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Abstract
The Internet of Things (IoT) is a system of wireless, interrelated, and connected
digital devices that can collect, send, and store data over a network without requiring
human-to-human or human-to-computer interaction. The IoT promises many benefits to
streamlining and enhancing health care delivery to proactively predict health issues
and diagnose, treat, and monitor patients both in and out of the hospital. Worldwide,
government leaders and decision makers are implementing policies to deliver health
care services using technology and more so in response to the novel COVID-19 pandemic.
It is now becoming increasingly important to understand how established and emerging
IoT technologies can support health systems to deliver safe and effective care. The
aim of this viewpoint paper is to provide an overview of the current IoT technology
in health care, outline how IoT devices are improving health service delivery, and
outline how IoT technology can affect and disrupt global health care in the next decade.
The potential of IoT-based health care is expanded upon to theorize how IoT can improve
the accessibility of preventative public health services and transition our current
secondary and tertiary health care to be a more proactive, continuous, and coordinated
system. Finally, this paper will deal with the potential issues that IoT-based health
care generates, barriers to market adoption from health care professionals and patients
alike, confidence and acceptability, privacy and security, interoperability, standardization
and remuneration, data storage, and control and ownership. Corresponding enablers
of IoT in current health care will rely on policy support, cybersecurity-focused guidelines,
careful strategic planning, and transparent policies within health care organizations.
IoT-based health care has great potential to improve the efficiency of the health
system and improve population health.
Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. Methods We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting. Findings Globally, for females, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and haemoglobinopathies and haemolytic anaemias in both 1990 and 2017. For males, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and tuberculosis including latent tuberculosis infection in both 1990 and 2017. In terms of YLDs, low back pain, headache disorders, and dietary iron deficiency were the leading Level 3 causes of YLD counts in 1990, whereas low back pain, headache disorders, and depressive disorders were the leading causes in 2017 for both sexes combined. All-cause age-standardised YLD rates decreased by 3·9% (95% uncertainty interval [UI] 3·1–4·6) from 1990 to 2017; however, the all-age YLD rate increased by 7·2% (6·0–8·4) while the total sum of global YLDs increased from 562 million (421–723) to 853 million (642–1100). The increases for males and females were similar, with increases in all-age YLD rates of 7·9% (6·6–9·2) for males and 6·5% (5·4–7·7) for females. We found significant differences between males and females in terms of age-standardised prevalence estimates for multiple causes. The causes with the greatest relative differences between sexes in 2017 included substance use disorders (3018 cases [95% UI 2782–3252] per 100 000 in males vs s1400 [1279–1524] per 100 000 in females), transport injuries (3322 [3082–3583] vs 2336 [2154–2535]), and self-harm and interpersonal violence (3265 [2943–3630] vs 5643 [5057–6302]). Interpretation Global all-cause age-standardised YLD rates have improved only slightly over a period spanning nearly three decades. However, the magnitude of the non-fatal disease burden has expanded globally, with increasing numbers of people who have a wide spectrum of conditions. A subset of conditions has remained globally pervasive since 1990, whereas other conditions have displayed more dynamic trends, with different ages, sexes, and geographies across the globe experiencing varying burdens and trends of health loss. This study emphasises how global improvements in premature mortality for select conditions have led to older populations with complex and potentially expensive diseases, yet also highlights global achievements in certain domains of disease and injury. Funding Bill & Melinda Gates Foundation.
Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
As interest in and use of telehealth during the COVID-19 global pandemic increase, the potential of digital health to increase access and quality of mental health is becoming clear. Although the world today must “flatten the curve” of spread of the virus, we argue that now is the time to “accelerate and bend the curve” on digital health. Increased investments in digital health today will yield unprecedented access to high-quality mental health care. Focusing on personal experiences and projects from our diverse authorship team, we share selected examples of digital health innovations while acknowledging that no single piece can discuss all the impressive global efforts past and present. Exploring the success of telehealth during the present crisis and how technologies like apps can soon play a larger role, we discuss the need for workforce training, high-quality evidence, and digital equity among other factors critical for bending the curve further.
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