Digital Health Engagement in the US Population: Insights From the 2018 Health Information National Trends Survey.

Objectives. To examine prevalence and predictors of digital health engagement among the US population. Methods. We analyzed nationally representative cross-sectional data on 7 digital health engagement behaviors, as well as demographic and socioeconomic predictors, from the Health Information National Trends Survey (HINTS 5, cycle 2, collected in 2018; n = 2698-3504). We fitted multivariable logistic regression models using weighted survey responses to generate population estimates. Results. Digitally seeking health information (70.14%) was relatively common, whereas using health apps (39.53%) and using a digital device to track health metrics (35.37%) or health goal progress (38.99%) were less common. Digitally communicating with one's health care providers (35.58%) was moderate, whereas sharing health data with providers (17.20%) and sharing health information on social media (14.02%) were uncommon. Being female, younger than 65 years, a college graduate, and a smart device owner positively predicted several digital health engagement behaviors (odds ratio range = 0.09-4.21; P value range < .001-.03). Conclusions. Many public health goals depend on a digitally engaged populace. These data highlight potential barriers to 7 key digital engagement behaviors that could be targeted for intervention.

Impact of COVID-19 on Health Economics and Technology of Diabetes Care: Use Cases of Real-Time Continuous Glucose Monitoring to Transform Health Care During a Global Pandemic.

Background: The coronavirus disease 2019 (COVID-19) pandemic has exposed vulnerabilities and placed tremendous financial pressure on nearly all aspects of the U.S. health care system. Diabetes care is an example of the confluence of the pandemic and heightened importance of technology in changing care delivery. It has been estimated the added total direct U.S. medical cost burden due to COVID-19 to range between $160B (20% of the population infected) and $650B (80% of the population infected) over the course of the pandemic. The corresponding range for the population with diabetes is between $16B and $65B, representing between 5% and 20% of overall diabetes expenditure in the United States. We examine the evidence to support allocating part of this added spend to infrastructure capabilities to accelerate remote monitoring and management of diabetes. Methods and Results: We reviewed recent topical literature and COVID-19-related analyses in the public health, health technology, and health economics fields in addition to databases and surveys from government sources and the private sector. We summarized findings on use cases for real-time continuous glucose monitoring in the community, for telehealth, and in the hospital setting to highlight the successes and challenges of accelerating the adoption of a digital technology out of necessity during the pandemic and beyond. Conclusions: One critical and lasting consequence of the pandemic will be the accelerated adoption of digital technology in health care delivery. We conclude by discussing ways in which the changes wrought by COVID-19 from a health care, policy, and economics perspective can add value and are likely to endure postpandemic.

Trends and Predictors of Use of Digital Health Technology in the United States.

BACKGROUND: Digital health technology is becoming central to health care. A better understanding of the trends and predictors of its use could reflect how people engage with the health care system and manage their health care needs. METHODS: Using data from the National Health Interview Survey for years 2011 to 2018, we assessed the use of digital health technology among individuals aged ≥18 years in the United States across 2 domains: 1) search for health information online and 2) interaction with health care providers (eg, fill a prescription, schedule a medical appointment, or communicate with health care providers). RESULTS: Our study included 253,829 individuals; representing nearly 237 million adults in the United States annually; mean age 49.6 years (SD 18.4); 51.8% women; and 65.9% non-Hispanic white individuals. Overall, 49.2% of individuals reported searching for health information online and 18.5% reported at least 1 technology-based interaction with the health care system. Between 2011 and 2018, the proportion who searched for health information online increased from 46.5% to 55.3% (P < .001), whereas the proportion who used technology to interact with the health care system increased from 12.5% to 27.4% (P < .001). Although technology-based interaction with the health care system increased across most subgroups, there were significant disparities in the extent of increase across clinical and sociodemographic subgroups. CONCLUSIONS: The use of digital health technologies increased between 2011 and 2018, however, the uptake of these technologies has been unequal across subgroups. Future innovations and strategies should focus on expanding the reach of digital heath technology across all subgroups of society to ensure that its expansion does not exacerbate the existing health inequalities.

Using multivariate statistical methods to assess the urban smartness on the example of selected European cities.

The growing importance of maturity smart cities is currently observed worldwide. The vast majority of smart city models focus on hard domains such as communication and technology infrastructure. Scientists emphasize the need to take into account social capital and the knowledge of residents. The smart cities invest in enhanced openness and transparency data. Mature smart cities use real-time evidences and information to citizens, businesses and visitors. The smart cities are characterized by bottom-down management and civil government. The paper aims to assess the urban smartness of selected European cities based on the ISO 37120 standard. Several research methods including the Multidimensional Statistical Analysis (MSA) were applied. Using the statistical analysis of European smart cities with the implemented ISO 37120 standard, the author tried to fill gaps in the knowledge and to evaluate maturity smart cities. The results of the research have shown that the smart city concept is a viable strategy which contributes to the urban sustainability. The author also found out that urban sustainability frameworks contain a large number of indicators measuring environmental sustainability, the smart city frameworks lack environmental indicators while highlighting social and economic aspects.

Advanced Data Analytics for Clinical Research Part I: What are the Tools?

The concept of Big Data is changing the way that clinical research can be performed. Cardiothoracic surgeons need to understand the dynamic digital transformation taking place in the healthcare industry. In the last decade, technological advances and Big Data analytics have become powerful tools for businesses. In healthcare, rapid expansion of Big Data infrastructure has occurred in parallel with attempts to reduce cost and improve outcomes. Many hospitals around the country are augmenting traditional relational databases with Big Data infrastructure. Advanced data capture and categorization tools such as natural language processing and optical character recognition are being developed for clinical and research use, while Internet of Things in the form of wearable technology serves as an additional source of data usable for research. As cardiothoracic surgeons seek ways to innovate, novel approaches to data acquisition and analysis enable a more rigorous level of investigatory efforts.

Advanced Data Analytics for Clinical Research Part II: Application to Cardiothoracic Surgery.

In the first part of this series, we introduced the tools of Big Data, including Not Only Standard Query Language data warehouse, natural language processing (NLP), optical character recognition (OCR), and Internet of Things (IoT). There are nuances to the utilization of these analytics tools, which must be well understood by clinicians seeking to take advantage of these innovative research strategies. One must recognize technical challenges to NLP, such as unintended search outcomes and variability in the expression of human written texts. Other caveats include dealing written texts in image formats, which may ultimately be handled with transformation to text format by OCR, though this technology is still under development. IoT is beginning to be used in cardiac monitoring, medication adherence alerts, lifestyle monitoring, and saving traditional labs from equipment failure catastrophes. These technologies will become more prevalent in the future research landscape, and cardiothoracic surgeons should understand the advantages of these technologies to propel our research to the next level. Experience and understanding of technology are needed in building a robust NLP search result, and effective communication with the data management team is a crucial step in successful utilization of these technologies. In this second installment of the series, we provide examples of published investigations utilizing the advanced analytic tools introduced in Part I. We will explain our processes in developing the research question, barriers to achieving the research goals using traditional research methods, tools used to overcome the barriers, and the research findings.