The Effect of COVID-19 on Health Care Utilization Among Children with Medical Complexity: Retrospective Chart Review Study.

This study examines the trends, patterns, and potential health disparities in health care utilization among children with medical complexity, before and during COVID pandemic through a retrospective chart review. Our findings show significant differences in the average number of visits per patient over the years and support the adoption of telehealth consultations, while highlighting concerns about demographic disparities.

Community Caregivers' Perspectives on Health IT Use for Children With Medical Complexity: Qualitative Interview Study.

Background: Children with medical complexity represent a unique pediatric population requiring extensive health care needs and care coordination. Children with medical complexities have multiple significant chronic health problems that affect multiple organ systems and result in functional limitations and high health care needs or use. Often, there is a need for medical technology and total care for activities of daily living, much of which is provided at home by family and caregivers.

Relationship Between Heart Rate and Perceived Stress in Intensive Care Unit Residents: Exploratory Analysis Using Fitbit Data.

Background: Intensive care unit (ICU) residents are exposed to high stress levels due to the intense nature of their work, which can impact their mental health and job performance. Heart rate measured through wearable devices has the potential to provide insights into residents' self-reported stress and aid in developing targeted interventions.

Objective: This exploratory study aims to analyze continuous heart rate data and self-reported stress levels and stressors in ICU residents to examine correlations between physiological responses, stress levels, and daily stressors reported.

A Systematic Review on the Use of Consumer-Based ECG Wearables on Cardiac Health Monitoring.

This systematic review aims to summarize the consumer wearable devices used for collecting ECG signals, explore the models or algorithms employed in diagnosing and preventing heart-related diseases through ECG analysis, and discuss the challenges and future work related to adopting health monitoring using consumer wearable devices. Following the PRISMA method, we identified and reviewed 102 relevant papers from PubMed, IEEE, and Web of Science databases, covering the period from May 2013 to May 2023. This review comprehensively summarizes consumer wearable devices with ECG functions, available ECG datasets, and various algorithms for detecting cardiac diseases and monitoring long-term health.

Perceived Penalties for Sharing Patient Beliefs with Health Care Providers.

Background: Health care interactions may require patients to share with a physician information they believe but is incorrect. While a key piece of physicians' work is educating their patients, people's concerns of being seen as uninformed or incompetent by physicians may lead them to think that sharing incorrect health beliefs comes with a penalty. We tested people's perceptions of patients who share incorrect information and how these perceptions vary by the reasonableness of the belief and its centrality to the patient's disease.

Explainable Artificial Intelligence in Quantifying Breast Cancer Factors: Saudi Arabia Context.

Breast cancer represents a significant health concern, particularly in Saudi Arabia, where it ranks as the most prevalent cancer type among women. This study focuses on leveraging eXplainable Artificial Intelligence (XAI) techniques to predict benign and malignant breast cancer cases using various clinical and pathological features specific to Saudi Arabian patients. Six distinct models were trained and evaluated based on common performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC score.

Validating the Effectiveness of the Patient-Centered Cancer Care Framework by Assessing the Impact of Work System Factors on Patient-Centered Care and Quality of Care: Interview Study With Newly Diagnosed Cancer Patients.

Background: Patients with cancer who have recently been diagnosed have distinct requirements compared to cancer survivors. It is crucial to take into account their unique needs to ensure that they make informed decisions and are receptive to the care provided.

Objective: This study suggested a framework titled Effectiveness of Patient-Centered Cancer Care that considers the needs of newly diagnosed patients with cancer and related work system factors.

Shaping the future of chronic disease management: Insights into patient needs for AI-based homecare systems.

Background: The rising demand for healthcare resources, especially in chronic disease management, has elevated the importance of Artificial Intelligence (AI) in healthcare. While AI-based homecare systems are being developed, the perspectives of chronic patients, who are one of the primary beneficiaries and risk bearers of these technologies, remain largely under-researched. While recent research has highlighted the importance of AI-based homecare systems, the current understanding of patients' desired designs and features is still limited.

Perspectives of Patients With Chronic Diseases on Future Acceptance of AI-Based Home Care Systems: Cross-Sectional Web-Based Survey Study.

Background: Artificial intelligence (AI)-based home care systems and devices are being gradually integrated into health care delivery to benefit patients with chronic diseases. However, existing research mainly focuses on the technical and clinical aspects of AI application, with an insufficient investigation of patients' motivation and intention to adopt such systems.

Objective: This study aimed to examine the factors that affect the motivation of patients with chronic diseases to adopt AI-based home care systems and provide empirical evidence for the proposed research hypotheses.

The health information technology preferences and perceptions of newly diagnosed patients with cancer.

Background & Goals: Patients with new cancer diagnoses have unique needs. In this study, we explored the technological needs and preferences of new cancer patients and the challenges to technology use among these patients.

Methods: We used qualitative data from semi-structured interviews to identify the new cancer patients' technology preferences.

Artificial Intelligence-Based Consumer Health Informatics Application: Scoping Review.

Background: There is no doubt that the recent surge in artificial intelligence (AI) research will change the trajectory of next-generation health care, making it more approachable and accessible to patients. Therefore, it is critical to research patient perceptions and outcomes because this trend will allow patients to be the primary consumers of health technology and decision makers for their own health.

Objective: This study aimed to review and analyze papers on AI-based consumer health informatics (CHI) for successful future patient-centered care.

Lung Cancer Screening Decision Aid Designed for a Primary Care Setting: A Randomized Clinical Trial.

Importance: Guidelines recommend shared decision-making prior to initiating lung cancer screening (LCS). However, evidence is lacking on how to best implement shared decision-making in clinical practice.

Objective: To evaluate the impact of an LCS Decision Tool (LCSDecTool) on the quality of decision-making and LCS uptake.

Perceived Patient Workload and Its Impact on Outcomes During New Cancer Patient Visits: Analysis of a Convenience Sample.

Background: Studies exploring the workload in health care focus on the doctors' perspectives. The ecology of the health care environment is critical and different for doctors and patients.

Objective: In this study, we explore the patient workload among newly diagnosed patients with cancer during their first visit and its impact on the patient's perceptions of the quality of care (their trust in their doctors, their satisfaction with the care visits, their perception of technology use).

Multimedia tablet or paper handout to supplement counseling during preterm birth hospitalization: a randomized controlled trial.

Background: Parents of premature infants engage in shared decision-making regarding the care of their infant. The process of prenatal counseling typically involves a verbal conversation with a neonatal provider during hospitalization. Support people may not be available, and the pregnant person's memory is impaired by medications, pain, and stress.

The Impact of Patient-Centered Care on Cancer Patients' QOC, Self-Efficacy, and Trust Towards Doctors: Analysis of a National Survey.

Patient-centered approaches impact cancer patients' perceptions and outcomes in different ways. This study explores the impact of patient-centered care practices on cancer patients' quality-of-care (QOC), self-efficacy, and trust in their doctors. We utilized cross-sectional national survey data from the National Cancer Institute collected between 2017 and 2020.

Using health information technology to support the needs of Children with Medical Complexity: Mapping review of consumer informatics applications.

Background: Children with medical complexity (CMC) are fragile populations that require continuous care and supervision. CMC family caregivers experience many challenges trying to address CMC patients' needs which puts these caregivers in a stressful situation that may negatively impact the care of CMC patients. Consumer informatics might help these caregivers in coordinating care.

Successful application of the innovation process to a case of Floyd Type I tracheal agenesis.

Background: Innovation is broadly defined as the act of introducing a new product, idea, or process. The field of surgery is built upon innovation, revolutionizing technology, science, and tools to improve patient care. While most innovative solutions are aimed at problems with a significant patient population, the process can also be used on orphan pathologies without obvious solutions.

Managing Critical Patient-Reported Outcome Measures in Oncology Settings: System Development and Retrospective Study.

Background: Remote monitoring programs based on the collection of patient-reported outcome (PRO) data are being increasingly adopted in oncology practices. Although PROs are a great source of patient data, the management of critical PRO data is not discussed in detail in the literature.

Objective: This first-of-its-kind study aimed to design, describe, and evaluate a closed-loop alerting and communication system focused on managing PRO-related alerts in cancer care.

Clinicians' Perceptions of an Artificial Intelligence-Based Blood Utilization Calculator: Qualitative Exploratory Study.

Background: According to the US Food and Drug Administration Center for Biologics Evaluation and Research, health care systems have been experiencing blood transfusion overuse. To minimize the overuse of blood product transfusions, a proprietary artificial intelligence (AI)-based blood utilization calculator (BUC) was developed and integrated into a US hospital's electronic health record. Despite the promising performance of the BUC, this technology remains underused in the clinical setting.

Digital Twins for Managing Health Care Systems: Rapid Literature Review.

Background: Although most digital twin (DT) applications for health care have emerged in precision medicine, DTs can potentially support the overall health care process. DTs (twinned systems, processes, and products) can be used to optimize flows, improve performance, improve health outcomes, and improve the experiences of patients, doctors, and other stakeholders with minimal risk.

Objective: This paper aims to review applications of DT systems, products, and processes as well as analyze the potential of these applications for improving health care management and the challenges associated with this emerging technology.

Real-world implementation evaluation of an electronic health record-integrated consumer informatics tool that collects patient-generated contextual data.

Objective: Use the RE-AIM framework to examine the implementation of a patient contextual data (PCD) Tool designed to share patients' needs, values, and preferences with care teams ahead of clinical encounters.

Materials & Methods: Observational study that follows initial PCD Tool scaling across primary care at a Midwestern academic health network. Program invitations, enrollment, patient submissions, and clinician views were tracked over a 1-year study period.

Predictors of Healthcare Professionals' Work Difficulty Perception during the COVID-19 Pandemic: Study of Work Environment in a Pandemic Hospital.

COVID-19 has dramatically changed the work environment in healthcare, which is creating an additional burden for healthcare professionals. In this study, we investigate the factors that trigger professionals to have negative perceptions of their jobs during the pandemic. A cross-sectional survey is used for this study.

Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models.

Chronic diseases have become the most prevalent and costly health conditions in the healthcare industry, deteriorating the quality of life, adversely affecting the work productivity, and costing astounding medical resources. However, few studies have been conducted on the predictive analysis of multiple chronic conditions (MCC) based on the working population. Seven machine learning algorithms are used to support the decision making of healthcare practitioner on the risk of MCC.

A Veteran-Centric Web-Based Decision Aid for Lung Cancer Screening: Usability Analysis.

Background: Web-based tools developed to facilitate a shared decision-making (SDM) process may facilitate the implementation of lung cancer screening (LCS), an evidence-based intervention to improve cancer outcomes. Veterans have specific risk factors and shared experiences that affect the benefits and potential harms of LCS and thus may value a veteran-centric LCS decision tool (LCSDecTool).

Objective: This study aims to conduct usability testing of an LCSDecTool designed for veterans receiving care at a Veteran Affairs medical center.

Anxiety, Burnout and Depression, Psychological Well-Being as Predictor of Healthcare Professionals' Turnover during the COVID-19 Pandemic: Study in a Pandemic Hospital.

Turnover of healthcare professionals' is a rapidly growing human resource issue that affects healthcare systems. During the COVID-19 pandemic, healthcare professionals have faced stressful situations that have negatively impacted their psychological health. In this study, we explored impacts of the emotional wellbeing of healthcare professionals on their intention to quit their jobs.