Pi-CON methodology.

Post-Covid Remote Patient Monitoring Using Medical Internet Of Things And Machine Learning Analytics

The Covid-19 pandemic disturbed the smooth functioning of healthcare services throughout the world. New practices such as masking, social distancing and so on were followed to prevent the spread. Further, the severity of the problem increases for the elderly people and people having co-morbidities as proper medical care was not possible and as a result many deaths were recorded. Even for those patients who recovered from Covid could not get proper health monitoring in the Post-Covid phase as a result many deaths and severity in health conditions were reported after the Covid recovery i.e., the Post-Covid era. Technical interventions like the Internet of Things (IoT) based remote patient monitoring using Medical Internet of Things (M-IoT) wearables is one of the solutions that could help in the Post-Covid scenarios. The paper discusses a proposed framework where in a variety of IoT sensing devices along with ML algorithms are used for patient monitoring by utilizing aggregated data acquired from the registered Post-Covid patients. Thus, by using M-IoT along with Machine Learning (ML) approaches could help us in monitoring Post-Covid patients with co-morbidities for and immediate medical help. © 2023 SCPE.

An Iot-Based Covid Patient Health Control System

Healthcare monitoring frameworks emerged as one of the most essential frameworks and innovations established over the last decade. As a result of failing to provide adequate clinical attention to patients at the appropriate time, many people are facing the possibility of an untimely death. Ultimately, the goal was to develop an IoT-based integrated healthcare monitoring framework that could be relied upon by healthcare professionals to screen their patients, whether they were in the hospital or at home, to ensure that they were being well-cared for. A mobile phone-based remote healthcare monitoring framework has been constructed with the help of sensors, an information acquisition unit, a microcontroller (such as Arduino), and a product modification. This framework has the potential to provide continuous web-based data regarding a patient's physiological states (i.e., JAVA). Before transmitting it to the specialist's portable device along with the application, the framework examines the patient's temperature, heart rate, and EEG data. It then displays and saves this information. An Internet of Things-based patient monitoring framework may monitor a patient's health condition in an efficient manner and save the patient's life at the appropriate moment. © 2023 IEEE.

Allowing privacy-preserving fog computing with digital identity assurance in remote clinical services

Nowadays, there is an increasing demand for cloud-based remote clinical services, both for diagnosis and monitoring. The COVID-19 pandemic has dramatically amplified this need. E-government programs should quickly go towards the expansion of this type of services, also to avoid that people (especially elderly) renounce treatment or adequate healthcare. However, to be effective, latency between IoT medical devices and the cloud should be reduced as much as possible. For this reason, fog computing appears the best approach, as part of the elaboration is moved closer to the user. However, some privacy threats arise. Indeed, these services can be delivered only based on secure digital identity and authentication systems, but the intermediate fog layer should learn nothing about the identity of users and the link among different service requests. In this paper, we propose a concrete solution to the above issue by leveraging eIDAS-compliant digital identity and by including a cryptographic protocol to provide anonymity and unlinkability of user's access to fog servers. Copyright © 2023 Inderscience Enterprises Ltd.

Mobile health vs. standard care after cardiac surgery: results of The Box 2.0 study.

AIMS: Postoperative atrial fibrillation (POAF) is a common complication of cardiac surgery, yet difficult to detect in ambulatory patients. The primary aim of this study is to investigate the effect of a mobile health (mHealth) intervention on POAF detection after cardiac surgery. METHODS AND RESULTS: We performed an observational cohort study among 730 adult patients who underwent cardiac surgery at a tertiary care hospital in The Netherlands. Of these patients, 365 patients received standard care and were included as a historical control group, undergoing surgery between December 2017 and September 2018, and 365 patients were prospectively included from November 2018 and November 2020, undergoing an mHealth intervention which consisted of blood pressure, temperature, weight, and electrocardiogram (ECG) monitoring. One physical outpatient follow-up moment was replaced by an electronic visit. All patients were requested to fill out a satisfaction and quality of life questionnaire. Mean age in the intervention group was 62 years, 275 (70.4%) patients were males. A total of 4136 12-lead ECGs were registered. In the intervention group, 61 (16.7%) patients were diagnosed with POAF vs. 25 (6.8%) patients in the control group [adjusted risk ratio (RR) of POAF detection: 2.15; 95% confidence interval (CI): 1.55-3.97]. De novo atrial fibrillation was found in 13 patients using mHealth (6.5%) vs. 4 control group patients (1.8%; adjusted RR 3.94, 95% CI: 1.50-11.27). CONCLUSION: Scheduled self-measurements with mHealth devices could increase the probability of detecting POAF within 3 months after cardiac surgery. The effect of an increase in POAF detection on clinical outcomes needs to be addressed in future research.

Applicability of edge computing paradigm for Covid-19 mitigation

With lockdowns and overburdened medical facilities during the Covid-19 pandemic, technology and computing paradigms play a vital role in providing remote healthcare solutions. We assess as how the existing computing paradigms could be deployed to prevent the spread of the disease, expedite the diagnosis, and facilitate remote monitoring of patients to reduce the burden on the overstretched medical facilities. The chapter will include a literature survey based on the articles published in but not limited to Science Direct, Google Scholar, Research Gate, and PubMed. This study weighs the pros and cons of using different paradigms in diverse scenarios and provides recommendations for efficient healthcare solutions. The chapter also focuses on the issues related to edge computing, such as resource provisioning, energy preservation, etc. In this era of technology, edge computing can be used to enhance the efficacy of healthcare solutions without burdening healthcare professionals and facilities. In this chapter, experimentation will focus on deploying intelligent techniques in the edge computing paradigm. © 2023 Elsevier Inc. All rights reserved.

Remote Monitoring of COVID-19 Patients Following Discharge from a Tertiary Care Center

The COVID-19 pandemic has affected people, healthcare systems and caregivers on a global scale causing bottlenecks in hospital resources and overload of healthcare systems. The presence of disease sequelae in patients hospitalized due to CO VID-19 warrants additional care and monitoring of these patients. Remote monitoring techniques have been implemented in several domains of healthcare such as cardiology, cardiac rehabilitation and nephrology. Monitoring of vital signs using these technologies has allowed the tracking of patients with more granularity, resulting in better clinical outcomes such as reduction in hospitalizations. Therefore, we hypothesize that remote monitoring is beneficial in managing CO VID-19 patients post-hospitalization, enabling home-based patient follow-up. In this study, we investigated the use of remote monitoring on a COVID-19 patient cohort discharged from a tertiary care center. A post-hoc division of patients into two groups (alert-generating patients and non-alert generating patients) was performed. The longitudinal progression of sensor and questionnaire data was studied using linear mixed-effect models. The measured heart rate values were statistically significant in terms of the intercept (p < 0.001), indicating a difference between the two patient groups at baseline immediately post-discharge. © 2022 Creative Commons.

IoT-Based Telemedicine Health Monitoring System with a Fuzzy Inference-Based Medical Decision Support Module for Clinical Risk Evaluation

This paper discusses the design and implementation of an Internet of Things (IoT)-based telemedicine health monitoring system (THMS) with an early warning scoring (EWS) function that reads, assesses, and logs physiological parameters of a patient such as body temperature, oxygen saturation level, systemic arterial pressure, breathing patterns, pulse (heart) rate, supplemental oxygen dependency, consciousness, and pain level using Particle Photon microcontrollers interfaced with biosensors and switches. The Mandami fuzzy inference-based medical decision support system (FI-MDSS) was also developed using MATLAB to assist medical professionals in evaluating a patient's health risk and deciding on the appropriate clinical intervention. The patient's physiological measurements, EWS, and health risk category are stored on the Particle cloud and Thing Speak cloud platforms and can be accessed remotely and in real-time via the Internet. Furthermore, a RESTful application programming interface (API) was developed using GO language and PostgreSQL database to enhance data presentation and accessibility. Based on the paired samples t-tests obtained from 6 sessions with 10 trials for each vital sign per session, there were no significant differences between the clinical data obtained from the designed prototype and the commercially sold medical equipment. The mean differences between the compared samples for each physiological data were not more than 0.40, the standard deviations were less than 2.3, and the p-values were greater than 0.05. With a 96.67% accuracy, the FI-MDSS predicted health risk levels that were comparable to conventional EWS techniques such as the Modified National Early Warning Score (m-NEWS) and NEWS2, which are used in the clinical decision-making process for managing patients with COVID-19 and other infectious illnesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Framework for Real-time Remote ECG Monitoring and Diagnoses

Enabled by the fast development of Internet of Things (IoT) technologies in recent years, the healthcare domain has witnessed significant advancements in wearable devices that seamlessly collect vital medical information. With the availability of IoT devices serving the healthcare domain, extraordinary amounts of sensory data are generated in real-time, requiring immediate diagnoses and attention in critical medical conditions. The provision of remote patient monitoring (RPM) and analytics infrastructure proved to be fundamental components of the healthcare domain during the Coronavirus pandemic. Traditional healthcare services are digitized and offered virtually, where patients are monitored and managed remotely without the need to go to hospitals. This paper presents a comprehensive RPM framework for real-time telehealth operations with scalable data monitoring, real-time analytics and decision-making, fine-grained data access and robust notification mechanisms in emergencies and critical health conditions. We focus on the overall framework architecture, enabling technologies integration, various system-level integrations and deployment options. Furthermore, we provide a use case application for patients with chronic heart conditions for real-time electrocardiogram (ECG) monitoring. We are releasing the framework as open-source software to the active research community. © 2022 IEEE.

Machine learning-assisted remote patient monitoring with data analytics

The health condition of the patients needs to be monitored with immense care. Healthcare promotes good health, helps in monitoring the patient's health status, disease diagnosis, and its management along with recovery. Monitoring the health condition postdischarge or postoperation is required to ensure a speedy recovery. Healthcare services can benefit from technological advancements to ensure better service. Healthcare assisted with machine learning techniques plays a significant role in the effective diagnosis of ailments, monitoring patient's health condition, and extend support in taking suitable measures during abnormality. In the proposed work, we collect the patient's data using sensors and upload them to the cloud. The collected data are subjected to preprocessing followed by analysis. The patient's health is remotely monitored, and machine learning techniques are applied to foretell abnormalities in the patient's health condition. Existing remote monitoring systems are not flexible and, hence, may result in an increased number of false positives. We try to reduce unnecessary alerts via machine learning methods and data analytics. Essential attributes like pulse rate, blood pressure, temperature, gender, and cholesterol levels of the patient are taken into consideration while predicting the results. In the time of pandemics, like COVID-19 with the scarce availability of medical personnel and treatment resources, this prediction may help in taking appropriate measures at the earliest. We train the model with the Kaggle Heart Disease UCI data set and test the model with real-time patient data. We apply our model to k nearest neighbor (KNN) and Naïve Bayes algorithm. The KNN has performed well over the Naïve Bayes algorithm. © 2022 Scrivener Publishing LLC.

Remote Cardiovascular Hypertension Program Enhanced Blood Pressure Control During the COVID-19 Pandemic.

Background The COVID-19 pandemic disrupted traditional health care; one fallout was a drastic decrease in blood pressure (BP) assessment. We analyzed the pandemic's impact on our existing remote hypertension management program's effectiveness and adaptability. Methods and Results This retrospective observational analysis evaluated BP control in an entirely remote management program before and during the pandemic. A team of pharmacists, nurse practitioners, physicians, and nonlicensed navigators used an evidence-based clinical algorithm to optimize hypertensive treatment. The algorithm was adapted during the pandemic to simplify BP control. Overall, 1256 patients (605 enrolled in the 6 months before the pandemic shutdown in March 2020 and 651 in the 6 months after) were a median age of 63 years old, 57% female, and 38.2% non-White. Among enrolled patients with sustained hypertension, 51.1% reached BP goals. Within this group, rates of achieving goal BP improved to 94.6% during the pandemic from 75.8% prepandemic (P<0.0001). Mean baseline home BP was 141.7/81.9 mm Hg during the pandemic and 139.8/82.2 prepandemic, and fell ≈16/9 mm Hg in both periods (P<0.0001). Maintenance during the pandemic was achieved earlier (median 11.8 versus 19.6 weeks, P<0.0001), with more frequent monthly calls (8.2 versus 3.1, P<0.0001) and more monthly home BP recordings per patient (32.4 versus 18.9, P<0.0001), compared with the prepandemic period. Conclusions A remote clinical management program was successfully adapted and delivered significant improvements in BP control and increased home BP monitoring despite a nationally observed disruption of traditional hypertension care. Such programs have the potential to transform hypertension management and care delivery.

Prenatal Care via Telehealth.

During the COVID-19 pandemic, providers and patients explored the use of telehealth on a wide and rapid scale. Reflecting on how prenatal providers and pregnant patients used telehealth during the pandemic and afterward, we review existing and new lessons learned from the pandemic. This article summarizes international and national guidelines on prenatal care, presents practice examples on how telehealth and remote patient monitoring were used during the COVID-19 pandemic, and offers lessons learned and suggestions for future care.

Using a centralized nursing team to implement multi-specialty pediatric remote patient monitoring programs.

BACKGROUND: The increase in telehealth usage has sustained since the beginning of the COVID-19 pandemic. While Remote Patient Monitoring (RPM) programs are abundantly used in the management of adults, pediatric RPM programs remain rare. METHODS: An RPM department was developed to serve several, multi-specialty pediatric programs. This department uses a centralized nursing team that manages all patients enrolled in RPM programs. Each program is unique and created in partnership with the centralized nurses and the ambulatory care teams. The various programs allow for transmission of patient- and caregiver-generated health data and consistent communication between the patient or caregiver and the managing providers, allowing for real-time plan adaptation. FINDINGS: Over 1200 patients have been managed through the 18 various RPM programs. Approximately 300 patients are monitored each month by the centralized nursing team. Patient and caregiver experience has been high due to resources offered including on-demand video visits and text messaging with the nursing team. DISCUSSION: Multi-specialty RPM departments help to expand the reach of an institution and provide care to more patients. Quality improvement must be ongoing to ensure equity of participation and perceived benefit of the programs for both providers and patients and caregivers. APPLICATION TO PRACTICE: Pediatric RPM programs can improve patient care delivery by decreasing days away from home while improving access to care. Ensuring equitable opportunity for patient participation is imperative in achieving success for an RPM department.

Perceived barriers and facilitators of structural reimbursement for remote patient monitoring, an exploratory qualitative study

Objective: Structural reimbursement can be an important factor for large-scale implementing and upscaling of remote patient monitoring (RPM). During the COVID-19 pandemic, the Dutch Healthcare Authority expanded regulations, creating novel opportunities to reimburse RPM. Despite these regulations, barriers to the reim-bursement of RPM remain. This study aimed to identify the barriers and facilitators of structural reimbursement of RPM in hospital care in the Netherlands and to propose actionable recommendations.Methods: This is an exploratory qualitative study with relevant stakeholders in the Dutch purchasing market: the Dutch Healthcare Authority, health insurers, and healthcare providers. Semi-structured interviews were held between October and December of 2020. All interviews were conducted using a digital medium, transcribed verbatim, and thematically analyzed.Results: Multiple perceived barriers were mentioned: wrong pocket problems (i.e. the entity that bears the costs of implementation does not receive the benefits), no uniform quality and outcome indicators, lack of willingness to redesign care pathways by providers, and difficulties implementing cross-sector models. Perceived facilitators included interdisciplinary cooperation and transparency, the use of alternative payment models, increase in the total number of patients per RPM project, and the optional reimbursement scheme. Conclusion: Our interviews found barriers and facilitators concerning structural reimbursement of RPM in hos-pital settings in the Netherlands. Our results emphasize that the successful integration of structural reimburse-ment requires: 1) understanding the improvement potential of RPM by creating business cases, 2) co-creation (redesigning care paths) from the outset of an RPM project, 3) and allocating financial risk by providers and insurers.Public Interest Summary: The COVID-19 pandemic has demonstrated the strong potential of consultation and monitoring patients at a distance. Remote patient monitoring -the use of information technologies for moni-toring patients at a distance -is seen as a potential solution to urgent challenges in the healthcare system. Nevertheless, embedding remote patient monitoring innovations into routine healthcare is often challenging, partly due to difficulties in reimbursing these initiatives. Barriers to reimbursing remote patient monitoring included organizational factors, no uniform quality and outcome indicators, and difficulties using different payment models. Perceived facilitators included an increase in the total number of patients per project, better interdisciplinary cooperation and transparency, and help from the Dutch Healthcare Authority. Introducing these insights into healthcare policy dialogues could support reimbursement of remote patient monitoring and stim-ulate the collaboration of healthcare stakeholders responsible for implementing and scaling up remote patient monitoring projects.

Systematic Threat Analysis of Modern Unified Healthcare Communication Systems

Recently, smart medical devices have become preva-lent in remote monitoring of patients and the delivery of medication. The ongoing Covid-19 pandemic situation has boosted the upward trend of the popularity of smart medical devices in the healthcare system. Simultaneously, different device manufacturers and technologies compete for a share in a smart medical device's market, which forces the integration of diverse smart medical de-vices into a common healthcare ecosystem. Hence, modern unified healthcare communication systems (UHCSs) combine ISO/IEEE 11073 and Health Level Seven (HL7) communication standards to support smart medical devices' interoperability and their communication with healthcare providers. Despite their advantages in supporting various smart medical devices and communication technologies, these standards do not provide any security and suffer from vulnerabilities. Existing studies provide stand-alone security solutions to components of UHCSs and do not cover UHCSs holistically. In this paper, we perform a systematic threat analysis of UHCSs that relies on attack-defense tree (ADTree) formalisms. Considering the attack landscape and defense ecosys-tem, we build an ADTree for UHCSs and convert the ADTree to stochastic timed automata (STA) to perform quantitative analysis. Our analysis using UPPAAL SMC shows that the Man-in-the-Middle and unauthorized remote access attacks are the most probable attacks that a malicious entity could pursue, causing mistreatment to patients. We also extract valuable information about the top threats, the likelihood of performing different individual and simultaneous attacks, and the expected cost for attackers. © 2022 IEEE.

Experiences with the implementation of remote monitoring in patients with COVID-19: A qualitative study with patients and healthcare professionals.

INTRODUCTION: This study investigated how patients with COVID-19, telemonitoring (TM) teams, general practitioners (GPs) and primary care nurses in Belgium experienced remote patient monitoring (RPM) in 12 healthcare organizations, in relation to the patients' illness, health, and care needs, perceived quality of care, patient and health system outcomes, and implementation challenges. DESIGN: A qualitative research approach was adopted, including focus group discussions and semi-structured interviews. METHODS: Four different groups of participants were interviewed, that is, patients (n = 17), TM teams (n = 27), GPs (n = 16), and primary care nurses (n = 12). An interview guide was drafted based on a literature review. Interviews were transcribed verbatim, and NVivo was used for managing and analyzing the data. The QUAGOL method was used to guide the data analysis process and was adapted for the purpose of a thematic content analysis. RESULTS: All participants agreed that RPM-reassured patients. The overall perceived value of RPM for individual patients depended on how well the intervention matched with their needs. Patients who did not have the necessary language (Dutch/French speaking) and digital skills, who did not have the right equipment (smartphone or tablet), or who missed the necessary infrastructure (no internet coverage in their region) were often excluded. Remote patient monitoring also reassured healthcare professionals as it gave them information on a disease they had little knowledge about. Professionals involved in RPM experienced a high workload. All TM teams agreed that quality of data was a key factor to ensure an adequate follow-up, but they differed in what they found important. The logistic management of RPM was a challenge because of the contagious character of COVID-19, and the need for an effective information flow between the hospital team and primary care providers. Participants missed clarification about who was accountable for the care for patients in the projects. Primary care nurses and GPs missed access to RPM data. All agreed that the funding they received was not sufficient to cover all the costs associated with RPM. CONCLUSION: Healthcare professionals and patients perceive RPM as valuable and believe that the concept will have its place in the Belgium health system. However, current RPM practice is challenged by many barriers, and the sustainability of RPM implementation is low. CLINICAL RELEVANCE: Remote patient monitoring (RPM) was perceived as a valuable intervention for patients with COVID-19, but there were important concerns about unequal access to care. While the technology for RPM is available, the sustainability of implementation is low because of concerns with data quality, challenging logistics within projects, lack of data integration and communication, and a lack of an overarching guiding framework.