JMIR Formative Research

Blood pressure and hemoglobin concentration measurements are essential components of preoperative anesthetic evaluation. Remote photoplethysmography is an emerging technology that may be used to measure blood pressure and hemoglobin concentration noninvasively with just a consumer grade smartphone, replacing traditional in-person measurements. However, there is limited data regarding the use of this technology in patients with diverse skin tones and medical comorbidities. Hence, widespread applicability is yet to be achieved. The potential benefits of achieving this would be immense, allowing for greater convenience, accessibility and reduction in labor and resources.

Conventional nystagmus classification methods often rely on subjective observation by specialists, which is time-consuming and variable among clinicians. Recently, deep learning techniques have been employed to automate nystagmus classification using convolutional and recurrent neural networks. These networks can accurately classify nystagmus patterns using video data. However, associated challenges include the need for large datasets when creating models, the fact that they only address specific image conditions, and the complexity associated with utilizing the models.

While mobile health applications (mHealth apps) have been made for various diseases, including sickle cell disease (SCD), most focus on a single purpose. SCD is a chronic disease that requires knowledge of the disease, self-management, and adherence to treatment plans. While mHealth apps have been made with single features for SCD, there is limited understanding of using a mHealth app with a more comprehensive set of features that could engage adults with SCD depending on what features they prefer and need to engage and empower them in their disease.

The primary aim of genetic counseling at a human genetics center is to empower individuals at risk for hereditary diseases to make informed decisions regarding their health. In Germany, genetic counseling sessions typically last approximately one hour and provide highly personalized information by a specialist in human genetics. Despite this, many counselees report a need for additional support following the counseling session.

Collaborative Creativity (CC) is a social process of generating creative and innovative solutions to real-world problems through collective effort and interaction. By engaging in this process, medical students can develop abilities and mindset for creative thinking, teamwork, interdisciplinary learning, complex problem-solving, and enhanced patient care. However, medical students have demonstrated limited creativity, constrained by existing pedagogical approaches that predominantly emphasize knowledge outcomes. The increasing complexity of healthcare challenges necessitates a pedagogical framework for medical students to foster collaborative creativity in a rapidly evolving professional environment.

Nurse scheduling is a complex challenge in healthcare, impacting both patient care quality and nurse well-being. Traditional scheduling methods often fail to consider individual preferences, leading to dissatisfaction, burnout, and high turnover. Inadequate scheduling practices, including restricted autonomy and lack of transparency, can further reduce nurse morale and negatively affect patient outcomes. Research suggests that participative scheduling approaches incorporating nurse preferences can improve job satisfaction. Artificial intelligence (AI) and mathematical optimization methods, such as Mixed-Integer Programming (MIP), Constraint Programming (CP), Genetic Programming (GP), and Reinforcement Learning (RL), offer potential solutions to optimize scheduling and address these challenges.

Evaluating health counseling services is crucial for ensuring their quality and effectiveness. However, this process is hampered by challenges such as language barriers and limited awareness of their needs and concerns.

Childhood cancer has an annual incidence of 150-160 cases per million children worldwide but remains vastly under-diagnosed in low to middle income countries (LMIC) such as in Sub-Saharan Africa. Moi Teaching and Referral Hospital (MTRH) serves a population of 25 million people, including 10 million children. The average number of pediatric cancer diagnoses was 216 cases annually in 2017-2019, well below the anticipated 1500 cases based on epidemiology data. The remaining 75-80% of pediatric cancer cases remain undiagnosed and these patients do not likely survive. Prior outreach and needs assessment demonstrated lack of medical knowledge related to pediatric cancer as a primary barrier to improved referrals, diagnoses, and ultimately cure.

Innovative approaches to community-level data collection are crucial to inform policies and programs that support people in aging well within their communities. For example, community-level data can proactively identify unmet health needs, inform preventative care strategies, and ensure the equitable distribution of resources that enable older adults to age in place.

Many mental health conditions (eg, substance use or panic disorders) involve long-term patient assessment and treatment. Growing evidence suggests that the progression and presentation of these conditions may be highly individualized. Digital sensing and predictive modeling can augment scarce clinician resources to expand and personalize patient care. We discuss techniques to process patient data into risk predictions, for instance, the lapse risk for a patient with alcohol use disorder (AUD). Of particular interest are idiographic approaches that fit personalized models to each patient.

Depression is the top contributor to global disability. Early detection of depression and depressive symptoms enables timely intervention and reduces their physical and social consequences. Prevalence estimates of depression approach 30% among college students. Passive, device-based sensing further enables detection of depressive symptoms at a low burden to the individual.

During pregnancy, self-rated health (SRH) and self-rated mental health (SRMH) are key indicators of health status and predictors of future healthcare needs. The relationship between pregnant women’s health perceptions and their choice of antenatal care providers, midwives or general practitioners (GPs), is not known. Factors like childhood experiences and socioeconomic status are important determinants of health throughout life. Understanding these health determinants can help healthcare providers better address the diverse needs of pregnant women.