Recent Articles
![Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study Article Thumbnail](https://asset.jmir.pub/assets/e399f301ed15c3696aaf12ef7cc73c42.png 480w,https://asset.jmir.pub/assets/e399f301ed15c3696aaf12ef7cc73c42.png 960w,https://asset.jmir.pub/assets/e399f301ed15c3696aaf12ef7cc73c42.png 1920w,https://asset.jmir.pub/assets/e399f301ed15c3696aaf12ef7cc73c42.png 2500w)
Patients with heart failure (HF) are the most commonly readmitted group of adult patients in Germany. Most patients with HF are readmitted for noncardiovascular reasons. Understanding the relevance of HF management outside the hospital setting is critical to understanding HF and factors that lead to readmission. Application of machine learning (ML) on data from statutory health insurance (SHI) allows the evaluation of large longitudinal data sets representative of the general population to support clinical decision-making.
![Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study Article Thumbnail](https://asset.jmir.pub/assets/cccf780f8c48e4c34eb1f04fe3dd582a.png 480w,https://asset.jmir.pub/assets/cccf780f8c48e4c34eb1f04fe3dd582a.png 960w,https://asset.jmir.pub/assets/cccf780f8c48e4c34eb1f04fe3dd582a.png 1920w,https://asset.jmir.pub/assets/cccf780f8c48e4c34eb1f04fe3dd582a.png 2500w)
Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients.
![Persuasive Systems Design Trends in Coronary Heart Disease Management: Scoping Review of Randomized Controlled Trials Article Thumbnail](https://asset.jmir.pub/assets/d340dfc5740eb8184a8e693fe2749122.png 480w,https://asset.jmir.pub/assets/d340dfc5740eb8184a8e693fe2749122.png 960w,https://asset.jmir.pub/assets/d340dfc5740eb8184a8e693fe2749122.png 1920w,https://asset.jmir.pub/assets/d340dfc5740eb8184a8e693fe2749122.png 2500w)
Behavior change support systems (BCSSs) have the potential to help people maintain healthy lifestyles and aid in the self-management of coronary heart disease (CHD). The Persuasive Systems Design (PSD) model is a framework for designing and evaluating systems designed to support lifestyle modifications and health behavior change using information and communication technology. However, evidence for the underlying design principles behind BCSSs for CHD has not been extensively reported in the literature.
![The Effect of an AI-Based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension: Single-Arm Nonrandomized Trial Article Thumbnail](https://asset.jmir.pub/assets/4c3428dfa3a35d770a4d88c6f52d085f.png 480w,https://asset.jmir.pub/assets/4c3428dfa3a35d770a4d88c6f52d085f.png 960w,https://asset.jmir.pub/assets/4c3428dfa3a35d770a4d88c6f52d085f.png 1920w,https://asset.jmir.pub/assets/4c3428dfa3a35d770a4d88c6f52d085f.png 2500w)
Home blood pressure (BP) monitoring with lifestyle coaching is effective in managing hypertension and reducing cardiovascular risk. However, traditional manual lifestyle coaching models significantly limit availability due to high operating costs and personnel requirements. Furthermore, the lack of patient lifestyle monitoring and clinician time constraints can prevent personalized coaching on lifestyle modifications.
![Cognitive Behavioral Therapy for Symptom Preoccupation Among Patients With Premature Ventricular Contractions: Nonrandomized Pretest-Posttest Study Article Thumbnail](https://asset.jmir.pub/assets/1aa129e75498d78787a541bc81a41fea.png 480w,https://asset.jmir.pub/assets/1aa129e75498d78787a541bc81a41fea.png 960w,https://asset.jmir.pub/assets/1aa129e75498d78787a541bc81a41fea.png 1920w,https://asset.jmir.pub/assets/1aa129e75498d78787a541bc81a41fea.png 2500w)
Premature ventricular contractions (PVCs) are a common cardiac condition often associated with disabling symptoms and impaired quality of life (QoL). Current treatment strategies have limited effectiveness in reducing symptoms and restoring QoL for patients with PVCs. Symptom preoccupation, involving cardiac-related fear, hypervigilance, and avoidance behavior, is associated with disability in other cardiac conditions and can be effectively targeted by cognitive behavioral therapy (CBT).
![Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data Article Thumbnail](https://asset.jmir.pub/assets/6319588d12037ee5cddb46a948df6a1c.png 480w,https://asset.jmir.pub/assets/6319588d12037ee5cddb46a948df6a1c.png 960w,https://asset.jmir.pub/assets/6319588d12037ee5cddb46a948df6a1c.png 1920w,https://asset.jmir.pub/assets/6319588d12037ee5cddb46a948df6a1c.png 2500w)
Cardiovascular conditions (eg, cardiac and coronary conditions, hypertensive disorders of pregnancy, and cardiomyopathies) were the leading cause of maternal mortality between 2017 and 2019. The United States has the highest maternal mortality rate of any high-income nation, disproportionately impacting those who identify as non-Hispanic Black or Hispanic. Novel clinical approaches to the detection and diagnosis of cardiovascular conditions are therefore imperative. Emerging research is demonstrating that machine learning (ML) is a promising tool for detecting patients at increased risk for hypertensive disorders during pregnancy. However, additional studies are required to determine how integrating ML and big data, such as electronic health records (EHRs), can improve the identification of obstetric patients at higher risk of cardiovascular conditions.
![A Multidisciplinary Assessment of ChatGPT’s Knowledge of Amyloidosis: Observational Study Article Thumbnail](https://asset.jmir.pub/assets/2c96de2e675a47f9ca5e395b7f244f32.png 480w,https://asset.jmir.pub/assets/2c96de2e675a47f9ca5e395b7f244f32.png 960w,https://asset.jmir.pub/assets/2c96de2e675a47f9ca5e395b7f244f32.png 1920w,https://asset.jmir.pub/assets/2c96de2e675a47f9ca5e395b7f244f32.png 2500w)
Amyloidosis, a rare multisystem condition, often requires complex, multidisciplinary care. Its low prevalence underscores the importance of efforts to ensure the availability of high-quality patient education materials for better outcomes. ChatGPT (OpenAI) is a large language model powered by artificial intelligence that offers a potential avenue for disseminating accurate, reliable, and accessible educational resources for both patients and providers. Its user-friendly interface, engaging conversational responses, and the capability for users to ask follow-up questions make it a promising future tool in delivering accurate and tailored information to patients.
![Association of Arterial Stiffness With Mid- to Long-Term Home Blood Pressure Variability in the Electronic Framingham Heart Study: Cohort Study Article Thumbnail](https://asset.jmir.pub/assets/80661c9d93c299807b708b01ca4f79c8.png 480w,https://asset.jmir.pub/assets/80661c9d93c299807b708b01ca4f79c8.png 960w,https://asset.jmir.pub/assets/80661c9d93c299807b708b01ca4f79c8.png 1920w,https://asset.jmir.pub/assets/80661c9d93c299807b708b01ca4f79c8.png 2500w)
![Cardiac Rehabilitation During the COVID-19 Pandemic and the Potential for Digital Technology to Support Physical Activity Maintenance: Qualitative Study Article Thumbnail](https://asset.jmir.pub/assets/17b0dac0bea6679045475f0da779e5bc.png 480w,https://asset.jmir.pub/assets/17b0dac0bea6679045475f0da779e5bc.png 960w,https://asset.jmir.pub/assets/17b0dac0bea6679045475f0da779e5bc.png 1920w,https://asset.jmir.pub/assets/17b0dac0bea6679045475f0da779e5bc.png 2500w)
Social distancing from the COVID-19 pandemic may have decreased engagement in cardiac rehabilitation (CR) and may have had possible consequences on post-CR exercise maintenance. The increased use of technology as an adaptation may benefit post-CR participants via wearables and social media. Thus, we sought to explore the possible relationships of both the pandemic and technology on post-CR exercise maintenance.
![Cloud-Based Machine Learning Platform to Predict Clinical Outcomes at Home for Patients With Cardiovascular Conditions Discharged From Hospital: Clinical Trial Article Thumbnail](https://asset.jmir.pub/assets/4fcfd6ae4144896d40ae3f20feb77daa.png 480w,https://asset.jmir.pub/assets/4fcfd6ae4144896d40ae3f20feb77daa.png 960w,https://asset.jmir.pub/assets/4fcfd6ae4144896d40ae3f20feb77daa.png 1920w,https://asset.jmir.pub/assets/4fcfd6ae4144896d40ae3f20feb77daa.png 2500w)
Hospitalizations account for almost one-third of the US $4.1 trillion health care cost in the United States. A substantial portion of these hospitalizations are attributed to readmissions, which led to the establishment of the Hospital Readmissions Reduction Program (HRRP) in 2012. The HRRP reduces payments to hospitals with excess readmissions. In 2018, >US $700 million was withheld; this is expected to exceed US $1 billion by 2022. More importantly, there is nothing more physically and emotionally taxing for readmitted patients and demoralizing for hospital physicians, nurses, and administrators. Given this high uncertainty of proper home recovery, intelligent monitoring is needed to predict the outcome of discharged patients to reduce readmissions. Physical activity (PA) is one of the major determinants for overall clinical outcomes in diabetes, hypertension, hyperlipidemia, heart failure, cancer, and mental health issues. These are the exact comorbidities that increase readmission rates, underlining the importance of PA in assessing the recovery of patients by quantitative measurement beyond the questionnaire and survey methods.
![Factors That Influence Patient Satisfaction With the Service Quality of Home-Based Teleconsultation During the COVID-19 Pandemic: Cross-Sectional Survey Study Article Thumbnail](https://asset.jmir.pub/assets/a029446b7e7ff3e2aff939167ef10cc3.png 480w,https://asset.jmir.pub/assets/a029446b7e7ff3e2aff939167ef10cc3.png 960w,https://asset.jmir.pub/assets/a029446b7e7ff3e2aff939167ef10cc3.png 1920w,https://asset.jmir.pub/assets/a029446b7e7ff3e2aff939167ef10cc3.png 2500w)
Ontario stroke prevention clinics primarily held in-person visits before the COVID-19 pandemic and then had to shift to a home-based teleconsultation delivery model using telephone or video to provide services during the pandemic. This change may have affected service quality and patient experiences.