TOPOL, Eric Jeffrey
Digital medicine, on its way to being just plain medicine.npj Digital Medicine, 1, article number 20175, https://doi.org/10.1038/s41746-017-0005-1, 2018.
"There are already nearly 30,000 peer-reviewed English-language scientific journals, producing an estimated 2.5 million articles a year.1 So why another, and why one focused specifically on digital medicine?
"To answer that question, we need to begin by defining what “digital medicine” means: using digital tools to upgrade the practice of medicine to one that is high-definition and far more individualized. It encompasses our ability to digitize human beings using biosensors that track our complex physiologic systems, but also the means to process the vast data generated via algorithms, cloud computing, and artificial intelligence. It has the potential to democratize medicine, with smartphones as the hub, enabling each individual to generate their own real world data and being far more engaged with their health. Add to this new imaging tools, mobile device laboratory capabilities, end-to-end digital clinical trials, telemedicine, and one can see there is a remarkable array of transformative technology which lays the groundwork for a new form of healthcare.
"As is obvious by its definition, the far-reaching scope of digital medicine straddles many and widely varied expertise. Computer scientists, healthcare providers, engineers, behavioral scientists, ethicists, clinical researchers, and epidemiologists are just some of the backgrounds necessary to move the field forward. But to truly accelerate the development of digital medicine solutions in health requires the collaborative and thoughtful interaction between individuals from several, if not most of these specialties. That is the primary goal of npj Digital Medicine: to serve as a cross-cutting resource for everyone interested in this area, fostering collaborations and accelerating its advancement.
"Current systems of healthcare face multiple insurmountable challenges. Patients are not receiving the kind of care they want and need, caregivers are dissatisfied with their role, and in most countries, especially the United States, the cost of care is unsustainable. We are confident that the development of new systems of care that take full advantage of the many capabilities that digital innovations bring can address all of these major issues. Researchers too, can take advantage of these leading-edge technologies as they enable clinical research to break free of the confines of the academic medical center and be brought into the real world of participants’ lives. The continuous capture of multiple interconnected streams of data will allow for a much deeper refinement of our understanding and definition of most phenotypes, with the discovery of novel signals in these enormous data sets made possible only through the use of machine learning.
"Our enthusiasm for the future of digital medicine is tempered by the recognition that presently too much of the publicized work in this field is characterized by irrational exuberance and excessive hype. Many technologies have yet to be formally studied in a clinical setting, and for those that have, too many began and ended with an under-powered pilot program. In addition, there are more than a few examples of digital “snake oil” with substantial uptake prior to their eventual discrediting.2 Both of these practices are barriers to advancing the field of digital medicine.
"Our vision for npj Digital Medicine is to provide a reliable, evidence-based forum for all clinicians, researchers, and even patients, curious about how digital technologies can transform every aspect of health management and care. Being open source, as all medical research should be, allows for the broadest possible dissemination, which we will strongly encourage, including through advocating for the publication of preprints
"And finally, quite paradoxically, we hope that npj Digital Medicine is so successful that in the coming years there will no longer be a need for this journal, or any journal specifically focused on digital medicine. Because if we are able to meet our primary goal of accelerating the advancement of digital medicine, then soon, we will just be calling it medicine. And there are already several excellent journals for that."
Subjects: Digital Health & Medicine
Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: A population-based study.Lancet Digital Health, 2, PE85-E93, 2020.
Order of authorship in the original publication: Radin, Wineinger, Topol, Steinhubl.
"Acute infections can cause an individual to have an elevated resting heart rate (RHR) and change their routine daily activities due to the physiological response to the inflammatory insult. Consequently, we aimed to evaluate if population trends of seasonal respiratory infections, such as influenza, could be identified through wearable sensors that collect RHR and sleep data.
"We obtained de-identified sensor data from 200 000 individuals who used a Fitbit wearable device from March 1, 2016, to March 1, 2018, in the USA. We included users who wore a Fitbit for at least 60 days and used the same wearable throughout the entire period, and focused on the top five states with the most Fitbit users in the dataset: California, Texas, New York, Illinois, and Pennsylvania. Inclusion criteria included having a self-reported birth year between 1930 and 2004, height greater than 1 m, and weight greater than 20 kg. We excluded daily measurements with missing RHR, missing wear time, and wear time less than 1000 min per day. We compared sensor data with weekly estimates of influenza-like illness (ILI) rates at the state level, as reported by the US Centers for Disease Control and Prevention (CDC), by identifying weeks in which Fitbit users displayed elevated RHRs and increased sleep levels. For each state, we modelled ILI case counts with a negative binomial model that included 3-week lagged CDC ILI rate data (null model) and the proportion of weekly Fitbit users with elevated RHR and increased sleep duration above a specified threshold (full model). We also evaluated weekly change in ILI rate by linear regression using change in proportion of elevated Fitbit data. Pearson correlation was used to compare predicted versus CDC reported ILI rates.
"We identified 47 249 users in the top five states who wore a Fitbit consistently during the study period, including more than 13·3 million total RHR and sleep measures. We found the Fitbit data significantly improved ILI predictions in all five states, with an average increase in Pearson correlation of 0·12 (SD 0·07) over baseline models, corresponding to an improvement of 6·3–32·9%. Correlations of the final models with the CDC ILI rates ranged from 0·84 to 0·97. Week-to-week changes in the proportion of Fitbit users with abnormal data were associated with week-to-week changes in ILI rates in most cases.
"Activity and physiological trackers are increasingly used in the USA and globally to monitor individual health. By accessing these data, it could be possible to improve real-time and geographically refined influenza surveillance. This information could be vital to enact timely outbreak response measures to prevent further transmission of influenza cases during outbreaks."
Subjects: Digital Health & Medicine , INFECTIOUS DISEASE › Influenza