Ethical and Legal Implications of Remote Monitoring of Medical Devices

Healthcare Transformation: Artificial Intelligence Is the Dire Imperative of the Day

At present, healthcare systems around the world are confronted with unprecedented challenges caused by aging demographics, increasing chronic diseases, and resource challenges. In this scenario, artificial intelligence (AI) emerges as a disruptive technology that can provide solutions to these complicated problems. This review article outlines the vital role played by AI in altering the health landscape. The constant demand for effective and accessible healthcare demands the use of new solutions. AI can be described as an important imperative, enabling advancements in many areas of the delivery of healthcare. This review article explores the possibilities of use of AI to aid in the field of healthcare assistants, diagnosing, disease prediction, and personalized treatment and the discovery of drugs, telemedicine and remote monitoring of patients, robotic-assisted procedures imaging for pathology and radiology analysis, and the analysis of genomic data. By analyzing the existing research and cases, we explain how AI-driven technology can optimize processes in healthcare, improve diagnosis accuracy, improve the quality of treatment, and simplify administrative tasks. By highlighting the most successful AI applications and laying out possible future developments, the review article will provide insight for healthcare professionals, policymakers, researchers, and other stakeholders in harnessing the power of AI to transform healthcare delivery and enhance the quality of care for patients.

Remote monitoring of cardiac implantable electronic devices using smart device interface versus radiofrequency-based interface: A systematic review

Background

Guidelines recommended remote monitoring (RM) in managing patients with Cardiac Implantable Electronic Devices. In recent years, smart device (phone or tablet) monitoring-based RM (SM-RM) was introduced. This study aims to systematically review SM-RM versus bedside monitor RM (BM-RM) using radiofrequency in terms of compliance, connectivity, and episode transmission time.

Methods

We conducted a systematic review, searching three international databases from inception until July 2023 for studies comparing SM-RM (intervention group) versus BM-RM (control group).

Results

Two matched studies (21 978 patients) were retrieved (SM-RM arm: 9642 patients, BM-RM arm: 12 336 patients). There is significantly higher compliance among SM-RM patients compared with BM-RM patients in both pacemaker and defibrillator patients. Manyam et al. found that more SM-RM patients than BM-RM patients transmitted at least once (98.1% vs. 94.3%, p < .001), and Tarakji et al. showed that SM-RM patients have higher success rates of scheduled transmissions than traditional BM-RM methods (SM-RM: 94.6%, pacemaker manual: 56.3%, pacemaker wireless: 77.0%, defibrillator wireless: 87.1%). There were higher enrolment rates, completed scheduled and patient-initiated transmissions, shorter episode transmission time, and higher connectivity among SM-RM patients compared to BM-RM patients. Younger patients (aged <75) had more patient-initiated transmissions, and a higher proportion had ≥10 transmissions compared with older patients (aged ≥75) in both SM-RM and BM-RM groups.

Conclusion

SM-RM is a step in the right direction, with good compliance, connectivity, and shorter episode transmission time, empowering patients to be in control of their health. Further research on cost-effectiveness and long-term clinical outcomes can be carried out.

Impact of digital services on healthcare and social welfare: An umbrella review

BACKGROUND

Digital services can be effective and cost-efficient options for treating non-communicable diseases, but generalizability is limited due to heterogeneous treatment effects. This umbrella review aims to evaluate the impact of digital services on population health, costs, and patient and healthcare professional satisfaction, and to identify facilitators and barriers to using digital services in healthcare and social welfare.

METHODS

The protocol of the study was registered on the 4th of September 2022 to the International Prospective Register of Systematic Reviews, PROSPERO (CRD42022355635). The review was performed using the Centre for Reviews and Dissemination, Cochrane, Ovid Medline, Scopus, and Web of Science in June 2022. The methodological quality of the included reviews was assessed. The impact of digital services was categorized as no evidence, no dominance, and mixed and positive effect. Inductive content analysis was used to identify facilitators and barriers.

RESULTS

A total of 66 studies were included in the review, 64 % of which were evaluated as high quality. Studies on the impact of digital services in social welfare were not identified. Sixty-five percent of reviews evaluated the impact of digital services on population health with mixed effects; 21 % were on costs with mixed effects; 27 % were on patient satisfaction with positive effects; and 7.6 % were on healthcare professionals' satisfaction with mixed effects. Various features, allocation, end-user support, organized services, and service development facilitated the use of digital services. Correspondingly, barriers were related to service limitations, digital competency, funding- and service strategies, resources and change management.

CONCLUSIONS

Compared to usual care, digital services had a mixed impact on population health and costs with high satisfaction in patients. Mixed healthcare professionals' satisfaction was associated with the use of digital services, and it was less studied. To ensure successful implementation and sustainability of digital services, attention must be paid to address barriers and supporting facilitators at all levels.

Robotic Pills as Innovative Personalized Medicine Tools: A Mini Review

The most common route for drug administration is the oral route due to the various advantages offered by this route, such as ease of administration, controlled and sustained drug delivery, convenience, and non-invasiveness. In spite of this, oral drug absorption faces challenges due to various issues related to its stability, permeability and solubility in the GI tract. Biologic drugs generally face problems when administered by oral route as they are readily degradable and thus required to be injected. To overcome these issues in oral absorption, different approaches like novel drug delivery systems and newer pharmaceutical technologies have been adopted. With a combined knowledge of drug delivery and pharmaceutical technology, robotic pills can be designed and used successfully to enhance the adhesion and permeation of drugs through the mucus membrane of the GI tract to achieve drug delivery at the target site. The potential application of robotic pills in diagnosis and drug dispensing is also discussed. The review highlights recent developments in robotic pill drug-device technology and discusses its potential applications to solve the problems and challenges in oral drug delivery.

Recommendation to implementation of remote patient monitoring in rheumatology: lessons learned and barriers to take

Remote patient monitoring (RPM) leverages advanced technology to monitor and manage patients' health remotely and continuously. In 2022 European Alliance of Associations for Rheumatology (EULAR) points-to-consider for remote care were published to foster adoption of RPM, providing guidelines on where to position RPM in our practices. Sample papers and studies describe the value of RPM. But for many rheumatologists, the unanswered question remains the 'how to?' implement RPM.Using the successful, though not frictionless example of the Southmead rheumatology department, we address three types of barriers for the implementation of RPM: service, clinician and patients, with subsequent learning points that could be helpful for new teams planning to implement RPM. These address, but are not limited to, data governance, selecting high quality cost-effective solutions and ensuring compliance with data protection regulations. In addition, we describe five lacunas that could further improve RPM when addressed: establishing quality standards, creating a comprehensive database of available RPM tools, integrating data with electronic patient records, addressing reimbursement uncertainties and improving digital literacy among patients and healthcare professionals.

Reducing Clinical Trial Monitoring Resources and Costs With Remote Monitoring: Retrospective Study Comparing On-Site Versus Hybrid Monitoring

BACKGROUND

Clinical research associates (CRAs) monitor the progress of a trial, verify the data collected, and ensure that the trial is carried out and reported in accordance with the trial protocol, standard operating procedures, and relevant laws and regulations. In response to monitoring challenges during the COVID-19 pandemic, Peking University Cancer Hospital launched a remote monitoring system and established a monitoring model, combining on-site and remote monitoring of clinical trials. Considering the increasing digitization of clinical trials, it is important to determine the optimal monitoring model for the general benefit of centers conducting clinical trials worldwide.

OBJECTIVE

We sought to summarize our practical experience of a hybrid model of remote and on-site monitoring of clinical trials and provide guidance for clinical trial monitoring management.

METHODS

We evaluated 201 trials conducted by our hospital that used on-site monitoring alone or a hybrid monitoring model, of which 91 trials used on-site monitoring alone (arm A) and 110 used a hybrid model of remote and on-site monitoring (arm B). We reviewed trial monitoring reports from June 20, 2021, to June 20, 2022, and used a customized questionnaire to collect and compare the following information: monitoring cost of trials in the 2 models as a sum of the CRAs' transportation (eg, taxi fare and air fare), accommodation, and meal costs; differences in monitoring frequency; the number of monitored documents; and monitoring duration.

RESULTS

From June 20, 2021, to June 20, 2022, a total of 320 CRAs representing 201 sponsors used the remote monitoring system for source data review and the verification of data from 3299 patients in 320 trials. Arm A trials were monitored 728 times and arm B trials were monitored 849 times. The hybrid model in arm B had 52.9% (449/849) remote visits and 48.1% (409/849) on-site visits. The number of patients' visits that could be reviewed in the hybrid monitoring model increased by 34% (4.70/13.80; P=.004) compared with that in the traditional model, whereas the duration of monitoring decreased by 13.8% (3.96/28.61; P=.03) and the total cost of monitoring decreased by 46.2% (CNY ¥188.74/408.80; P<.001). These differences were shown by nonparametric testing to be statistically significant (P<.05).

CONCLUSIONS

The hybrid monitoring model can ensure timely detection of monitoring issues, improve monitoring efficiency, and reduce the cost of clinical trials and should therefore be applied more broadly in future clinical studies.

Artificial intelligence tools in clinical neuroradiology: essential medico-legal aspects

Commercial software based on artificial intelligence (AI) is entering clinical practice in neuroradiology. Consequently, medico-legal aspects of using Software as a Medical Device (SaMD) become increasingly important. These medico-legal issues warrant an interdisciplinary approach and may affect the way we work in daily practice. In this article, we seek to address three major topics: medical malpractice liability, regulation of AI-based medical devices, and privacy protection in shared medical imaging data, thereby focusing on the legal frameworks of the European Union and the USA. As many of the presented concepts are very complex and, in part, remain yet unsolved, this article is not meant to be comprehensive but rather thought-provoking. The goal is to engage clinical neuroradiologists in the debate and equip them to actively shape these topics in the future.

Artificial intelligence applied in pulmonary hypertension: a bibliometric analysis

Introduction

Advances in Artificial Intelligence (AI) offer new Information Technology (IT) opportunities in various applications and fields (industry, health, etc.). The medical informatics scientific community expends tremendous effort on the management of diseases affecting vital organs making it a complex disease (lungs, heart, brain, kidneys, pancreas, and liver). Scientific research becomes more complex when several organs are simultaneously affected, as is the case with Pulmonary Hypertension (PH), which affects both the lungs and the heart. Therefore, early detection and diagnosis of PH are essential to monitor the disease's progression and prevent associated mortality.

Method

The issue addressed relates to knowledge of recent developments in AI approaches applied to PH. The aim is to provide a systematic review through a quantitative analysis of the scientific production concerning PH and the analysis of the networks of this production. This bibliometric approach is based on various statistical, data mining, and data visualization methods to assess research performance using scientific publications and various indicators (e.g., direct indicators of scientific production and scientific impact).

Results

The main sources used to obtain citation data are the Web of Science Core Collection and Google Scholar. The results indicate a diversity of journals (e.g., IEEE Access, Computers in Biology and Medicine, Biology Signal Processing and Control, Frontiers in Cardiovascular Medicine, Sensors) at the top of publications. The most relevant affiliations are universities from United States of America (Boston Univ, Harvard Med Sch, Univ Oxford, Stanford Univ) and United Kingdom (Imperial Coll London). The most cited keywords are "Classification", "Diagnosis", "Disease", "Prediction", and "Risk".

Conclusion

This bibliometric study is a crucial part of the review of the scientific literature on PH. It can be viewed as a guideline or tool that helps researchers and practitioners to understand the main scientific issues and challenges of AI modeling applied to PH. On the one hand, it makes it possible to increase the visibility of the progress made or the limits observed. Consequently, it promotes their wide dissemination. Furthermore, it offers valuable assistance in understanding the evolution of scientific AI activities applied to managing the diagnosis, treatment, and prognosis of PH. Finally, ethical considerations are described in each activity of data collection, treatment, and exploitation to preserve patients' legitimate rights.

Smart Pacemaker: A Review

Since the first pacemaker was implanted, nearly 60 years have passed. Since then, pacemaker technology has made major advancements that have increased both its safety and effectiveness in treating people with bradyarrhythmias. The repeated stimulation of cells in specialized "pacemaker" regions of the mammalian heart and the transmission of stimulus via the ventricles serve as evidence that the electrical function of the mammalian heart is necessary for a regular mechanical (pump) role. The development of action potentials in individual cardiac cells is linked to myocardial electrical activity and the heart's regular cooperative electrical functioning. A container or pulse initiator that houses the battery and electronics, as well as lines that connect to the myocardium to deliver a depolarizing pulse and detect intrinsic cardiac stimulation, are all parts of a pacemaker. Defibrillators could be used with artificial hearts that have electrical pacemakers integrated into them in order to treat arrhythmia, heart failure, and cardiac arrest. Modern pacemakers have units for supporting patients with other disorders like "heart failure," which happens when the heart does not pump as forcefully as it should. While many pacemakers are effective in treating different types of arrhythmias (irregular heartbeats), they also have units for treating them.

Remote Monitoring of Patient- and Family-Generated Health Data in Pediatrics

In this article, we provide an overview of remote monitoring of pediatric PGHD and family-generated health data, including its current uses, future opportunities, and implementation resources.

Management of Medico-Legal Risks in Digital Health Era: A Scoping Review

Artificial intelligence needs big data to develop reliable predictions. Therefore, storing and processing health data is essential for the new diagnostic and decisional technologies but, at the same time, represents a risk for privacy protection. This scoping review is aimed at underlying the medico-legal and ethical implications of the main artificial intelligence applications to healthcare, also focusing on the issues of the COVID-19 era. Starting from a summary of the United States (US) and European Union (EU) regulatory frameworks, the current medico-legal and ethical challenges are discussed in general terms before focusing on the specific issues regarding informed consent, medical malpractice/cognitive biases, automation and interconnectedness of medical devices, diagnostic algorithms and telemedicine. We aim at underlying that education of physicians on the management of this (new) kind of clinical risks can enhance compliance with regulations and avoid legal risks for the healthcare professionals and institutions.

Remote Monitoring of CIEDs-For Both Safety, Economy and Convenience?

Cardiac implantable electronic devices such as pacemakers and defibrillators are increasingly monitored by systems transmitting information directly from the patient to the hospital. This may increase safety and patient satisfaction and also under certain circumstances represent an economic advantage. The review summarizes some of the recent research in the field of remote monitoring of cardiac devices.

Organizational Models for Cardiac Implantable Electronic Device Remote Monitoring: Current and Future Directions

This review provides an overview of the literature on the organization, staffing, and structure of remote monitoring (RM) clinics, primarily from countries in Western Europe and United States, as well as the challenges, considerations, and future directions for RM clinic models of care. Using a current case example of an RM clinic in the Midwestern United States, this document provides key information from the viewpoint of a clinic undergoing a shift in workflow. Finally, this review distills key considerations for RM management for electrophysiology clinics, vendors and industry, and policy makers.

Lessons Learned from the COVID-19 Pandemic: Emphasizing the Emerging Role and Perspectives from Artificial Intelligence, Mobile Health, and Digital Laboratory Medicine

SARS-CoV-2, the new coronavirus causing COVID-19, is one of the most contagious disease of past decades. COVID-19 is different only in that everyone is encountering it for the first time during this pandemic. The world has gone from complete ignorance to a blitz of details in a matter of months. The foremost challenge that the scientific community faces is to understand the growth and transmission capability of the virus. As the world grapples with the global pandemic, people are spending more time than ever before living and working in the digital milieu, and the adoption of Artificial Intelligence (AI) is propelled to an unprecedented level especially as AI has already proven to play an important role in counteracting COVID-19. AI and Data Science are rapidly becoming important tools in clinical research, precision medicine, biomedical discovery and medical diagnostics. Machine learning (ML) and their subsets, such as deep learning, are also referred to as cognitive computing due to their foundational basis and relationship to cognition. To date, AI based techniques are helping epidemiologists in projecting the spread of virus, contact tracing, early detection, monitoring, social distancing, compiling data and training of healthcare workers. Beside AI, the use of telemedicine, mobile health or mHealth and the Internet of Things (IOT) is also emerging. These techniques have proven to be powerful tools in fighting against the pandemic because they provide strong support in pandemic prevention and control. The present study highlights applications and evaluations of these technologies, practices, and health delivery services as well as regulatory and ethical challenges regarding AI/ML-based medical products.