How Consumers and Physicians View New Medical Technology: Comparative Survey.

BACKGROUND: As a result of the digital revolution coming to medicine, a number of new tools are becoming available and are starting to be introduced in clinical practice. OBJECTIVE: We aim to assess health care professional and consumer attitudes toward new medical technology including smartphones, genetic testing, privacy, and patient-accessible electronic health records. METHODS: We performed a survey with 1406 health care providers and 1102 consumer responders. RESULTS: Consumers who completed the survey were more likely to prefer new technologies for a medical diagnosis (437/1102, 39.66%) compared with providers (194/1406, 13.80%; P<.001), with more providers (393/1406, 27.95%) than consumers (175/1102, 15.88%) reporting feeling uneasy about using technology for a diagnosis. Both providers and consumers supported genetic testing for various purposes, with providers (1234/1406, 87.77%) being significantly more likely than consumers (806/1102, 73.14%) to support genetic testing when planning to have a baby (P<.001). Similarly, 91.68% (1289/1406) of providers and 81.22% (895/1102) of consumers supported diagnosing problems in a fetus (P<.001). Among providers, 90.33% (1270/1406) were concerned that patients would experience anxiety after accessing health records, and 81.95% (1149/1406) felt it would lead to requests for unnecessary medical evaluations, but only 34.30% (378/1102; P<.001) and 24.59% (271/1102; P<.001) of consumers expressed the same concerns, respectively. Physicians (137/827, 16.6%) reported less concern about the use of technology for diagnosis compared to medical students (21/235, 8.9%; P=.03) and also more frequently felt that patients owned their medical record (323/827, 39.1%; and 30/235, 12.8%, respectively; P<.001). CONCLUSIONS: Consumers and health professionals differ significantly and broadly in their views of emerging medical technology, with more enthusiasm and support expressed by consumers.

Digital technology to enable aging in place.

Aging, both of individuals and populations, presents challenges and opportunities. The multitude of morbidities and disabilities that are a too common component of aging represent significant challenges to individuals, their families and to healthcare systems. Aging in place is the ability to safely and comfortably maintain an independent and high quality of life in one's own home and community and is a highly desirable goal of most individuals with the additional benefit of significantly impacting the impending enormous healthcare burden. In order to make this possible, new care models that take advantage of novel technologies for tracking important physiologic and safety parameters need to be developed and implemented. By thoughtfully doing so, it can be possible to seamlessly provide preventative interventions when and as needed, detect the earliest signs of aggravation of chronic conditions, or identify and respond to any emergency situations, such as falls or cardiac arrest. In contrast to current approaches, caring for elderly individuals in their homes based on a digital technology infrastructure could be effective and cost-saving. In this review, we provide an overview of the characteristics of potential digital solutions applicable to creative aging along with the existing evidence supporting their ability to improve care, increase quality of life, and substantially decrease the emotional and financial costs associated with aging.

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

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.