News & Comment

In recent years, a steady swell of biological image data has driven rapid progress in healthcare applications of computer vision and machine learning. To make sense of this data, scientists often rely on detailed annotations from domain experts for training artificial intelligence (AI) algorithms. The time-consuming and costly process of collecting annotations presents a sizable bottleneck for AI research and development. HALS (Human-Augmenting Labeling System) is a collaborative human-AI labeling workflow that uses an iterative “review-and-revise” model to improve the efficiency of this critical process in computational pathology.

Current public health measures catalyzed a large shift to virtual care, resulting in a great uptake in telephone and video-enabled care. While pre-pandemic public healthcare funding rarely covered the telephone as a reimbursable care delivery model, it has proven a crucial offering for many populations. As the new standard of virtual service delivery is being solidified, simple technological solutions that provide access to care must continue to be supported. This paper explores an important consequence of relying on complex technologies as the new standard of virtual care: the risk of exacerbating health disparities by enabling a deeper digital divide for marginalized populations.

For the past century, health care measurement and delivery have been centered in hospitals and clinics. That is beginning to change as health measures and increasingly care delivery are migrating to homes and mobile devices. The COVID-19 pandemic has only accelerated this transition. While increasing access to care and improving convenience, this move toward platforms operated by for-profit firms raises concerns about privacy, equity, and duty that will have to be addressed. In addition, this change in measuring health and delivering health care will create opportunities for educators to expand the settings for training, researchers to conduct studies at enormous scale, payors to embrace lower-cost clinical settings, and patients to make their voices heard.

The COVID-19 pandemic has been a catalyst for the implementation of decentralized clinical trials (DCTs) enabled by digital health technologies (DHTs) in the field while curtailing in-person interactions and putting significant demands on health care resources. DHTs offer improvements in real-time data acquisition remotely while maintaining privacy and security. Here, we describe the implications of technologies, including edge computing, zero-trust environments, and federated computing in DCTs enabled by DHTs. Taken together, these technologies—in the setting of policy and regulation that enable their use while protecting the users—extend the scope and accelerate the pace of clinical research.

Natural language computer applications are becoming increasingly sophisticated and, with the recent release of Generative Pre-trained Transformer 3, they could be deployed in healthcare-related contexts that have historically comprised human-to-human interaction. However, for GPT-3 and similar applications to be considered for use in health-related contexts, possibilities and pitfalls need thoughtful exploration. In this article, we briefly introduce some opportunities and cautions that would accompany advanced Natural Language Processing applications deployed in eHealth.

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.

Competence and warmth are two essential dimensions of patient care. During the twentieth century, the industrial revolution in data collection, with the increasing use of machines and the division of labor that led to the development of many subspecialities, increased the overall competence of physicians at the expense of the warmth dimension. The spread of patient-centered care principles aimed to rebalance the two dimensions. In the twenty-first century, the industrial revolution in data processing with the emergence of algorithmic decision-making systems based on artificial intelligence is likely to disrupt further this balance. Competence will no longer be the prerogative of physicians, but a dimension to be shared between physicians and autonomous algorithmic decision-making systems, by contrast to warmth which should remain a human attribute. In this comment, we discuss the extent to which competence and warmth can remain the core dimensions of physician care in the era of artificial intelligence.

From clinical trials to care delivery, advanced, digitally enabled technologies and analytics offer new approaches to how we think about medicine, health, and biology. The Covid-19 pandemic has accelerated this conversation, and forced a roadmap, once measured in years or decades, to unfold over days, weeks, and months. Yet the scaffolding for this roadmap had already emerged prior to the Covid-19 pandemic. In this perspective, we highlight a special collection of papers on “digital medicine,” which emerged from a symposium held in Boston in 2019 and were published in 2020 and 2021. The symposium was hosted by Harvard Business School and the Harvard MIT Center for Regulatory Science, and included a range of speakers and attendees from industry, government, and academics. We describe their ongoing relevance as we contemplate our early 2021 pandemic reality and the near future of digitally empowered health care.

The use of remote monitoring and virtual visits has accelerated to support socially-distanced patient care during the COVID-19 pandemic. Despite the necessity of this expansion, ambiguity in coding is hindering adoption and patient access, most notably for remote physiologic monitoring due to a lack of definition of the term “physiologic”. In this analysis, we describe the history of remote monitoring code development, present several examples in respiratory disease and other chronic conditions in which gaps and confusion remain and suggest ways to clarify and broaden coverage to ensure equitable access to remote monitoring.

The COVID-19 pandemic has profoundly affected life worldwide. Governments have been faced with the formidable task of implementing public health measures, such as social distancing, quarantines, and lockdowns, while simultaneously supporting a sluggish economy and stimulating research and development (R&D) for the pandemic. Catalyzing bottom-up entrepreneurship is one method to achieve this. Home-grown efforts by citizens wishing to contribute their time and resources to help have sprouted organically, with ideas shared widely on the internet. We outline a framework for structured, crowdsourced innovation that facilitates collaboration to tackle real, contextualized problems. This is exemplified by a series of virtual hackathon events attracting over 9000 applicants from 142 countries and 49 states. A hackathon is an event that convenes diverse individuals to crowdsource solutions around a core set of predetermined challenges in a limited amount of time. A consortium of over 100 partners from across the healthcare spectrum and beyond defined challenges and supported teams after the event, resulting in the continuation of at least 25% of all teams post-event. Grassroots entrepreneurship can stimulate economic growth while contributing to broader R&D efforts to confront public health emergencies.