Clinical outcomes of digital sensor alerting systems in remote monitoring: a systematic review and meta-analysis

Remote-Controlled Sensing and Drug Delivery via 3D-Printed Hollow Microneedles

Remote health monitoring and treatment serve as critical drivers for advancing health equity, bridging geographical and socioeconomic disparities, ensuring equitable access to quality healthcare for those in underserved or remote regions. By democratizing healthcare, this approach offers timely interventions, continuous monitoring, and personalized care independent of one's location or socioeconomic status, thereby striving for an equitable distribution of health resources and outcomes. Meanwhile, microneedle arrays (MNAs), revolutionize painless and minimally invasive access to interstitial fluid for drug delivery and diagnostics. This paper introduces an integrated theranostic MNA system employing an array of colorimetric sensors to quantitatively measure -pH, glucose, and lactate, alongside a remotely-triggered system enabling on-demand drug delivery. Integration of an ultrasonic atomizer streamlines the drug delivery, facilitating rapid, pumpless, and point-of-care drug delivery, enhancing system portability while reducing complexities. An accompanying smartphone application interfaces the sensing and drug delivery components. Demonstrated capabilities include detecting pH (3 to 8), glucose (up to 16 mm), and lactate (up to 1.6 mm), showcasing on-demand drug delivery, and assessing delivery system performance via a scratch assay. This innovative approach confronts drug delivery challenges, particularly in managing chronic diseases requiring long-term treatment, while also offering avenues for non-invasive health monitoring through microneedle-based sensors.

A systematic review of the impacts of remote patient monitoring (RPM) interventions on safety, adherence, quality-of-life and cost-related outcomes

Due to rapid technological advancements, remote patient monitoring (RPM) technology has gained traction in recent years. While the effects of specific RPM interventions are known, few published reviews examine RPM in the context of care transitions from an inpatient hospital setting to a home environment. In this systematic review, we addressed this gap by examining the impacts of RPM interventions on patient safety, adherence, clinical and quality of life outcomes and cost-related outcomes during care transition from inpatient care to a home setting. We searched five academic databases (PubMed, CINAHL, PsycINFO, Embase and SCOPUS), screened 2606 articles, and included 29 studies from 16 countries. These studies examined seven types of RPM interventions (communication tools, computer-based systems, smartphone applications, web portals, augmented clinical devices with monitoring capabilities, wearables and standard clinical tools for intermittent monitoring). RPM interventions demonstrated positive outcomes in patient safety and adherence. RPM interventions also improved patients' mobility and functional statuses, but the impact on other clinical and quality-of-life measures, such as physical and mental health symptoms, remains inconclusive. In terms of cost-related outcomes, there was a clear downward trend in the risks of hospital admission/readmission, length of stay, number of outpatient visits and non-hospitalisation costs. Future research should explore whether incorporating intervention components with a strong human element alongside the deployment of technology enhances the effectiveness of RPM. The review highlights the need for more economic evaluations and implementation studies that shed light on the facilitators and barriers to adopting RPM interventions in different care settings.

Feasibility of Observing Cerebrovascular Disease Phenotypes with Smartphone Monitoring: Study Design Considerations for Real-World Studies

Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient ischemic attack. In this perspective, we present participant feedback and relevant smartphone data metrics suggesting that digital phenotyping of post-stroke depression is feasible. Additionally, we proffer thoughtful considerations for designing feasible real-world study protocols tracking cerebrovascular dysfunction with smartphone sensors.

Characterizing barriers and facilitators of metabolic bariatric surgery tourism: a systematic review

BACKGROUND

Metabolic bariatric surgery tourism continues to rise and has become a growing concern for bariatric surgeons globally. With varying degrees of regulation, counselling and success, those that develop complications may have to deal with a multitude of challenges often distant from their country of operation. The aim of this study was to characterize the barriers and facilitators influencing individuals to undergo metabolic bariatric surgery tourism, in order to better understand the implications to the National Health Service and other healthcare systems.

METHODS

A systematic literature search, restricted to the English language, was performed to identify relevant studies. All studies were included until December 2022, the last search date. Study quality was assessed with the validated mixed-methods appraisal tool. A Braun and Clarke thematic analysis was undertaken to identify themes and subthemes.

RESULTS

A total of five studies met the inclusion criteria. Identified themes included: availability, accessibility, cost, eligibility, reputation, and stigma; the available evidence was of varying quality.

CONCLUSION

This work identifies a series of subthemes influencing the decision to undertake metabolic bariatric surgery tourism. The results highlight the limited literature available in understanding the complex motivational insights; the scale of the problem in the current healthcare system; cost and long-term outcomes. A National Emergency Bariatric Surgery audit would allow generation of more robust data to explore further the issues of clinical relationships and networks and to guide policy making.

Patient-reported symptom monitoring: using (big) data to improve supportive care at the macro-, meso-, and micro-levels

PURPOSE

This paper aims to provide a comprehensive understanding of the need for continued development of symptom monitoring (SM) implementation, utilization, and data usage at the macro-, meso-, and micro-levels.

METHODS

Discussions from a patient-reported SM workshop at the MASCC/ISSO 2022 annual meeting were analyzed using a macro-meso-micro analytical framework of cancer care delivery. The workshop categories "initiation and implementation, barriers to adoption and utilization, and data usage" were integrated for each level.

RESULTS

At the macro-level, policy development could encourage data sharing and international collaboration, including the exchange of SM methods, supportive care models, and self-management modules. At the meso-level, institutions should adjust clinical workflow and service delivery and promote a thorough technical and clinical integration of SM. At the micro-level, SM should be individualized, with timely feedback for patients, and should foster trust and understanding of AI decision support tools amongst clinicians to improve supportive care.

CONCLUSIONS

The workshop reached a consensus among international experts on providing guidance on SM implementation, utilization, and (big) data usage pathways in cancer survivors across the cancer continuum and on macro-meso-micro levels.

Remote monitoring for long-term physical health conditions: an evidence and gap map

Background

Remote monitoring involves the measurement of an aspect of a patient's health without that person being seen face to face. It could benefit the individual and aid the efficient provision of health services. However, remote monitoring can be used to monitor different aspects of health in different ways. This evidence map allows users to find evidence on different forms of remote monitoring for different conditions easily to support the commissioning and implementation of interventions.

Objectives

The aim of this map was to provide an overview of the volume, diversity and nature of recent systematic reviews on the effectiveness, acceptability and implementation of remote monitoring for adults with long-term physical health conditions.

Data sources

We searched MEDLINE, nine further databases and Epistemonikos for systematic reviews published between 2018 and March 2022, PROSPERO for continuing reviews, and completed citation chasing on included studies.

Review methods

(Study selection and Study appraisal): Included systematic reviews focused on adult populations with a long-term physical health condition and reported on the effectiveness, acceptability or implementation of remote monitoring. All forms of remote monitoring where data were passed to a healthcare professional as part of the intervention were included. Data were extracted on the characteristics of the remote monitoring intervention and outcomes assessed in the review. AMSTAR 2 was used to assess quality. Results were presented in an interactive evidence and gap map and summarised narratively. Stakeholder and public and patient involvement groups provided feedback throughout the project.

Results

We included 72 systematic reviews. Of these, 61 focus on the effectiveness of remote monitoring and 24 on its acceptability and/or implementation, with some reviews reporting on both. The majority contained studies from North America and Europe (38 included studies from the United Kingdom). Patients with cardiovascular disease, diabetes and respiratory conditions were the most studied populations. Data were collected predominantly using common devices such as blood pressure monitors and transmitted via applications, websites, e-mail or patient portals, feedback provided via telephone call and by nurses. In terms of outcomes, most reviews focused on physical health, mental health and well-being, health service use, acceptability or implementation. Few reviews reported on less common conditions or on the views of carers or healthcare professionals. Most reviews were of low or critically low quality.

Limitations

Many terms are used to describe remote monitoring; we searched as widely as possible but may have missed some relevant reviews. Poor reporting of remote monitoring interventions may mean some included reviews contain interventions that do not meet our definition, while relevant reviews might have been excluded. This also made the interpretation of results difficult.

Conclusions and future work

The map provides an interactive, visual representation of evidence on the effectiveness of remote monitoring and its acceptability and successful implementation. This evidence could support the commissioning and delivery of remote monitoring interventions, while the limitations and gaps could inform further research and technological development. Future reviews should follow the guidelines for conducting and reporting systematic reviews and investigate the application of remote monitoring in less common conditions.

Review registration

A protocol was registered on the OSF registry (https://doi.org/10.17605/OSF.IO/6Q7P4).

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Services and Delivery Research programme (NIHR award ref: NIHR135450) as part of a series of evidence syntheses under award NIHR130538. For more information, visit https://fundingawards.nihr.ac.uk/award/NIHR135450 and https://fundingawards.nihr.ac.uk/award/NIHR130538. The report is published in full in Health and Social Care Delivery Research; Vol. 11, No. 22. See the NIHR Funding and Awards website for further project information.

Vagal Nerve Therapy in the Management of Obesity: A Systematic Review and Meta-Analysis

INTRODUCTION

The vagus nerve has an important role in satiety, metabolism, and autonomic control in upper gastrointestinal function. However, the role and effects of vagal nerve therapy on weight loss remain controversial. This systematic review and meta-analysis assessed the effects of vagal nerve therapy on weight loss, body mass index (BMI), and obesity-related conditions.

METHODS

MEDLINE, EMBASE, and CINAHL databases were searched for studies up to April 2022 that reported on percentage excess weight loss (%EWL) or BMI at 12 months or remission of obesity-related conditions following vagal nerve therapy from January 2000 to April 2022. Weighted mean difference (WMD) was calculated, meta-analysis was performed using random-effects models, and between-study heterogeneity was assessed.

RESULTS

Fifteen studies, of which nine were randomised controlled trials, of 1,447 patients were included. Vagal nerve therapy led to some improvement in %EWL (WMD 17.19%; 95% confidence interval [CI]: 10.94-23.44; p < 0.001) and BMI (WMD -2.24 kg/m2; 95% CI: -4.07 to -0.42; p = 0.016). There was a general improvement found in HbA1c following vagal nerve therapy when compared to no treatment given. No major complications were reported.

CONCLUSIONS

Vagal nerve therapy can safely result in a mild-to-moderate improvement in weight loss. However, further clinical trials are required to confirm these results and investigate the possibility of the long-term benefit of vagal nerve therapy as a dual therapy combined with standard surgical bariatric interventions.

Interface of Clinical Nurse Specialist Practice and Healthcare Technology

PURPOSE

This article demonstrates the leadership role of the clinical nurse specialist in developing and implementing healthcare technology across the continuum of care.

DESCRIPTION

Three virtual nursing practices-facilitated self-care, remote patient monitoring, and virtual acute care nursing-illustrate how the clinical nurse specialist is well suited to transform traditional practice models to ones that use healthcare technology effectively. These 3 practices use interactive healthcare technology to gather patient data and allow communication and coordination with the healthcare team to meet patient-specific needs.

OUTCOME

Use of healthcare technology in virtual nursing practices led to early care team intervention, optimized care team processes, proactive patient outreach, timely access to care, and reduction in healthcare-associated errors and near-miss events.

CONCLUSION

Clinical nurse specialists are well positioned to develop innovative, effective, accessible, and high-quality virtual nursing practices. Integrating healthcare technology with nursing practice augments care for various patients, ranging from those with low illness severity in the outpatient setting to acutely ill patients in the inpatient hospital environment.

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

PURPOSE OF REVIEW

Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research.

RECENT FINDINGS

In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.

Readiness for implementation of novel digital health interventions for postoperative monitoring: a systematic review and clinical innovation network analysis

An increasing number of digital health interventions (DHIs) for remote postoperative monitoring have been developed and evaluated. This systematic review identifies DHIs for postoperative monitoring and evaluates their readiness for implementation into routine health care. Studies were defined according to idea, development, exploration, assessment, and long-term follow-up (IDEAL) stages of innovation. A novel clinical innovation network analysis used coauthorship and citations to examine collaboration and progression within the field. 126 DHIs were identified, with 101 (80%) being early stage innovations (IDEAL stage 1 and 2a). None of the DHIs identified had large-scale routine implementation. There is little evidence of collaboration, and there are clear omissions in the evaluation of feasibility, accessibility, and the health-care impact. Use of DHIs for postoperative monitoring remains at an early stage of innovation, with promising but generally low-quality supporting evidence. Comprehensive evaluation within high-quality, large-scale trials and real-world data are required to definitively establish readiness for routine implementation.

Outcomes of Vital Sign Monitoring of an Acute Surgical Cohort With Wearable Sensors and Digital Alerting Systems: A Pragmatically Designed Cohort Study and Propensity-Matched Analysis

Background: The implementation and efficacy of wearable sensors and alerting systems in acute secondary care have been poorly described.

Objectives: to pragmatically test one such system and its influence on clinical outcomes in an acute surgical cohort.

Methods: In this pragmatically designed, pre-post implementation trial, participants admitted to the acute surgical unit at our institution were recruited. In the pre-implementation phase (September 2017 to May 2019), the SensiumVitals™ monitoring system, which continuously measures temperature, heart, and respiratory rates, was used for monitoring alongside usual care (intermittent monitoring in accordance with the National Early Warning Score 2 [NEWS 2] protocol) without alerts being generated. In the post-implementation phase (May 2019 to March 2020), alerts were generated when pre-established thresholds for vital parameters were breached, requiring acknowledgement from healthcare staff on provided mobile devices. Hospital length of stay, intensive care use, and 28-days mortality were measured. Balanced cohorts were created with 1:1 ‘optimal’ propensity score logistic regression models.

Results: The 1:1 matching method matched the post-implementation group (n = 141) with the same number of subjects from the pre-implementation group (n = 141). The median age of the entire cohort was 52 (range: 18–95) years and the median duration of wearing the sensor was 1.3 (interquartile range: 0.7–2.0) days. The median alert acknowledgement time was 111 (range: 1–2,146) minutes. There were no significant differences in critical care admission (planned or unplanned), hospital length of stay, or mortality.

Conclusion: This study offered insight into the implementation of digital health technologies within our institution. Further work is required for optimisation of digital workflows, particularly given their more favourable acceptability in the post pandemic era. Clinical trials registration information: ClinicalTrials.gov Identifier: NCT04638738.

Development stage of novel digital health interventions for postoperative monitoring: protocol of a systematic review

Introduction

The postoperative period represents a time where patients are at a high-risk of morbidity, which warrants effective surveillance. While digital health interventions (DHIs) for postoperative monitoring are promising, a coordinated, standardized and evidence-based approach regarding their implementation and evaluation is currently lacking. This study aimed to identify DHIs implemented and evaluated in postoperative care to highlight research gaps and assess the readiness for routine implementation.

Methods

A systematic review will be conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify studies describing the implementation and evaluation of DHIs for postoperative monitoring published since 2000 (PROSPERO ID: CRD42021264289). This will encompass the Embase, Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, Web of Science and ClinicalTrials.gov databases, and manual search of bibliographies for relevant studies and gray literature. Methodological reporting quality will be evaluated using the Idea, Development, Exploration, Assessment and Long-term Follow-up (IDEAL) reporting guideline relevant to the IDEAL stage of the study, and risk of bias will be assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework. Data will be extracted according to the WHO framework for monitoring and evaluating DHIs, and a narrative synthesis will be performed.

Discussion

This review will assess the readiness for implementation of DHIs for routine postoperative monitoring and will include studies describing best practice from service changes already being piloted out of necessity during the COVID-19 pandemic. This will identify interventions with sufficient evidence to progress to the next IDEAL stage, and promote standardized and comprehensive evaluation of future implementational studies.

A natural language processing pipeline to synthesize patient-generated notes toward improving remote care and chronic disease management: a cystic fibrosis case study

Objectives

Patient-generated health data (PGHD) are important for tracking and monitoring out of clinic health events and supporting shared clinical decisions. Unstructured text as PGHD (eg, medical diary notes and transcriptions) may encapsulate rich information through narratives which can be critical to better understand a patient’s condition. We propose a natural language processing (NLP) supported data synthesis pipeline for unstructured PGHD, focusing on children with special healthcare needs (CSHCN), and demonstrate it with a case study on cystic fibrosis (CF).

Materials and Methods

The proposed unstructured data synthesis and information extraction pipeline extract a broad range of health information by combining rule-based approaches with pretrained deep-learning models. Particularly, we build upon the scispaCy biomedical model suite, leveraging its named entity recognition capabilities to identify and link clinically relevant entities to established ontologies such as Systematized Nomenclature of Medicine (SNOMED) and RXNORM. We then use scispaCy’s syntax (grammar) parsing tools to retrieve phrases associated with the entities in medication, dose, therapies, symptoms, bowel movements, and nutrition ontological categories. The pipeline is illustrated and tested with simulated CF patient notes.

Results

The proposed hybrid deep-learning rule-based approach can operate over a variety of natural language note types and allow customization for a given patient or cohort. Viable information was successfully extracted from simulated CF notes. This hybrid pipeline is robust to misspellings and varied word representations and can be tailored to accommodate the needs of a specific patient, cohort, or clinician.

Discussion

The NLP pipeline can extract predefined or ontology-based entities from free-text PGHD, aiming to facilitate remote care and improve chronic disease management. Our implementation makes use of open source models, allowing for this solution to be easily replicated and integrated in different health systems. Outside of the clinic, the use of the NLP pipeline may increase the amount of clinical data recorded by families of CSHCN and ease the process to identify health events from the notes. Similarly, care coordinators, nurses and clinicians would be able to track adherence with medications, identify symptoms, and effectively intervene to improve clinical care. Furthermore, visualization tools can be applied to digest the structured data produced by the pipeline in support of the decision-making process for a patient, caregiver, or provider.

Conclusion

Our study demonstrated that an NLP pipeline can be used to create an automated analysis and reporting mechanism for unstructured PGHD. Further studies are suggested with real-world data to assess pipeline performance and further implications.

Recent Academic Research on Clinically Relevant Digital Measures: Systematic Review

Background

Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, ingestibles, and implantables are increasingly used by individuals and clinicians to capture health outcomes or behavioral and physiological characteristics of individuals. Although academia is taking an active role in evaluating digital sensing products, academic contributions to advancing the safe, effective, ethical, and equitable use of digital clinical measures are poorly characterized.

Objective

We performed a systematic review to characterize the nature of academic research on digital clinical measures and to compare and contrast the types of sensors used and the sources of funding support for specific subareas of this research.

Methods

We conducted a PubMed search using a range of search terms to retrieve peer-reviewed articles reporting US-led academic research on digital clinical measures between January 2019 and February 2021. We screened each publication against specific inclusion and exclusion criteria. We then identified and categorized research studies based on the types of academic research, sensors used, and funding sources. Finally, we compared and contrasted the funding support for these specific subareas of research and sensor types.

Results

The search retrieved 4240 articles of interest. Following the screening, 295 articles remained for data extraction and categorization. The top five research subareas included operations research (research analysis; n=225, 76%), analytical validation (n=173, 59%), usability and utility (data visualization; n=123, 42%), verification (n=93, 32%), and clinical validation (n=83, 28%). The three most underrepresented areas of research into digital clinical measures were ethics (n=0, 0%), security (n=1, 0.5%), and data rights and governance (n=1, 0.5%). Movement and activity trackers were the most commonly studied sensor type, and physiological (mechanical) sensors were the least frequently studied. We found that government agencies are providing the most funding for research on digital clinical measures (n=192, 65%), followed by independent foundations (n=109, 37%) and industries (n=56, 19%), with the remaining 12% (n=36) of these studies completely unfunded.

Conclusions

Specific subareas of academic research related to digital clinical measures are not keeping pace with the rapid expansion and adoption of digital sensing products. An integrated and coordinated effort is required across academia, academic partners, and academic funders to establish the field of digital clinical measures as an evidence-based field worthy of our trust.

Association of a Remote Patient Monitoring (RPM) Program With Reduced Hospitalizations in Cancer Patients With COVID-19

PURPOSE

The goal of this study was to assess the impact of an interdisciplinary remote patient monitoring (RPM) program on clinical outcomes and acute care utilization in cancer patients with COVID-19.

METHODS

This is a cross-sectional analysis following a prospective observational study performed at Mayo Clinic Cancer Center. Adult patients receiving cancer-directed therapy or in recent remission on active surveillance with polymerase chain reaction-confirmed SARS-CoV-2 infection between March 18 and July 31, 2020, were included. RPM was composed of in-home technology to assess symptoms and physiologic data with centralized nursing and physician oversight.

RESULTS

During the study timeframe, 224 patients with cancer were diagnosed with COVID-19. Of the 187 patients (83%) initially managed in the outpatient setting, those who did not receive RPM were significantly more likely to experience hospitalization than those receiving RPM. Following balancing of patient characteristics by inverse propensity score weighting, rates of hospitalization for RPM and non-RPM patients were 2.8% and 13%, respectively, implying that the use of RPM was associated with a 78% relative risk reduction in hospital admission rate (95% CI, 54 to 102; P = .002). Furthermore, when hospitalized, these patients experienced a shorter length of stay and fewer prolonged hospitalizations, intensive care unit admissions, and deaths, although these trends did not reach statistical significance.

CONCLUSION

The use of RPM and a centralized virtual care team was associated with a reduction in hospital admission rate and lower overall acute care resource utilization among cancer patients with COVID-19.

Implementation of a multisite, interdisciplinary remote patient monitoring program for ambulatory management of patients with COVID-19

Established technology, operational infrastructure, and nursing resources were leveraged to develop a remote patient monitoring (RPM) program for ambulatory management of patients with COVID-19. The program included two care-delivery models with different monitoring capabilities supporting variable levels of patient risk for severe illness. The primary objective of this study was to determine the feasibility and safety of a multisite RPM program for management of acute COVID-19 illness. We report an evaluation of 7074 patients served by the program across 41 US states. Among all patients, the RPM technology engagement rate was 78.9%. Rates of emergency department visit and hospitalization within 30 days of enrollment were 11.4% and 9.4%, respectively, and the 30-day mortality rate was 0.4%. A multisite RPM program for management of acute COVID-19 illness is feasible, safe, and associated with a low mortality rate. Further research and expansion of RPM programs for ambulatory management of other acute illnesses are warranted.

IoT-Based Research Equipment Sharing System for Remotely Controlled Two-Photon Laser Scanning Microscopy

In this study, two-photon laser scanning microscopy (TPLSM) based on the internet of things (IoT) is proposed as a remote research equipment sharing system, which enables the remote sharing economy. IoT modules, where data are transmitted to and received from the remote users in the web service via IoT, instead of a data acquisition (DAQ) system embedded in the conventional TPLSM, are installed in the IoT-based TPLSM (IoT-TPLSM). The performance for each IoT module is evaluated independently, and it is confirmed that it works well even in a personal computer-free environment. In addition, a message queuing telemetry transport (MQTT) protocol is applied to the DAQ interface in the web service, and a graphic user interface for enabling the remote users to operate IoT-TPLSM remotely is also designed and implemented. For the image acquisition demonstration, the stained cellular images and the autofluorescent tissue images are obtained in IoT-TPLSM. Lastly, it is confirmed that the comparable performance is provided with the conventional TPLSM by evaluating the imaging conditions and qualities of the three-dimensional image stacks processed in IoT-TPLSM.