Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers

Parental executive functions and motivations unveil variations in young children's screen media use

BACKGROUND

The increased screen media use among children aged 3 to 5, particularly in the post-COVID era, is concerning. Despite several organizations' recommendation of a one-hour screen limit for young children, actual usage often exceeds this guideline.

OBJECTIVE

This study explored the influence of parental characteristics such as self-efficacy, motivation, socioeconomic status, and cognitive abilities on children's screen time habits.

METHODS

Employing a feature selection model, 251 caregivers answered an online survey, presenting data from themselves and on-screen usage for 126 girls and 125 boys. We found that the caregiver's executive functions, including cognitive flexibility, initiation, task monitoring, and material organization, significantly impact children's screen time.

RESULTS

Our analysis highlighted the vital role of caregivers' self-efficacy in moderating children's screen usage. Family net income, children's age and gender, and motivations related to children's desires and behavioral control were also significant contributors to usage patterns.

CONCLUSION

This study offers insights into interventions and effective parenting strategies in the digital age, highlighting the importance of addressing socio-demographic factors in understanding this complex issue.

Bayesian approach for design and analysis of medical device trials in the era of modern clinical studies

Medical device technology develops rapidly, and the life cycle of a medical device is much shorter than drugs. It is necessary to evaluate the safety and effectiveness of a medical device in a timely manner to keep up with technology flux. Bayesian methods provides an efficient approach to addressing this challenge. In this article, we review the characteristics of the Bayesian approach and some Bayesian designs that were commonly used in medical device regulatory setting, including Bayesian adaptive design, Bayesian diagnostic design, Bayesian multiregional design, and Bayesian label expansion study. We illustrate these designs with medical devices approved by the US Food and Drug Administration (FDA). We also review several innovative Bayesian information borrowing methods, and briefly discuss the challenges and future directions of the Bayesian application in medical device trials. Our objective is to promote the use of the Bayesian approach to accelerate the development of innovative medical devices and their accessibility to patients for effective disease diagnoses and treatments.

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems

The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.

Using real-world evidence data and digital monitoring to analyze the hepatotoxic profiles of biologics across more than two million patients

The real-world evidence data from multiple sources which includes information on patient health status and medical behavior in routine clinical setup can give deeper insights into drugs 'safety and efficacy. The RWE-based analysis in this study revealed a statistically significant link between biologics usage and hepatotoxicity in patients. To the best of our knowledge, this study is the first to conduct a large-scale multi-cohort analysis on the hepatotoxic profiles of biologics. Biologics are among the most prescribed medicines for several chronic inflammatory diseases. These agents target critical pathogenic pathways, but they may also have serious side effects. It is important to analyze whether biologics agents are an added concern or therapeutic opportunity. Real-world evidence (RWE) data were extracted for patients using biologics to monitor the safety and effectiveness of the biologics. All six biologics included in this analysis-are mostly highly prescribed biologics. The aim of the study was to assess the hepatotoxic profiles of subjects using different biologics. We evaluated the safety of current treatment regimens for patients in a large real-world cohort from multiple health care centers. Total number of eligible patients retrieved from the database is 38,112,285. Of these 38 million patients, 2.3 million take biologics. The primary objective was to assess the potential adverse hepatotoxic effects of the six biologics; adalimumab, trastuzumab, prevnar13, pegfilgrastim, interferon-beta1a and insulin glargine across different indications like diabetes mellitus, encounter for immunization, malignant neoplasm of breast, multiple sclerosis, malignant neoplasm of kidney, aplastic anaemias, radiation sickness, Crohn's disease, psoriasis, rheumatoid arthritis, spondylopathies. Data from patients using the six most-used biologics-adalimumab, trastuzumab, prevnar13, pegfilgrastim, interferon-beta1a and insulin glargine were retrieved from a global research network covering 250 million patients' data from 19 countries, and assigned to the cohorts 1 and 2, respectively. The cohorts were propensity score matched for age and sex. After defining the primary outcome as "hepatotoxicity" (endpoint defined as ICD-10 code: K71 (hepatotoxic liver disease), a Kaplan-Meier survival analysis was performed, and risk ratios (RR), odds ratios (OR), and hazard ratios (HR) were determined. A total number of 2,312,655 subjects were eligible who take biologics, and after matching total cohorts accounted for 2,303,445. We have considered the clinical data as a 1:1 matched-study design, using propensity score-matched sub-cohorts to better control for confounding associations that might stem from different distributions of age and gender between the whole dataset and the subset of patients. We discovered evidence supporting the hepatotoxic-causing effect of biologic drugs: (i) all biologics considered together had an OR of 1.9 (95% CI, 1.67-2.35), with (ii) Adalimumab 1.9 (95% CI, 1.72-2.20), Trastuzumab 1.7 (95% CI, 1.2-2.3), Prevnar13 2.3 (95% CI, 2.16-2.60), Pegfilgrastim 2.3 (95% CI, 2.0-2.50), Interferon-Beta1a 1.7 (95% CI, 1.18-2.51), and Insulin glargine 1.9 (95% CI, 1.8-1.99). Our findings indicate that clinicians should consider evaluating hepatic profiles of patients undergoing treatment with biologic drugs and counsel them regarding the risk of developing hepatic injury. Strengths of the study includes a large sample size and robust statistical techniques. Limitations of this study include lack of detailed information regarding clinical severity. Major biologics are associated with hepatotoxicity. We discovered evidence supporting the hepatotoxicity-causing effects of biologics: all biologics considered together had an OR of 1.9 (95% CI, 1.67-2.35).

No Boundaries for Toxicology in Clinical Medicine: One Health, One Society and One Planet for All of Us

The concept of One Health is not new; it can be traced back for at least two hundred years [...].