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Mobile health involves sensors, mobile apps, social media, and location-tracking technology used in disease diagnosis, prevention, and management. This article provides an overview of key functional and regulatory aspects; the article is accompanied by an explanatory video, an illustrated glossary, and an audio interview with the author.

Background There is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various ‘big data’ efforts. While these methods are necessary, they are insufficient to achieve the full personalized medicine promise. A rigorous, complementary ‘small data’ paradigm that can function both autonomously from and in collaboration with big data is also needed. By ‘small data’ we build on Estrin’s formulation and refer to the rigorous use of data by and for a specific N-of-1 unit (i.e., a single person, clinic, hospital, healthcare system, community, city, etc.) to facilitate improved individual-level description, prediction and, ultimately, control for that specific unit. Main body The purpose of this piece is to articulate why a small data paradigm is needed and is valuable in itself, and to provide initial directions for future work that can advance study designs and data analytic techniques for a small data approach to precision health. Scientifically, the central value of a small data approach is that it can uniquely manage complex, dynamic, multi-causal, idiosyncratically manifesting phenomena, such as chronic diseases, in comparison to big data. Beyond this, a small data approach better aligns the goals of science and practice, which can result in more rapid agile learning with less data. There is also, feasibly, a unique pathway towards transportable knowledge from a small data approach, which is complementary to a big data approach. Future work should (1) further refine appropriate methods for a small data approach; (2) advance strategies for better integrating a small data approach into real-world practices; and (3) advance ways of actively integrating the strengths and limitations from both small and big data approaches into a unified scientific knowledge base that is linked via a robust science of causality. Conclusion Small data is valuable in its own right. That said, small and big data paradigms can and should be combined via a foundational science of causality. With these approaches combined, the vision of precision health can be achieved.

The United States (US) Food and Drug Administration (FDA) approves new drugs based on sponsor-submitted clinical trials. The publication status of these trials in the medical literature and factors associated with publication have not been evaluated. We sought to determine the proportion of trials submitted to the FDA in support of newly approved drugs that are published in biomedical journals that a typical clinician, consumer, or policy maker living in the US would reasonably search. We conducted a cohort study of trials supporting new drugs approved between 1998 and 2000, as described in FDA medical and statistical review documents and the FDA approved drug label. We determined publication status and time from approval to full publication in the medical literature at 2 and 5 y by searching PubMed and other databases through 01 August 2006. We then evaluated trial characteristics associated with publication. We identified 909 trials supporting 90 approved drugs in the FDA reviews, of which 43% (394/909) were published. Among the subset of trials described in the FDA-approved drug label and classified as \"pivotal trials\" for our analysis, 76% (257/340) were published. In multivariable logistic regression for all trials 5 y postapproval, likelihood of publication correlated with statistically significant results (odds ratio [OR] 3.03, 95% confidence interval [CI] 1.78-5.17); larger sample sizes (OR 1.33 per 2-fold increase in sample size, 95% CI 1.17-1.52); and pivotal status (OR 5.31, 95% CI 3.30-8.55). In multivariable logistic regression for only the pivotal trials 5 y postapproval, likelihood of publication correlated with statistically significant results (OR 2.96, 95% CI 1.24-7.06) and larger sample sizes (OR 1.47 per 2-fold increase in sample size, 95% CI 1.15-1.88). Statistically significant results and larger sample sizes were also predictive of publication at 2 y postapproval and in multivariable Cox proportional models for all trials and the subset of pivotal trials. Over half of all supporting trials for FDA-approved drugs remained unpublished >/= 5 y after approval. Pivotal trials and trials with statistically significant results and larger sample sizes are more likely to be published. Selective reporting of trial results exists for commonly marketed drugs. Our data provide a baseline for evaluating publication bias as the new FDA Amendments Act comes into force mandating basic results reporting of clinical trials.

The electronic medical record (EMR) is an enabling technology that allows physician practices to pursue more powerful quality improvement programs than is possible with paper-based records. However, achieving quality improvement through EMR use is neither low-cost nor easy. Based on a qualitative study of physician practices that had implemented an EMR, we found that quality improvement depends heavily on physicians' use of the EMR - and not paper - for most of their daily tasks. We identified key barriers to physicians' use of EMRs. We then suggest policy interventions to overcome these barriers, including providing work/practice support systems, improving electronic clinical data exchange, and providing financial rewards for quality improvement. [PUBLICATION ABSTRACT]

Horwitz et al examine Bjornevik et al's recent research report in Science that tested the hypothesis that multiple sclerosis (MS) is caused by Epstein-Barr virus (EBV) in a cohort of more than ten million adults on active duty in the US military during a 20-year period (1993-2013). The authors reported that individuals who had prior EBV infection were 24 times more likely to develop MS than noninfected persons. The findings appear to confirm a long-standing suspicion linking EBV to MS and led many to call for an EBV vaccine to prevent MS. A closer examination of the article, however, indicates the analysis was incomplete and misrepresents the data. In fact, a strong association between EBV and MS was present only for those with recent infection occurring during active-duty military service.

Background The sharing of individual participant-level data from COVID-19 trials would allow re-use and secondary analysis that can help accelerate the identification of effective treatments. The sharing of trial data is not the norm, but the unprecedented pandemic caused by SARS-CoV-2 may serve as an impetus for greater data sharing. We sought to assess the data sharing intentions of interventional COVID-19 trials as declared in trial registrations and publications. Methods We searched ClinicalTrials.gov and PubMed for COVID-19 interventional trials. We analyzed responses to ClinicalTrials.gov fields regarding intent to share individual participant level data and analyzed the data sharing statements in eligible publications. Results Nine hundred twenty-four trial registrations were analyzed. 15.7% were willing to share, of which 38.6% were willing to share immediately upon publication of results. 47.6% declared they were not willing to share. Twenty-eight publications were analyzed representing 26 unique COVID-19 trials. Only seven publications contained data sharing statements; six indicated a willingness to share data whereas one indicated that data was not available for sharing. Conclusions At a time of pressing need for researchers to work together to combat a global pandemic, intent to share individual participant-level data from COVID-19 interventional trials is limited.

Health applications for mobile and wearable devices continue to experience tremendous growth both in the commercial and research sectors, but their impact on healthcare has yet to be fully realized. This commentary introduces three articles in a special issue that provides guidance on how to successfully address translational barriers to bringing mobile health technologies into clinical research and care. We also discuss how the cross-organizational sharing of data, software, and other digital resources can lower such barriers and accelerate progress across mobile health.

The relational nature of large language models and other forms of generative artificial intelligence raises additional ethical questions for medicine, beyond the daunting ethics surrounding predictive AI.

Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health (mHealth) and wearable technology. Data from Biometric Monitoring Technologies (BioMeTs), including mHealth and wearables, can be transformed into that act as indicators of health outcomes and can be used to diagnose and monitor a number of chronic diseases and conditions. There are many challenges faced by digital biomarker development, including a lack of regulatory oversight, limited funding opportunities, general mistrust of sharing personal data, and a shortage of open-source data and code. Further, the process of transforming data into digital biomarkers is computationally expensive, and standards and validation methods in digital biomarker research are lacking. In order to provide a collaborative, standardized space for digital biomarker research and validation, we present the first comprehensive, open-source software platform for end-to-end digital biomarker development: . Here, we detail the general DBDP framework as well as three robust modules within the DBDP that have been developed for specific digital biomarker discovery use cases. The clear need for such a platform will accelerate the DBDP's adoption as the industry standard for digital biomarker development and will support its role as the epicenter of digital biomarker collaboration and exploration.

The use of artificial intelligence (AI) in health care has significant implications for patient-clinician interactions. Practical and ethical challenges have emerged with the adoption of large language models (LLMs) that respond to prompts from clinicians, patients, and caregivers. With an emphasis on patient experience, this paper examines the potential of LLMs to act as facilitators, interrupters, or both in patient-clinician relationships. Drawing on our experiences as patient advocates, computer scientists, and physician informaticists working to improve data exchange and patient experience, we examine how LLMs might enhance patient engagement, support triage, and inform clinical decision-making. While affirming LLMs as a tool enabling the rise of the “AI patient,” we also explore concerns surrounding data privacy, algorithmic bias, moral injury, and the erosion of human connection. To help navigate these tensions, we outline a conceptual framework that anticipates the role and impact of LLMs in patient-clinician dynamics and propose key areas for future inquiry. Realizing the potential of LLMs requires careful consideration of which aspects of the patient-clinician relationship must remain distinctly human and why, even when LLMs offer plausible substitutes. This inquiry should draw on ethics and philosophy, aligned with AI imperatives such as patient-centered design and transparency, and shaped through collaboration between technologists, health care providers, and patient communities.