Showing papers by "Eric J. Topol published in 2016"

State of Telehealth

Abstract: With the digital revolution, telehealth is evolving from clinics to the home. In this review, the authors summarize current trends, barriers and limitations, and the potential for telehealth to improve health care delivery.

Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists.

Abstract: Artificial intelligence—the mimicking of human cognition by computers—was once a fable in science fiction but is becoming reality in medicine. The combination of big data and artificial intelligence, referred to by some as the fourth industrial revolution,1 will change radiology and pathology along with other medical specialties. Although reports of radiologists and pathologists being replaced by computers seem exaggerated,2 these specialties must plan strategically for a future in which artificial intelligence is part of the health care workforce. Radiologists have always revered machines and technology. In 1960, Lusted predicted “an electronic scannercomputer to examine chest photofluorograms, to separate the clearly normal chest films from the abnormal chest films.”3 Lusted further suggested that “the abnormal chest films would be marked for later study by the radiologists.”3 Lusted’s intuitions were prescient: interpreting radiographs is pattern recognition; computers can recognize patterns and may be helpful because some roentgenographic analyses can be automated. Nearly 60 years after Lusted’s prediction, Enlitic, a technology company in Silicon Valley, inputted images of normal radiographs and radiographs with fractures into a computerized database.4 Using deep learning, a refined version of artificial neural networks, the

Privacy and security in the era of digital health: what should translational researchers know and do about it?

Abstract: The rapid growth in the availability and incorporation of digital technologies in almost every aspect of our lives creates extraordinary opportunities but brings with it unique challenges This is especially true for the translational researcher, whose work has been markedly enhanced through the capabilities of big data aggregation and analytics, wireless sensors, online study enrollment, mobile engagement, and much more At the same time each of these tools brings distinctive security and privacy issues that most translational researchers are inadequately prepared to deal with despite accepting overall responsibility for them For the researcher, the solution for addressing these challenges is both simple and complex Cyber-situational awareness is no longer a luxury-it is fundamental in combating both the elite and highly organized adversaries on the Internet as well as taking proactive steps to avoid a careless turn down the wrong digital dark alley The researcher, now responsible for elements that may/may not be beyond his or her direct control, needs an additional level of cyber literacy to understand the responsibilities imposed on them as data owner Responsibility lies with knowing what you can do about the things you can control and those you can't The objective of this paper is to describe the data privacy and security concerns that translational researchers need to be aware of, and discuss the tools and techniques available to them to help minimize that risk

A prospective randomized trial examining health care utilization in individuals using multiple smartphone-enabled biosensors

Abstract: Background. Mobile health and digital medicine technologies are becoming increasingly used by individuals with common, chronic diseases to monitor their health. Numerous devices, sensors, and apps are available to patients and consumers-some of which have been shown to lead to improved health management and health outcomes. However, no randomized controlled trials have been conducted which examine health care costs, and most have failed to provide study participants with a truly comprehensive monitoring system. Methods. We conducted a prospective randomized controlled trial of adults who had submitted a 2012 health insurance claim associated with hypertension, diabetes, and/or cardiac arrhythmia. The intervention involved receipt of one or more mobile devices that corresponded to their condition(s) (hypertension: Withings Blood Pressure Monitor; diabetes: Sanofi iBGStar Blood Glucose Meter; arrhythmia: AliveCor Mobile ECG) and an iPhone with linked tracking applications for a period of 6 months; the control group received a standard disease management program. Moreover, intervention study participants received access to an online health management system which provided participants detailed device tracking information over the course of the study. This was a monitoring system designed by leveraging collaborations with device manufacturers, a connected health leader, health care provider, and employee wellness program-making it both unique and inclusive. We hypothesized that health resource utilization with respect to health insurance claims may be influenced by the monitoring intervention. We also examined health-self management. Results & Conclusions. There was little evidence of differences in health care costs or utilization as a result of the intervention. Furthermore, we found evidence that the control and intervention groups were equivalent with respect to most health care utilization outcomes. This result suggests there are not large short-term increases or decreases in health care costs or utilization associated with monitoring chronic health conditions using mobile health or digital medicine technologies. Among secondary outcomes there was some evidence of improvement in health self-management which was characterized by a decrease in the propensity to view health status as due to chance factors in the intervention group.

Stop the privatization of health data

Abstract: Tech giants moving into health may widen inequalities and harm research, unless people can access and share their data, warn John T. Wilbanks and Eric J. Topol.

Molecular Autopsy for Sudden Unexpected Death.

Abstract: Molecular Autopsy for Sudden Unexpected Death Approximately 11 000 individuals younger than 45 years in the United States die suddenly and unexpectedly each year from conditions including sudden infant death, pulmonary embolism, ruptured aortic aneurysm, and sudden cardiac death (SCD). Sometimes the cause of death is not determined, even after a clinical autopsy, leaving living relatives with an inaccurate or ambiguous family health history. Moreover, the rate of clinical autopsy has declined from approximately 50% fifty years ago to less than 10% in 2008, contributing further to uncertain family health histories.1 This uncertainty may be partially resolved with postmortem genetic testing (“molecular autopsy”).2 Initial studies, limited to cardiac channelopathy and epilepsy genes, have yielded molecular diagnoses in approximately 25% of cases.3,4 A more comprehensive molecular autopsy program, expanded beyond SCD, has the potential to provide more accurate family health information to a wider spectrum of afflicted families. Here we report preliminary results from a systematic, prospective, family-based, molecular autopsy study.

Self-Monitoring Utilization Patterns Among Individuals in an Incentivized Program for Healthy Behaviors.

Abstract: Background: The advent of digital technology has enabled individuals to track meaningful biometric data about themselves. This novel capability has spurred nontraditional health care organizations to develop systems that aid users in managing their health. One of the most prolific systems is Walgreens Balance Rewards for healthy choices (BRhc) program, an incentivized, Web-based self-monitoring program. Objective: This study was performed to evaluate health data self-tracking characteristics of individuals enrolled in the Walgreens’ BRhc program, including the impact of manual versus automatic data entries through a supported device or apps. Methods: We obtained activity tracking data from a total of 455,341 BRhc users during 2014. Upon identifying users with sufficient follow-up data, we explored temporal trends in user participation. Results: Thirty-four percent of users quit participating after a single entry of an activity. Among users who tracked at least two activities on different dates, the median length of participating was 8 weeks, with an average of 5.8 activities entered per week. Furthermore, users who participated for at least twenty weeks (28.3% of users; 33,078/116,621) consistently entered 8 to 9 activities per week. The majority of users (77%; 243,774/315,744) recorded activities through manual data entry alone. However, individuals who entered activities automatically through supported devices or apps participated roughly four times longer than their manual activity-entering counterparts (average 20 and 5 weeks, respectively; P<.001). Conclusions: This study provides insights into the utilization patterns of individuals participating in an incentivized, Web-based self-monitoring program. Our results suggest automated health tracking could significantly improve long-term health engagement. [J Med Internet Res 2016;18(11):e292]

A Whole Blood Molecular Signature for Acute Myocardial Infarction

Abstract: Chest pain is a leading reason patients seek medical evaluation. While assays to detect myocyte death are used to diagnose a heart attack (acute myocardial infarction, AMI), there is no biomarker to indicate an impending cardiac event. Transcriptional patterns present in circulating endothelial cells (CEC) may provide a window into the plaque rupture process and identify a proximal biomarker for AMI. Thus, we aimed to identify a transcriptomic signature of AMI present in whole blood, but derived from CECs. Candidate genes indicative of AMI were nominated from microarray of enriched CEC samples, and then verified for detectability and predictive potential via qPCR in whole blood. This signature was validated in an independent cohort. Our findings suggest that a whole blood CEC-derived molecular signature identifies patients with AMI and sets the framework to potentially identify the earlier stages of an impending cardiac event where conventional biomarkers indicative of myonecrosis remain undetected.

Money back guarantees for non-reproducible results?

Abstract: There are better solutions to the “reproducibility crisis” in research Money back guarantees are generally unheard of in biomedicine and healthcare. Recently, the US provider Geisenger Health System, in Pennsylvania, started a programme to give patients their money back if they were dissatisfied.1 That came as quite a surprise. Soon thereafter, the chief medical officer at Merck launched an even bigger one, proposing an “incentive-based approach” to non-reproducible results—what he termed a “reproducibility crisis” that “threatens the entire biomedical research enterprise.”2 The problem of irreproducibility in biomedical research is real and has been emphasised in multiple reports.3 4 5 In the same vein, the retraction of academic papers has been rising, attributable, in nearly equal parts, to irreproducible results or data that have been falsified.6 But this problem is not confined to basic science or animal model work from academic laboratories. Clinical trials, the final common pathway for the validation and approval of new drugs, have been plagued with serious drawbacks. The bad science in clinical trials has been well documented and includes selective publication of …

A framework for smartphone-enabled, patient-generated health data analysis

Abstract: Background: Digital medicine and smartphone-enabled health technologies provide a novel source of human health and human biology data. However, in part due to its intricacies, few methods have been established to analyze and interpret data in this domain. We previously conducted a six-month interventional trial examining the efficacy of a comprehensive smartphone-based health monitoring program for individuals with chronic disease. This included 38 individuals with hypertension who recorded 6,290 blood pressure readings over the trial. Methods: In the present study, we provide a hypothesis testing framework for unstructured time series data, typical of patient-generated mobile device data. We used a mixed model approach for unequally spaced repeated measures using autoregressive and generalized autoregressive models, and applied this to the blood pressure data generated in this trial. Results: We were able to detect, roughly, a 2 mmHg decrease in both systolic and diastolic blood pressure over the course of the trial despite considerable intra- and inter-individual variation. Furthermore, by supplementing this finding by using a sequential analysis approach, we observed this result over three months prior to the official study end—highlighting the effectiveness of leveraging the digital nature of this data source to form timely conclusions. Conclusions: Health data generated through the use of smartphones and other mobile devices allow individuals the opportunity to make informed health decisions, and provide researchers the opportunity to address innovative health and biology questions. The hypothesis testing framework we present can be applied in future studies utilizing digital medicine technology or implemented in the technology itself to support the quantified self.

Smartphone Medicine

Abstract: In just a short time, smartphones have had a profound impact on people's everyday lives. However, they have yet to display any substantial impact in the field of healthcare and medicine. This article aims to explore the ways in which smartphones and the Internet of medical things can improve medicine, both today and in the future.

THE FUTURIST Toward a personalized, learning healthcare system.

Abstract: The great inversion of medicine, with its roots just starting to take hold now, will have been fully achieved over the next few decades.