2017 Roadmap for Innovation-ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health: A Report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

BACKGROUND

 Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.

METHODS

 Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included.

RESULTS

 Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison.

CONCLUSION

 ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.

A Comprehensive Review on Digital Detox: A Newer Health and Wellness Trend in the Current Era

This research investigates the effects of an electronic detox treatment on the utilization of social media and smartphones, addiction levels, and the general health of individuals. Remarkably, individuals discovered that the digital detox was less challenging than anticipated, with a significant number expressing sensations of pleasure and alleviation. Although a few individuals encountered instances of alienation and solitude, the majority managed to adapt to the limited availability of the internet. Notably, individuals saw heightened tedium and replaced their use of social networking sites with additional tasks using screens. After the procedure, measures demonstrated favorable or neutral enhancements in addictions and health-related results. The quantitative findings indicate an increased understanding of online conduct and the use of self-regulating strategies. Concrete recommendations put forward by respondents include reducing stringent deadlines, implementing personalized limitations, and devising tactics to regulate alerts and their use. These observations may be used to shape subsequent digital detox programs in order to improve their efficacy and increase participation from participants.

Unleashing the Potential for Patient-Generated Health Data (PGHD)

Patient-generated health data (PGHD) is data created, captured, or recorded by patients in between healthcare appointments, and is an important supplement to data generated during periodic clinical encounters. PGHD has potential to improve diagnosis and management of chronic conditions, improve health outcomes, and facilitate more "connected health" between patients and their care teams. Electronic PGHD is rapidly accelerating due to the proliferation of consumer health technologies, remote patient monitoring systems, and personal health platforms. Despite this tremendous growth in PGHD and anticipated benefits, broadscale use of PGHD has been challenging to implement with significant gaps in current knowledge about how PGHD can best be employed in the service of high-quality, patient-centered care. While the role of PGHD in patient self-management continues to grow organically, we need a deeper understanding of how data collection and sharing translate into actionable information that supports shared decision-making and informs clinical care in real-world settings. This, in turn, will foster both clinical adoption and patient engagement with PGHD. We propose an agenda for PGHD-related research in the Veterans Health Administration that emphasizes this clinical value to enhance our understanding of its potential and limitations in supporting shared decision-making and informing clinical care.

Advancing the care of individuals with cancer through innovation & technology: Proceedings from the cardiology oncology innovation summit 2020 and 2021

As cancer therapies increase in effectiveness and patients' life expectancies improve, balancing oncologic efficacy while reducing acute and long-term cardiovascular toxicities has become of paramount importance. To address this pressing need, the Cardiology Oncology Innovation Network (COIN) was formed to bring together domain experts with the overarching goal of collaboratively investigating, applying, and educating widely on various forms of innovation to improve the quality of life and cardiovascular healthcare of patients undergoing and surviving cancer therapies. The COIN mission pillars of innovation, collaboration, and education have been implemented with cross-collaboration among academic institutions, private and public establishments, and industry and technology companies. In this report, we summarize proceedings from the first two annual COIN summits (inaugural in 2020 and subsequent in 2021) including educational sessions on technological innovations for establishing best practices and aligning resources. Herein, we highlight emerging areas for innovation and defining unmet needs to further improve the outcome for cancer patients and survivors of all ages. Additionally, we provide actionable suggestions for advancing innovation, collaboration, and education in cardio-oncology in the digital era.

Telehealth and Health Equity in Older Adults With Heart Failure: A Scientific Statement From the American Heart Association

Enhancing access to care using telehealth is a priority for improving outcomes among older adults with heart failure, increasing quality of care, and decreasing costs. Telehealth has the potential to increase access to care for patients who live in underresourced geographic regions, have physical disabilities or poor access to transportation, and may not otherwise have access to cardiologists with expertise in heart failure. During the COVID-19 pandemic, access to telehealth expanded, and yet barriers to access, including broadband inequality, low digital literacy, and structural barriers, prevented many of the disadvantaged patients from getting equitable access. Using a health equity lens, this scientific statement reviews the literature on telehealth for older adults with heart failure; provides an overview of structural, organizational, and personal barriers to telehealth; and presents novel interventions that pair telemedicine with in-person services to mitigate existing barriers and structural inequities.

Looking at the fringes of MedTech innovation: a mapping review of horizon scanning and foresight methods

OBJECTIVES

Horizon scanning (HS) is a method used to examine signs of change and may be used in foresight practice. HS methods used for the identification of innovative medicinal products cannot be applied in medical technologies (MedTech) due to differences in development and regulatory processes. The aim of this study is to identify HS and other methodologies used for MedTech foresight in support to healthcare decision-making.

METHOD

A mapping review was performed. We searched bibliographical databases including MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore and Compendex Engineering Village and grey literature sources such as Google, CORE database and the International HTA database. Our searches identified 8888 records. After de-duplication, and manual and automated title, abstracts and full-text screening, 49 papers met the inclusion criteria and were data extracted.

RESULTS

Twenty-five single different methods were identified, often used in combination; of these, only three were novel (appearing only once in the literature). Text mining or artificial intelligence solutions appear as early as 2012, often practised in patent and social media sources. The time horizon used in scanning was not often justified. Some studies regarded experts both as a source and as a method. Literature searching remains one of the most used methods for innovation identification. HS methods were vaguely reported, but often involved consulting with experts and stakeholders.

CONCLUSION

Heterogeneous methodologies, sources and time horizons are used for HS and foresight of MedTech innovation with little or no justification provided for their use. This review revealed an array of known methods being used in combination to overcome the limitations posed by single methods. The review also revealed inconsistency in methods reporting, with a lack of any consensus regarding best practice. Greater transparency in methods reporting and consistency in methods use would contribute to increased output quality to support informed timely decision-making.

Remote Monitoring for Heart Failure Management at Home

Early telemonitoring of weights and symptoms did not decrease heart failure hospitalizations but helped identify steps toward effective monitoring programs. A signal that is accurate and actionable with response kinetics for early re-assessment is required for the treatment of patients at high risk, while signal specifications differ for surveillance of low-risk patients. Tracking of congestion with cardiac filling pressures or lung water content has shown most impact to decrease hospitalizations, while multiparameter scores from implanted rhythm devices have identified patients at increased risk. Algorithms require better personalization of signal thresholds and interventions. The COVID-19 epidemic accelerated transition to remote care away from clinics, preparing for new digital health care platforms to accommodate multiple technologies and empower patients. Addressing inequities will require bridging the digital divide and the deep gap in access to HF care teams, who will not be replaced by technology but by care teams who can embrace it.

Digital health, cardiometabolic disease and ethnicity: an analysis of United Kingdom government policies from 2010 to 2022

Recent health policies in the United Kingdom (UK) and internationally have focussed on digitisation of healthcare. We examined UK policies for evidence of government action addressing health inequalities and digital health, using cardiometabolic disease as an exemplar. Using a systematic search methodology, we identified 87 relevant policy documents published between 2010 and 2022. We found increasing emphasis on digital health, including for prevention, diagnosis and management of cardiometabolic disease. Several policies also focused on tackling health inequalities and improving digital access. The COVID-19 pandemic amplified inequalities. No policies addressed ethnic inequalities in digital health for cardiometabolic disease, despite high prevalence in minority ethnic communities. Our findings suggest that creating opportunities for digital inclusion and reduce longer-term health inequalities, will require future policies to focus on: the heterogeneity of ethnic groups; cross-sectoral disadvantages which contribute to disease burden and digital accessibility; and disease-specific interventions which lend themselves to culturally tailored solutions.

Convolution Neural Network Algorithm for Shockable Arrhythmia Classification Within a Digitally Connected Automated External Defibrillator

Background Diagnosis of shockable rhythms leading to defibrillation remains integral to improving out-of-hospital cardiac arrest outcomes. New machine learning techniques have emerged to diagnose arrhythmias on ECGs. In out-of-hospital cardiac arrest, an algorithm within an automated external defibrillator is the major determinant to deliver defibrillation. This study developed and validated the performance of a convolution neural network (CNN) to diagnose shockable arrhythmias within a novel, miniaturized automated external defibrillator. Methods and Results There were 26 464 single-lead ECGs that comprised the study data set. ECGs of 7-s duration were retrospectively adjudicated by 3 physician readers (N=18 total readers). After exclusions (N=1582), ECGs were divided into training (N=23 156), validation (N=721), and test data sets (N=1005). CNN performance to diagnose shockable and nonshockable rhythms was reported with area under the receiver operating characteristic curve analysis, F1, and sensitivity and specificity calculations. The duration for the CNN to output was reported with the algorithm running within the automated external defibrillator. Internal and external validation analyses included CNN performance among arrhythmias, often mistaken for shockable rhythms, and performance among ECGs modified with noise to mimic artifacts. The CNN algorithm achieved an area under the receiver operating characteristic curve of 0.995 (95% CI, 0.990-1.0), sensitivity of 98%, and specificity of 100% to diagnose shockable rhythms. The F1 scores were 0.990 and 0.995 for shockable and nonshockable rhythms, respectively. After input of a 7-s ECG, the CNN generated an output in 383±29 ms (total time of 7.383 s). The CNN outperformed adjudicators in classifying atrial arrhythmias as nonshockable (specificity of 99.3%-98.1%) and was robust against noise artifacts (area under the receiver operating characteristic curve range, 0.871-0.999). Conclusions We demonstrate high diagnostic performance of a CNN algorithm for shockable and nonshockable rhythm arrhythmia classifications within a digitally connected automated external defibrillator. Registration URL: https://clinicaltrials.gov/ct2/show/NCT03662802; Unique identifier: NCT03662802.

In-ear infrasonic hemodynography with a digital health device for cardiovascular monitoring using the human audiome

Human bodily mechanisms and functions produce low-frequency vibrations. Our ability to perceive these vibrations is limited by our range of hearing. However, in-ear infrasonic hemodynography (IH) can measure low-frequency vibrations (<20 Hz) created by vital organs as an acoustic waveform. This is captured using a technology that can be embedded into wearable devices such as in-ear headphones. IH can acquire sound signals that travel within arteries, fluids, bones, and muscles in proximity to the ear canal, allowing for measurements of an individual's unique audiome. We describe the heart rate and heart rhythm results obtained in time-series analysis of the in-ear IH data taken simultaneously with ECG recordings in two dedicated clinical studies. We demonstrate a high correlation (r = 0.99) between IH and ECG acquired interbeat interval and heart rate measurements and show that IH can continuously monitor physiological changes in heart rate induced by various breathing exercises. We also show that IH can differentiate between atrial fibrillation and sinus rhythm with performance similar to ECG. The results represent a demonstration of IH capabilities to deliver accurate heart rate and heart rhythm measurements comparable to ECG, in a wearable form factor. The development of IH shows promise for monitoring acoustic imprints of the human body that will enable new real-time applications in cardiovascular health that are continuous and noninvasive.

Interventions Targeted to Address Social Determinants of Health in Ischemic Heart Disease: A Sex- and Gender-Oriented Scoping Review

The burden of ischemic heart disease (IHD) is a major health problem worldwide. The detrimental effect of gendered (ie, unevenly distributed between female and male) socioeconomic determinants of health (SDOH) on outcomes has been demonstrated, more so in female individuals. Therefore, addressing SDOH is a priority for the care implementation of patients with IHD. We conducted a scoping review to identify the types of SDOH-tailored interventions tested in randomised controlled trials (RCTs) among IHD patients, and whether the reporting of findings was sex-unbiased. We identified 8 SDOH domains: education, physical environment, health care system, economic stability, social support, sexual orientation, culture/language, and systemic racism. A total of 28 RCTs (2 ongoing) were evaluated. Since the 1990s, 26 RCTs have been conducted, mainly in the Middle East and Asia, and addressed only education, physical environment, health care system, and social support. The 77% of studies focused on patient-education interventions, and around 80% on SDOH-based interventions achieved positive effects on a variety of primary outcome(s). Among the limitations of the conducted RCTs, the most relevant were an overall low participation of female and racial/ethnical minority participants, a lack of sex-stratified analyses, and a missing opportunity of tailoring some SDOH interventions relevant for health. The SDOH-tailored interventions tested so far in RCTs, enrolling predominantly male patients and mainly targeting education and health literacy, were effective in improving outcomes among patients with IHD. Future studies should focus on a wider range of SDOH with an adequate representation of female and minority patients who would most benefit from such interventions.

Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care

BACKGROUND

Phase space is a mechanical systems approach and large-scale data representation of an object in 3-dimensional space. Whether such techniques can be applied to predict left ventricular pressures non-invasively and at the point-of-care is unknown.

OBJECTIVE

This study prospectively validated a phase space machine-learned approach based on a novel electro-mechanical pulse wave method of data collection through orthogonal voltage gradient (OVG) and photoplethysmography (PPG) for the prediction of elevated left ventricular end diastolic pressure (LVEDP).

METHODS

Consecutive outpatients across 15 US-based healthcare centers with symptoms suggestive of coronary artery disease were enrolled at the time of elective cardiac catheterization and underwent OVG and PPG data acquisition immediately prior to angiography with signals paired with LVEDP (IDENTIFY; NCT #03864081). The primary objective was to validate a ML algorithm for prediction of elevated LVEDP using a definition of ≥25 mmHg (study cohort) and normal LVEDP ≤ 12 mmHg (control cohort), using AUC as the measure of diagnostic accuracy. Secondary objectives included performance of the ML predictor in a propensity matched cohort (age and gender) and performance for an elevated LVEDP across a spectrum of comparative LVEDP (<12 through 24 at 1 mmHg increments). Features were extracted from the OVG and PPG datasets and were analyzed using machine-learning approaches.

RESULTS

The study cohort consisted of 684 subjects stratified into three LVEDP categories, ≤12 mmHg (N = 258), LVEDP 13-24 mmHg (N = 347), and LVEDP ≥25 mmHg (N = 79). Testing of the ML predictor demonstrated an AUC of 0.81 (95% CI 0.76-0.86) for the prediction of an elevated LVEDP with a sensitivity of 82% and specificity of 68%, respectively. Among a propensity matched cohort (N = 79) the ML predictor demonstrated a similar result AUC 0.79 (95% CI: 0.72-0.8). Using a constant definition of elevated LVEDP and varying the lower threshold across LVEDP the ML predictor demonstrated and AUC ranging from 0.79-0.82.

CONCLUSION

The phase space ML analysis provides a robust prediction for an elevated LVEDP at the point-of-care. These data suggest a potential role for an OVG and PPG derived electro-mechanical pulse wave strategy to determine if LVEDP is elevated in patients with symptoms suggestive of cardiac disease.

Effect of medical innovation policies on the prevention and control of the COVID-19 and the impact of the "Belt and Road" economy

With the spread of the COVID-19, it is urgent for everyone to protect themselves. The introduction of the medical innovation policy has also brought certain effects to the prevention and control of the COVID-19. The specific effect will be reflected in the following research. This paper firstly analyzed research results related to medical innovation policy, COVID-19 prevention and control, and the "One Belt, One Road" economy, finding out the content that fits this research, and innovates the research work on this basis. Then, this paper provided a detailed explanation of medical innovation policies, the prevention and control of the COVID-19, and the "One Belt, One Road" economy. Among them, this paper focuses on the "One Belt and One Road," uses the α-convergence model to analyze the economic changes of the "One Belt and One Road," and conducts experimental tests in the medical field. The results have shown that from 2017 to 2019, the average hospitalization expenses paid by the pooled funds were 4986.19, 4997.34, and 4888.60 yuan, respectively.

Connected Health Innovation Research Program (C.H.I.R.P.): A bridge for digital health and wellness in cardiology and oncology

Study objective

Cancer and heart disease are leading causes of mortality, and cardio-oncology is emerging as a new field addressing the cardiovascular toxicities related to cancer and cancer therapy. Interdisciplinary research platforms that incorporate digital health to optimize cardiovascular health and wellness in cancer survivors are therefore needed as we advance in the digital era. Our goal was to develop the Connected Health Innovation Research Program (C.H.I.R.P.) to serve as a foundation for future integration and assessments of adoption and clinical efficacy of digital health tools for cardiovascular health and wellness in the general population and in oncology patients.

Design/setting/participants

Partner companies were identified through the American Medical Association innovation platform, as well as LinkedIn and direct contact by our team. Company leaders met with our team to discuss features of their technology or software. Non-disclosure agreements were signed and data were discussed and obtained for descriptive or statistical analysis.

Results

A suite of companies with technologies focused on wellness, biometrics tracking, audio companions, oxygen saturation, weight trends, sleep patterns, heart rate variability, electrocardiogram patterns, blood pressure patterns, real-time metabolism tracking, instructional video modules, or integration of these technologies into electronic health records was collated. We formed an interdisciplinary research team and established an academia-industry collaborative foundation for connecting patients with wellness digital health technologies.

Conclusions

A suite of software and device technologies accessible to the cardiology and oncology population has been established and will facilitate retrospective, prospective, and case research studies assessing adoption and clinical efficacy of digital health tools in cardiology/oncology.

COVID-19 telehealth preparedness: a cross-sectional assessment of cardiology practices in the USA

Aim: The COVID-19 pandemic forced medical practices to augment healthcare delivery to remote and virtual services. We describe the results of a nationwide survey of cardiovascular professionals regarding telehealth perspectives. Materials & methods: A 31-question survey was sent early in the pandemic to assess the impact of COVID-19 on telehealth adoption & reimbursement. Results: A total of 342 clinicians across 42 states participated. 77% were using telehealth, with the majority initiating usage 2 months after the COVID-19 shutdown. A variety of video-based systems were used. Telehealth integration requirements differed, with electronic medical record integration being mandated in more urban than rural practices (70 vs 59%; p < 0.005). Many implementation barriers surfaced, with over 75% of respondents emphasizing reimbursement uncertainty and concerns for telehealth generalizability given the complexity of cardiovascular diseases. Conclusion: Substantial variation exists in telehealth practices. Further studies and legislation are needed to improve access, reimbursement and the quality of telehealth-based cardiovascular care.

Artificial intelligence in healthcare: a primer for medical education in radiomics

The application of artificial intelligence (AI) to healthcare has garnered significant enthusiasm in recent years. Despite the adoption of new analytic approaches, medical education on AI is lacking. We aim to create a usable AI primer for medical education. We discuss how to generate a clinical question involving AI, what data are suitable for AI research, how to prepare a dataset for training and how to determine if the output has clinical utility. To illustrate this process, we focused on an example of how medical imaging is employed in designing a machine learning model. Our proposed medical education curriculum addresses AI's potential and limitations for enhancing clinicians' skills in research, applied statistics and care delivery.

Research on Digital Technology Use in Cardiology: Bibliometric Analysis

Background

Digital technology uses in cardiology have become a popular research focus in recent years. However, there has been no published bibliometric report that analyzed the corresponding academic literature in order to derive key publishing trends and characteristics of this scientific area.

Objective

We used a bibliometric approach to identify and analyze the academic literature on digital technology uses in cardiology, and to unveil popular research topics, key authors, institutions, countries, and journals. We further captured the cardiovascular conditions and diagnostic tools most commonly investigated within this field.

Methods

The Web of Science electronic database was queried to identify relevant papers on digital technology uses in cardiology. Publication and citation data were acquired directly from the database. Complete bibliographic data were exported to VOSviewer, a dedicated bibliometric software package, and related to the semantic content of titles, abstracts, and keywords. A term map was constructed for findings visualization.

Results

The analysis was based on data from 12,529 papers. Of the top 5 most productive institutions, 4 were based in the United States. The United States was the most productive country (4224/12,529, 33.7%), followed by United Kingdom (1136/12,529, 9.1%), Germany (1067/12,529, 8.5%), China (682/12,529, 5.4%), and Italy (622/12,529, 5.0%). Cardiovascular diseases that had been frequently investigated included hypertension (152/12,529, 1.2%), atrial fibrillation (122/12,529, 1.0%), atherosclerosis (116/12,529, 0.9%), heart failure (106/12,529, 0.8%), and arterial stiffness (80/12,529, 0.6%). Recurring modalities were electrocardiography (170/12,529, 1.4%), angiography (127/12,529, 1.0%), echocardiography (127/12,529, 1.0%), digital subtraction angiography (111/12,529, 0.9%), and photoplethysmography (80/12,529, 0.6%). For a literature subset on smartphone apps and wearable devices, the Journal of Medical Internet Research (20/632, 3.2%) and other JMIR portfolio journals (51/632, 8.0%) were the major publishing venues.

Conclusions

Digital technology uses in cardiology target physicians, patients, and the general public. Their functions range from assisting diagnosis, recording cardiovascular parameters, and patient education, to teaching laypersons about cardiopulmonary resuscitation. This field already has had a great impact in health care, and we anticipate continued growth.

The pursuit of health equity in digital transformation, health informatics, and the cardiovascular learning healthcare system

African Americans have a higher rate of cardiovascular morbidity and mortality and a lower rate of specialty consultation and treatment than Caucasians. These disparities also exist in the care and treatment of chemotherapy-related cardiovascular complications. African Americans suffer from cardiotoxicity at a higher rate than Caucasians and are underrepresented in clinical trials aimed at preventing cardiovascular injury associated with cancer therapies. To eliminate racial and ethnic disparities in the prevention of cardiotoxicity, an interdisciplinary and innovative approach will be required. Diverse forms of digital transformation leveraging health informatics have the potential to contribute to health equity if they are implemented carefully and thoughtfully in collaboration with minority communities. A learning healthcare system can serve as a model for developing, deploying, and disseminating interventions to minimize health inequities and maximize beneficial impact.

Big Data in Cardiology: State-of-Art and Future Prospects

Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted.

Bridging the gap to advance the care of individuals with cancer: collaboration and partnership in the Cardiology Oncology Innovation Network (COIN)

Cardiovascular diseases and cancer continue to be the two leading causes of death in the United States. While innovations in artificial intelligence, digital health, and telemedicine may revolutionize cardio-oncology clinical practice, barriers to widespread adoption continue to exist. The most effective way to advance these technologies is through a broad range of stakeholders sharing a common vision. Additionally, as we enter the digital era in healthcare, we must help lead this charge for the benefit of our cardiology and oncology patients. Bolstering collaborations in cardiology and oncology is key, in partnership with technology firms, industry, academia, and private practice, with an emphasis on various forms of innovation. The ultimate goal is to connect our patients and their health to informatics-based opportunities to advance cardiovascular disease prevention in cancer patients. We have established the Cardiology Oncology Innovation Network in accordance with this vision, to develop new care delivery options through the use of innovative technological strategies. Our tripartite mission - innovation, collaboration, and education - aims to increase access to and expertise in digital transformation to prevent cardiovascular diseases in cancer patients. Here we describe network initiatives, early accomplishments, and future milestones.

Therapeutic components of digital counseling for chronic heart failure

BACKGROUND

Task force statements support the use of cognitive behavioral therapy (CBT) and motivational interviewing (MI) to promote self-care in chronic heart failure (CHF) patients. Digital counseling interventions have the potential to complement conventional programs. However, therapeutic components of digital programs associated with improved outcomes are not clearly established.

OBJECTIVE

Identify therapeutic components of the Canadian e-Platform to Promote Behavioral Self-Management in Chronic Heart Failure (CHF-CePPORT) protocol that were associated with improved health-related quality of life (HRQL).

MATERIALS AND METHODS

Ordinal logistic regression was used to identify therapeutic components of the CHF-CePPORT protocol. The primary outcome was the 12-month Kansas City Cardiomyopathy Questionnaire Overall Summary (KCCQ-OS) tertile. Logistic regressions determined the association between 12-month KCCQ-OS tertile, using logon hours for key segments of the protocol, modality of content delivery, and clinical themes.

RESULTS

A total of 117 patients were enrolled in the e-Counseling arm of the CHF-CePPORT trial. Median age was 60 years (IQR 52-69). Total logon hours in the initial 4-month segment of CHF-CePPORT (Sessions 1-16) was associated with increased 12-month KCCQ-OS tertile (Odds Ratio, OR = 1.31, 95% CI, 1.1-1.5, P = 0.001). Within sessions 1-16, improved KCCQ-OS was associated with logon hours for self-assessment tools/trackers (OR = 1.49, 95% CI, 1.1-2.0, P = 0.007), and videos (OR = 1.57, 95% CI, 1.03-2.4, P = 0.04), but not for CHF information pages.

CONCLUSION

This study highlights the importance of using evidence-based guidelines from CBT and MI as core components of digital counseling, delivered through videos and interactive tools/trackers, to improve HRQL with CHF.

Falling Through the Cracks: The Current Gap in the Health Care Transition of Patients With Kawasaki Disease: A Scientific Statement From the American Heart Association

Background

Health care transition (HCT) is a period of high vulnerability for patients with chronic childhood diseases, particularly when patients shift from a pediatric to an adult care setting. An increasing number of patients with Kawasaki disease (KD) who develop medium and large coronary artery aneurysms (classified by the American Heart Association according to maximal internal coronary artery diameter Z‐scores ≥5 and ≥10, respectively) are becoming adults and thus undergoing an HCT. However, a poor transition to an adult provider represents a risk of loss to follow‐up, which can result in increasing morbidity and mortality.

Methods and Results

This scientific statement provides a summary of available literature and expert opinion pertaining to KD and HCT of children as they reach adulthood. The statement reviews the existing life‐long risks for patients with KD, explains current guidelines for long‐term care of patients with KD, and offers guidance on assessment and preparation of patients with KD for HCT. The key element to a successful HCT, enabling successful transition outcomes, is having a structured intervention that incorporates the components of planning, transfer, and integration into adult care. This structured intervention can be accomplished by using the Six Core Elements approach that is recommended by the American Academy of Pediatrics, the American Academy of Family Physicians, and the American College of Physicians.

Conclusions

Formal HCT programs for patients with KD who develop aneurysms should be established to ensure a smooth transition with uninterrupted medical care as these youths become adults.

Cost-Comparison Analysis of a Physician-Delivered Step-Count Prescription Strategy

Background

Increments of 1000 steps/d predict cardiovascular disease (CVD) event reductions. In adults with type 2 diabetes and/or hypertension, our Step Monitoring to Improve Arterial Health (SMARTER) trial demonstrated a physician-delivered step-count prescription strategy to increase steps by more than this amount over 1 year, compared to usual care. In the present analysis, we aimed to determine the costs of the intervention compared to usual care, incorporating 1-year intervention costs and projected savings from lower CVD hospitalizations over the subsequent 5 years.

Methods

We considered Canadians aged 55 to 74 years with type 2 diabetes and/or hypertension. Using time estimates from our trial, we computed nursing costs corresponding to patient support time over 1 year, and pedometer costs for an anticipated 50% of patients without a smartphone. We estimated the number of CVD hospitalizations, the reduction expected with a mean 1000 steps/d increase, and the associated savings. We calculated the net cost (savings), the proportion of patients with their own device required for cost neutrality, and costs (savings) if all patients needed to be provided with a device.

Results

At an average intervention cost of $51.28/patient, the total cost would be $168 million. With an estimated 8875 CVD events prevented, $208 million would be saved. This savings would result in ~$40 million in net savings with 50% device ownership, cost neutrality with 25% device ownership, and ~$42 million in net costs if all patients required the healthcare system to provide a device.

Conclusions

At current levels of smartphone ownership, adoption of the SMARTER strategy is cost-saving to cost-neutral from the healthcare system perspective.

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

Efficacy of IVRS-based mHealth intervention in reducing cardiovascular risk in metabolic syndrome: A cluster randomized trial

AIMS

Efficacy of mobile-phone based intervention for reducing cardiovascular risk in metabolic syndrome (MetSyn).

METHODS

We screened adults 20-60 years in 10 villages in India for MetSyn using stratified cluster sampling. Lifestyle and biochemical risk factors were assessed. International Harmonized Criteria were used for diagnosis. Villages were randomized with 5 each in control and intervention groups. Interactive voice response system (IVRS) in Hindi was developed. In intervention clusters two messages for promotion of healthy lifestyle and medical treatment were broadcast daily over 12-months and risk factors reassessed.

RESULTS

1012/1200(84%) persons were screened and MetSyn diagnosed in 286(28.3%). Villages were divided into 5 control(n = 136) and 5 intervention(n = 147) clusters. Baseline characteristics in both clusters were similar. Acceptability of intervention was >60% in 80% participants. At 12 months, significantly greater participants in intervention vs control clusters had healthier lifestyle (healthy diet 28.8vs14.7%, physical activity 25.9vs13.1%, tobacco 13.7vs32.5%), anthropometry (waist circumference 85.7 ± 6.3vs88.6 ± 14.0 cm, body mass index 21.9 ± 2.8vs23.1 ± 2.9 kg/m²), systolic BP 123.6 ± 7.7vs128.6 ± 14.1 mmHg, fasting glucose 95.6 ± 19.4vs109.4 ± 43.7 mg/dl, cholesterol 175.5 ± 36.5vs186.4 ± 43.3 mg/dl, and triglycerides 147.6 ± 48.3vs159.5 ± 60.7 mg/dl (p < 0.01). Prevalence of metabolic syndrome declined in intervention group by 22.3%vs3.9%, p < 0.001).

CONCLUSION

An interactive voice response system based technology significantly reduced multiple cardiovascular risk factors and prevalence of metabolic syndrome.

The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees

Big data is rapidly being seen as a new frontier for improving organizational performance. However, it is still in its early phases of implementation in developing countries’ healthcare organizations. As data-driven insights become critical competitive advantages, it is critical to ascertain which elements influence an organization’s decision to adopt big data. The aim of this study is to propose and empirically test a theoretical framework based on technology–organization–environment (TOE) factors to identify the level of readiness of big data adoption in developing countries’ healthcare organizations. The framework empirically tested 302 Malaysian healthcare employees. The structural equation modeling was used to analyze the collected data. The results of the study demonstrated that technology, organization, and environment factors can significantly contribute towards big data adoption in healthcare organizations. However, the complexity of technology factors has shown less support for the notion. For technology practitioners, this study showed how to enhance big data adoption in healthcare organizations through TOE factors.

Technology assessment framework for precision health applications

OBJECTIVE

Established and emerging technologies-such as wearable sensors, smartphones, mobile apps, and artificial intelligence-are shaping positive healthcare models and patient outcomes. These technologies have the potential to become precision health (PH) innovations. However, not all innovations meet regulatory standards or have the required scientific evidence to be used for health applications. In response, an assessment framework was developed to facilitate and standardize the assessment of innovations deemed suitable for PH.

METHODS

A scoping literature review undertaken through PubMed and Google Scholar identified approximately 100 relevant articles. These were then shortlisted (n = 12) to those that included specific metrics, criteria, or frameworks for assessing technologies that could be applied to the PH context.

RESULTS

The proposed framework identified nine core criteria with subcriteria and grouped them into four categories for assessment: technical, clinical, human factors, and implementation. Guiding statements with response options and recommendations were used as metrics against each criterion.

CONCLUSION

The proposed framework supports health services, health technology innovators, and researchers in leveraging current and emerging technologies for PH innovations. It covers a comprehensive set of criteria as part of the assessment process of these technologies.

Lifespan Perspective on Congenital Heart Disease Research: JACC State-of-the-Art Review

More than 90% of patients with congenital heart disease (CHD) are nowadays surviving to adulthood and adults account for over two-thirds of the contemporary CHD population in Western countries. Although outcomes are improved, surgery does not cure CHD. Decades of longitudinal observational data are currently motivating a paradigm shift toward a lifespan perspective and proactive approach to CHD care. The aim of this review is to operationalize these emerging concepts by presenting new constructs in CHD research. These concepts include long-term trajectories and a life course epidemiology framework. Focusing on a precision health, we propose to integrate our current knowledge on the genome, phenome, and environome across the CHD lifespan. We also summarize the potential of technology, especially machine learning, to facilitate longitudinal research by embracing big data and multicenter lifelong data collection.

Digital Health Interventions for Cardiac Rehabilitation: Systematic Literature Review

Background

Cardiovascular disease (CVD) is the leading cause of death worldwide. Despite strong evidence supporting the benefits of cardiac rehabilitation (CR), over 80% of eligible patients do not participate in CR. Digital health technologies (ie, the delivery of care using the internet, wearable devices, and mobile apps) have the potential to address the challenges associated with traditional facility-based CR programs, but little is known about the comprehensiveness of these interventions to serve as digital approaches to CR. Overall, there is a lack of a systematic evaluation of the current literature on digital interventions for CR.

Objective

The objective of this systematic literature review is to provide an in-depth analysis of the potential of digital health technologies to address the challenges associated with traditional CR. Through this review, we aim to summarize the current literature on digital interventions for CR, identify the key components of CR that have been successfully addressed through digital interventions, and describe the gaps in research that need to be addressed for sustainable and scalable digital CR interventions.

Methods

Our strategy for identifying the primary literature pertaining to CR with digital solutions (defined as technology employed to deliver remote care beyond the use of the telephone) included a consultation with an expert in the field of digital CR and searches of the PubMed (MEDLINE), Embase, CINAHL, and Cochrane databases for original studies published from January 1990 to October 2018.

Results

Our search returned 31 eligible studies, of which 22 were randomized controlled trials. The reviewed CR interventions primarily targeted physical activity counseling (31/31, 100%), baseline assessment (30/31, 97%), and exercise training (27/31, 87%). The most commonly used modalities were smartphones or mobile devices (20/31, 65%), web-based portals (18/31, 58%), and email-SMS (11/31, 35%). Approximately one-third of the studies addressed the CR core components of nutrition counseling, psychological management, and weight management. In contrast, less than a third of the studies addressed other CR core components, including the management of lipids, diabetes, smoking cessation, and blood pressure.

Conclusions

Digital technologies have the potential to increase access and participation in CR by mitigating the challenges associated with traditional, facility-based CR. However, previously evaluated interventions primarily focused on physical activity counseling and exercise training. Thus, further research is required with more comprehensive CR interventions and long-term follow-up to understand the clinical impact of digital interventions.

Innovation in Cardiology: The ACC Innovation Program

The last half century has seen extraordinary advances in the field of cardiology, including innovations in medications, diagnostic modalities, and therapeutics. Even so, cardiovascular disease remains the leading cause of morbidity and mortality globally, with suboptimal quality of care, inconsistent health outcomes, and unsustainable costs. It is clear that cardiovascular medicine must undergo a digital transformation to enhance the delivery of quality care and to improve outcomes. To meet this need, the American College of Cardiology developed an innovation program focused on the digital transformation of cardiovascular care, with the goal of improving heart health for individuals and populations.

Digital Health: Opportunities and Challenges to Develop the Next-Generation Technology-Enabled Models of Cardiovascular Care

The wide gap between the development of new healthcare technologies and their integration into clinical practice argues for a deeper understanding of how effective quality improvement can be designed to meet the needs of patients and their clinical teams. The COVID-19 pandemic has forced us to address this gap and create long-term strategies to bridge it. On the one hand, it has enabled the rapid implementation of telehealth. On the other hand, it has raised important questions about our preparedness to adopt and employ new digital tools as part of a new process of care. While healthcare organizations are seeking to improve the quality of care by integrating innovations in digital health, they must also address key issues such as patient experience, develop clinical decision support systems that analyze digital health data trends, and create efficient clinical workflows. Given the breadth of such requirements, embracing new technologies as a core competency of a modern healthcare system introduces a host of questions, such as "How best do patients participate in digital health programs that promote behavioral changes and mitigate risk?" and "What type of data analytics are required that enable a deeper understanding of disease phenotypes and corresponding treatment decisions?" This review presents the challenges in implementing digital health technology and discusses how patient-centered digital health programs are designed within real-world models of remote monitoring. It also provides a framework for developing new devices and wearables for the next generation of data-driven, technology-enabled cardiovascular care.

Automated E-Counseling for Chronic Heart Failure: CHF-CePPORT Trial

BACKGROUND

International task force statements advocate telehealth programs to promote health-related quality of life for patients with chronic heart failure (CHF). To that end, we evaluated the efficacy and usability of an automated e-counseling program.

METHODS

This Canadian multi-site double-blind randomized trial assessed whether usual care plus either internet-based e-counseling (motivational and cognitive-behavioral tools for CHF self-care) or e-based conventional CHF self-care education (e-UC) improved 12-month Kansas City Cardiomyopathy Questionnaire Overall Summary (KCCQ-OS). Secondary outcomes included program engagement (total logon weeks, logons, and logon hours), total CHF self-care behaviors, diet (fruit and vegetable servings), 6-minute walk test, and 4-day step count. The association between program engagement and health-related quality of life was assessed using KCCQ-OS tertiles.

RESULTS

We enrolled 231 patients, median age =59.5 years, 22% female, and elevated median KCCQ-OS=83.0 (interquartile range, 68-93). KCCQ-OS increase ≥5 points was not more prevalent for e-counseling, n=29 (29.6%) versus e-UC, n=32 (34.0%), P=0.51. E-Counseling versus e-UC increased total logon weeks (P=0.02), logon hours (P=0.001), and logons (P<0.001). Only e-counseling showed a positive association between 12-month KCCQ-OS tertile and logon weeks (P=0.04) and logon hours (P=0.004). E-Counseling increased CHF self-care behavior and diet but not 6-minute walk test or 4-day step count.

CONCLUSIONS

The primary KCCQ-OS end point was negative for this trial. Only e-counseling showed a positive association between program engagement and 12-month KCCQ-OS tertile, and it improved CHF self-care behavior and diet. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01864369.

Digital Health Applications in Heart Failure: a Critical Appraisal of Literature

Purpose of the review

Despite advancements in the diagnostic and therapeutic armamentarium, heart failure (HF) remains a major public health concern in the USA and worldwide. Digital health applications hold promise to bridge this gap and improve HF care. This review will provide the reader with a concise overview of the current digital health applications in HF, the main challenges to its use, and discuss the future of digital health for promoting care for HF patients.

Recent findings

Emerging evidence continues to support the potential role of digital health across the continuum of HF disease process including primary prevention, early detection, disease management, and reducing associated morbidity. There is also increasing emphasis on the need to pursue rigorous investigations to validate these promising claims, with some successful stories that have changed clinical practices.

Summary

Digital health technologies have emerged as potentially useful tools to complement HF care in both research and clinical realms. As digital technologies continue to play an increasing role in transforming healthcare delivery, creating the framework for its effective use would be necessary to ensure that digital health applications consistently improve outcomes and enhance care for HF patients.

Mobile Technologies to Promote Physical Activity during Cardiac Rehabilitation: A Scoping Review

Promoting regular physical activity (PA) and improving exercise capacity are the primary goals of cardiac rehabilitation (CR). Mobile technologies (mTechs) like smartphones, smartwatches, and fitness trackers might help patients in reaching these goals. This review aimed to scope current scientific literature on mTechs in CR to assess the impact on patients’ exercise capacity and to identify gaps and future directions for research. PubMed, CENTRAL, and CDSR were systematically searched for randomized controlled trials (RCTs). These RCTs had to utilize mTechs to objectively monitor and promote PA of patients during or following CR, aim at improvements in exercise capacity, and be published between December 2014 and December 2019. A total of 964 publications were identified, and 13 studies met all inclusion criteria. Home-based CR with mTechs vs. outpatient CR without mTechs and outpatient CR with mTechs vs. outpatient CR without mTechs did not lead to statistically significant differences in exercise capacity. In contrast, outpatient CR followed by home-based CR with mTechs led to significant improvement in exercise capacity as compared to outpatient CR without further formal CR. Supplying patients with mTechs may improve exercise capacity. To ensure that usage of and compliance with mTechs is optimal, a concentrated effort of CR staff has to be achieved. The COVID-19 pandemic has led to an unprecedented lack of patient support while away from institutional CR. Even though mTechs lend themselves as suitable assistants, evidence is lacking that they can fill this gap.

Gerotechnology for Older Adults With Cardiovascular Diseases: JACC State-of-the-Art Review

The growing population of older adults (age ≥65 years) is expected to lead to higher rates of cardiovascular disease. The expansion of digital health (encompassing telehealth, telemedicine, mobile health, and remote patient monitoring), Internet access, and cellular technologies provides an opportunity to enhance patient care and improve health outcomes-opportunities that are particularly relevant during the current coronavirus disease-2019 pandemic. Insufficient dexterity, visual impairment, and cognitive dysfunction, found commonly in older adults should be taken into consideration in the development and utilization of existing technologies. If not implemented strategically and appropriately, these can lead to inequities propagating digital divides among older adults, across disease severities and socioeconomic distributions. A systematic approach, therefore, is needed to study and implement digital health strategies in older adults. This review will focus on current knowledge of the benefits, barriers, and use of digital health in older adults for cardiovascular disease management.

The Future of Wearables in Heart Failure Patients

The adoption of mobile health (mHealth) devices is creating a unique opportunity to improve heart failure (HF) care. The rise of mHealth is driven by multiple factors including consumerism, policy changes in health care, and innovations in technology. Wearable health devices are one aspect of mHealth that may improve the delivery of HF care by allowing for medical data collection outside of a clinician's office or hospital. Wearable devices are externally applied and capture functional or physiological data in order to monitor and improve patients' health. Most wearable sensors capture data continuously and may be incorporated into accessories (e.g., a watch or clothing) or may be applied as a cutaneous patch. Wearable devices are often paired with another device, such as a smartphone, to collect, interpret, or transmit data. This study assessed the potential applications of wearable devices in HF care, summarizes available data for wearables, and discusses the future of wearables for improving the health of patients with HF.

Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning

In this review, we aim to assess the current state of science in relation to the integration of patient-generated health data (PGHD) and patient-reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We will also describe the critical role of artificial intelligence and machine-learning methodology in the efficient translation of PGHD, PROs, and traditional outcome measures into meaningful patient care models.

Toward precision health: applying artificial intelligence analytics to digital health biometric datasets

The rapid development of digital health devices has enabled patients to engage in their care to an unprecedented degree and holds the possibility of significantly improving the diagnosis, treatment and monitoring of many medical conditions. Combined with the emergence of artificial intelligence algorithms, biometric datasets produced from these digital health devices present new opportunities to create precision-based, personalized approaches for healthcare delivery. For effective implementation of such innovations to patient care, clinicians will require an understanding of the types of datasets produced from digital health technologies; the types of analytic methods including feature selection, convolution neural networking, and deep learning that can be used to analyze digital data; and how the interpretation of these findings are best translated to patient care. In this perspective, we aim to provide the groundwork for clinicians to be able to apply artificial intelligence to this transformation of healthcare.

[Artificial intelligence in vascular surgery and vascular medicine]

New digital technologies will also gain in importance in vascular surgery. There is a wide field of potential applications. Simulation-based training of endovascular procedures can lead to improvement in procedure-specific parameters and reduce fluoroscopy and procedural times. The use of intraoperative image-guided navigation and robotics also enables a reduction of the radiation dose. Artificial intelligence can be used for risk stratification and individualization of treatment approaches. Health apps can be used to improve the follow-up care of patients.