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Patient and staff experience is a vital factor to consider in the evaluation of remote patient monitoring (RPM) interventions. However, no comprehensive overview of available RPM patient and staff experience-measuring methods and tools exists. This review aimed at obtaining a comprehensive set of experience constructs and corresponding measuring instruments used in contemporary RPM research and at proposing an initial set of guidelines for improving methodological standardization in this domain. Full-text papers reporting on instances of patient or staff experience measuring in RPM interventions, written in English, and published after January 1, 2011, were considered for eligibility. By \"RPM interventions,\" we referred to interventions including sensor-based patient monitoring used for clinical decision-making; papers reporting on other kinds of interventions were therefore excluded. Papers describing primary care interventions, involving participants under 18 years of age, or focusing on attitudes or technologies rather than specific interventions were also excluded. We searched 2 electronic databases, Medline (PubMed) and EMBASE, on February 12, 2021.We explored and structured the obtained corpus of data through correspondence analysis, a multivariate statistical technique. In total, 158 papers were included, covering RPM interventions in a variety of domains. From these studies, we reported 546 experience-measuring instances in RPM, covering the use of 160 unique experience-measuring instruments to measure 120 unique experience constructs. We found that the research landscape has seen a sizeable growth in the past decade, that it is affected by a relative lack of focus on the experience of staff, and that the overall corpus of collected experience measures can be organized in 4 main categories (service system related, care related, usage and adherence related, and health outcome related). In the light of the collected findings, we provided a set of 6 actionable recommendations to RPM patient and staff experience evaluators, in terms of both what to measure and how to measure it. Overall, we suggested that RPM researchers and practitioners include experience measuring as part of integrated, interdisciplinary data strategies for continuous RPM evaluation. At present, there is a lack of consensus and standardization in the methods used to measure patient and staff experience in RPM, leading to a critical knowledge gap in our understanding of the impact of RPM interventions. This review offers targeted support for RPM experience evaluators by providing a structured, comprehensive overview of contemporary patient and staff experience measures and a set of practical guidelines for improving research quality and standardization in this domain.

Background Digital patient monitoring (DPM) tools can facilitate early symptom management for patients with cancer through systematic symptom reporting; however, low adherence can be a challenge. We assessed patient/healthcare professional (HCP) use of DPM in routine clinical practice. Materials and Methods Patients with locally advanced/metastatic lung cancer or HER2-positive breast cancer received locally approved/reimbursed drugs alongside DPM, with elements tailored by F. Hoffmann-La Roche Ltd, on the Kaiku Health DPM platform. Patient access to the DPM tool was through their own devices (eg, laptops, PCs, smartphones, or tablets), via either a browser or an app on Apple iOS or Android devices. Coprimary endpoints were patient DPM tool adoption (positive threshold: 60%) and week 1-6 adherence to weekly symptom reporting (positive threshold: 70%). Secondary endpoints included experience and clinical impact. Results At data cutoff (June 9, 2022), adoption was 85% and adherence was 76%. Customer satisfaction and effort scores for patients were 76% and 82%, respectively, and 83% and 79% for HCPs. Patients spent approximately 10 minutes using the DPM tool and completed approximately 1.0 symptom questionnaires per week (completion time 1-4 minutes). HCPs spent approximately 1-3 minutes a week using the tool per patient. Median time to HCP review for alerted versus non-alerted symptom questionnaires was 19.6 versus 21.5 hours. Most patients and HCPs felt that the DPM tool covered/mostly covered symptoms experienced (71% and 75%), was educational (65% and 92%), and improved patient-HCP conversations (70% and 83%) and cancer care (51% and 71%). Conclusion The DPM tool demonstrated positive adoption, adherence, and user experience for patients with lung/breast cancer, suggesting that DPM tools may benefit clinical cancer care. Digital patient monitoring tools can facilitate early symptom monitoring and management for cancer through systematic symptom reporting; however, low adherence can be a challenge. This study assessed patient and healthcare professional use of such tools in routine clinical practice.

During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate.

The co-design of health technology enables patient-centeredness and can help reduce barriers to technology use. The study objectives were to identify what remote patient monitoring (RPM) technology has been co-designed for inpatients and how effective it is, to identify and describe the co-design approaches used to develop RPM technologies and in which contexts they emerge, and to identify and describe barriers and facilitators of the co-design process. We conducted a systematic review of co-designed RPM technologies for inpatients or for the immediate postdischarge period and assessed (1) their effectiveness in improving health outcomes, (2) the co-design approaches used, and (3) barriers and facilitators to the co-design process. Eligible records included those involving stakeholders co-designing RPM technology for use in the inpatient setting or during the immediate postdischarge period. Searches were limited to the English language within the last 10 years. We searched MEDLINE, Embase, CINAHL, PsycInfo, and Science Citation Index (Web of Science) in April 2023. We used the Joanna Briggs Institute critical appraisal checklist for quasi-experimental studies and qualitative research. Findings are presented narratively. We screened 3334 reports, and 17 projects met the eligibility criteria. Interventions were designed for pre- and postsurgical monitoring (n=6), intensive care monitoring (n=2), posttransplant monitoring (n=3), rehabilitation (n=4), acute inpatients (n=1), and postpartum care (n=1). No projects evaluated the efficacy of their co-designed RPM technology. Three pilot studies reported clinical outcomes; their risk of bias was low to moderate. Pilot evaluations (11/17) also focused on nonclinical outcomes such as usability, usefulness, feasibility, and satisfaction. Common co-design approaches included needs assessment or ideation (16/17), prototyping (15/17), and pilot testing (11/17). The most commonly reported challenge to the co-design process was the generalizability of findings, closely followed by time and resource constraints and participant bias. Stakeholders' perceived value was the most frequently reported enabler of co-design. Other enablers included continued stakeholder engagement and methodological factors (ie, the use of flexible mixed method approaches and prototyping). Co-design methods can help enhance interventions' relevance, usability, and adoption. While included studies measured usability, satisfaction, and acceptability-critical factors for successful implementation and uptake-we could not determine the clinical effectiveness of co-designed RPM technologies. A stronger commitment to clinical evaluation is needed. Studies' use of diverse co-design approaches can foster stakeholder inclusivity, but greater standardization in co-design terminology is needed to improve the quality and consistency of co-design research.

Background Since the onset of COVID-19, oncology practices across the US have integrated telemedicine (TM) and remote patient monitoring (RPM) into routine care and clinical trials. The extent of provider experience and comfort with TM/RPM in treatment trials, however, is unknown. We surveyed oncology researchers to assess experience and comfort with TM/RPM. Methods Between April 10 and June 1, 2022, we distributed email surveys to US-based members of the American Society of Clinical Oncology (ASCO) whose member records indicated interest or specialization in clinical research. We collected respondent demographic data, clinical trial experience, workplace characteristics, and comfort and experience with TM/RPM use across trial components in phase I and phase II/III trials. TM/RPM was defined as clinical trial-related healthcare and monitoring for patients geographically separated from trial site. Results There were 141 surveys analyzed (5.1% response rate). Ninety percent of respondents had been Principal Investigators, 98% practiced in a norural site. Most respondents had enrolled patients in phase I (82%) and phase II/III trials (99%). Across all phases and trial components, there was a higher frequency of researcher comfort compared to experience. Regarding remote care in treatment trials, 75% reported using TM, RPM, or both. Among these individuals, 62% had never provided remote care to trial patients before the pandemic. Conclusion COVID-19 spurred the rise of TM/RPM in cancer treatment trials, and some TM/RPM use continues in this context. Among oncology researchers, higher levels of comfort compared with real-world experience with TM/RPM reveal opportunities for expanding TM/RPM policies and guidelines in oncology research. COVID-19 spurred the rise of telemedicine and remote patient monitoring in cancer treatment trials. This article assesses oncology researchers’ experience and comfort level with such methods of treatment in the context of treatment trials.

Remote patient monitoring (RPM) increased exponentially during the COVID-19 pandemic. RPM programs commonly incorporate tools to capture and transmit health-relevant data from the home to the clinical space to augment the clinical decision-making process of health care providers. Given the potential to improve patient health outcomes, health care systems around the world are actively engaged in fashioning, implementing, and exploring the outcomes of various RPM program models. However, new challenges to health care systems include increasing RPM program enrollment, optimizing condition-specific RPM programs to best address the needs of specific patient groups, integrating new RPM-derived data streams into existing IT infrastructure, overcoming limited availability of desired remote monitoring technologies, and quantifying the health outcomes produced by RPM use. Herein, we identify stakeholders for RPM in the United States, summarize the landscape of RPM tools available for chronic disease management, discuss the current regulatory environment, delve into the benefits and challenges of integrating these tools into clinical practice, summarize aspects of coverage and reimbursement, and examine the knowledge and policy gaps regarding sustained use of RPM in clinical practice, along with associated opportunities.

Hemoglobin A1C (HbA1c) is used as an index of average blood glucose measurement over a period of months and is a mainstay of blood glucose monitoring. This metric is easy to measure and relatively inexpensive to obtain, and it predicts diabetes-related microvascular complications. However, HbA1c provides only an approximate measure of glucose control; it does not address short-term glycemic variability (GV) or hypoglycemic events. Continuous glucose monitoring (CGM) is a tool which helps clinicians and people with diabetes to overcome the limitations of HbA1c in diabetes management. Time spent in the glycemic target range and time spent in hypoglycemia are the main CGM metrics that provide a more personalized approach to diabetes management. Moreover, the glucose management indicator (GMI), which calculates an approximate HbA1c level based on the average CGM-driven glucose level, facilitates individual decision-making when the laboratory-measured HbA1c and estimated HbA1c are discordant. GV, on the other hand, is a measure of swings in blood glucose levels over hours or days and may contribute to diabetes-related complications. In addition, addressing GV is a major challenge during the optimization of glycemia. The degree of GV is associated with the frequency, duration, and severity of the hypoglycemic events. Many factors affect GV in a patient, including lifestyle, diet, the presence of comorbidities, and diabetes therapy. Recent evidence supports the use of some glucose-lowering agents to improve GV, such as the new ultra-long acting insulin analogs, as these agents have a smoother pharmacodynamic profile and improve glycemic control with fewer fluctuations and fewer nocturnal hypoglycemic events. These newer glucose-lowering agents (such as incretin hormones or sodium–glucose cotransporter 2 inhibitors) can also reduce the degree of GV. However, randomized trials are needed to evaluate the effect of GV on important diabetes outcomes. In this review, we discuss the role of HbA1c as a measure of glycemic control and its limitations. We also explore additional glycemic metrics, with a focus on time (duration) in glucose target range, time (duration) in hypoglycemia, GV, GMI, and their correlation with clinical outcomes.

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.

Heart failure (HF) represents a growing public health concern, driven by rising prevalence and the challenge of frequent, costly (re-)hospitalizations from decompensation. To address these, HF management has progressed towards incorporating devices for remote patient monitoring (RPM), with most being focused on identifying decompensation and providing timely, tailored pharmacological interventions. To date, the pool of devices has enlarged substantially, forming a spectrum of invasive and non-invasive options whose clinical adoption potential is yet to be determined. This review summarizes existing devices for RPM in HF care, with a major focus on technical characteristics and potential clinical efficacy. To unify the two traditionally separated groups, we re-classify the sampled devices in a single taxonomical dimension, the physical location of the sensing element(s), and objectively assess their current development state using the Medical Device Readiness Level, a metric that merges technical and clinical perspectives. Furthermore, we outline additional evaluative metrics within two complementary dimensions, focused on process efficiency and patient outcomes, ultimately offering a structured framework to evaluate clinical adoption.