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.

Recently, advances in wearable technologies, data science and machine learning have begun to transform evidence-based medicine, offering a tantalizing glimpse into a future of next-generation ‘deep’ medicine. Despite stunning advances in basic science and technology, clinical translations in major areas of medicine are lagging. While the COVID-19 pandemic exposed inherent systemic limitations of the clinical trial landscape, it also spurred some positive changes, including new trial designs and a shift toward a more patient-centric and intuitive evidence-generation system.

Medicine’s ability to quickly respond to challenges raises questions from researchers, practitioners, and society as a whole. Our task in this study was to identify key and atypical current factors influencing the development of medicine and to predict the development of medicine in the short, medium, and long term. To implement our study, we selected 22 medical experts and applied the three-level Delphi method. The current trends caused by COVID-19 have a short-term impact, but they will launch other drivers that will transform the healthcare industry.

Inequities in cancer research and care are widespread. As more clinical data is analyzed and collected, the gaps in care for vulnerable communities have only become more glaring. A 2022 BMC Public Health study found that cancer survival rates were lower for individuals living in both a high-income country and a resource-deprived community (BMC Public Health 2022). Statistics and studies like this are bolstering research initiatives across the country.

Healthcare disparities are a persistent societal problem. One of the contributing factors to this status quo is the lack of diversity and representativeness of research efforts, which result in non-generalizable evidence that, in turn, provides suboptimal means to enable the best possible outcomes at the individual level.

With the rise of connected sensor technologies, there are seemingly endless possibilities for new ways to measure health. These technologies offer researchers and clinicians opportunities to go beyond brief snapshots of data captured by traditional in-clinic assessments, to redefine health and disease. Given the myriad opportunities for measurement, how do research or clinical teams know what they should be measuring? Patient engagement, early and often, is paramount to thoughtfully selecting what is most important.

Modern research in the life sciences is unthinkable without computational methods for extracting, quantifying and visualizing information derived from biological microscopy imaging data. In the past decade, we observed a dramatic increase in available software packages for these purposes. As it is increasingly difficult to keep track of the number of available image analysis platforms, tool collections, components and emerging technologies, we provide a conservative overview of software we use in daily routine and give insights into emerging new tools.

Digital health encompasses a broad scope of tools that can improve health care, enable lifestyle change and create operational efficiencies. This includes digital solutions involving telemedicine and telehealth, mHealth, wearables, remote monitoring, apps and others. In July 2016, the AMA first conducted a study of physicians' motivations and requirements for the adoption of digital clinical tools. While there is broad-based optimism, physicians told the AMA there are “must-haves” that digital health tools need to turn their enthusiasm into adoption.

A few years ago, I found myself having a similar conversation about digital measures time and time again with partners at pharma and biotech companies: Study team: “We want to measure activity using a sensor at home.” Me: “Activity is broad. HumanFirst’s Atlas platform catalogs 50+ ways activity could be measured — what’s most interesting to you?” Study team: “Let’s do moderate to vigorous activity ” Me: “Oh excellent. There are over 1000 ways that measure has been reported in the literature with different time domains, accelerometer cut points or METs .”

European health systems face unprecedented challenges, especially from the spiralling demand for complex and costly long-term health and social care for age-related chronic diseases, but also from health threats from antimicrobial resistance and climate change. Nevertheless, despite these challenges, EFPIA sees many reasons to be optimistic about a healthier future for Europe. Now is the time to use the lessons learned from COVID-19 pandemic, and the renewed political and public prioritisation of health, to ambitiously POWER up our future health systems.

Covid-19 pandemic has laid bare the deficiencies of health systems around the world, exacerbating systemic issues, and underlining the need to address the growing demands of an ageing population. Systems are struggling from long-term underinvestment and piecemeal funding that has been designed to manage rather than solve fundamental issues, an approach geared towards treating disease rather than optimising health – and a failure to fully embrace technology. Offering hope and real solutions to these problems, technology should be the centrepiece of a new health-care model.

The 2000s have ushered in a new era of precision health and medicine based on advances in both utility computing and omic measurements. The revolution began, in part, in 2003 when scientists sequenced 92% of the human genome, and digital medicine kicked off when wearables and app-based wellness applications began debuting in 2007.

April 1, 2019, Medical Device Innovation Consortium (MDIC)

MDIC surveyed 53 device and diagnostic industry stakeholders and separately surveyed 123 individuals identifying as patients. The goal of the complementary surveys was to inform future work to develop guidelines for industry on how to involve patients in the design of clinical trials. 

Within our survey response populations, we discovered: 

This environmental scan was prepared at the request of the Office of the Assistant Secretary for Planning and Evaluation (ASPE) as background information to assist the Physician-Focused Payment Model Technical Advisory Committee (PTAC) in preparing for a series of theme-based discussions on issues related to the development of larger population-based models with accountability for quality and total cost of care (TCOC).

Ipsos & the HLTH Foundation: In recent years, the healthcare industry has witnessed a significant increase in its adoption of digital tools and technologies, and there is no doubt that technology will play an increasing role in healthcare moving forward. This is perhaps particularly true as technology access and digital literacy are now “super” determinants of health – impacting individual’s abilities to access education, employment, and even food and/or transportation.

The American Enterprise Institute (AEI) is a nonpartisan, nonprofit, 501(c)(3) educational organization and does not take institutional positions on any issues. The views expressed in this testimony are those of the author.
Statement before the House Committee on Financial Services Devalued, Denied, and Disrespected: How Home Appraisal Bias and Discrimination Are Hurting Homeowners and Communities of Color

Abstract: Real-world evidence (RWE) increasingly informs public health and healthcare decisions worldwide. A large database has been created (“Integrated Dataset”) that integrates primary care electronic medical records with pharmacy and medical claims data on >123 million US patients since 2014. This article describes the components of the Integrated Dataset and evaluates its represen- tativeness to the US population and its potential use in evaluating influenza vaccine effectiveness.