In an era marked by scientific stagnation, Decentralized Science (DeSci) challenges the inefficiencies of traditional funding and publishing systems. DeSci employs blockchain technology to address the misalignment of incentives in academic research, emphasizing transparency, rapid funding, and open-source principles. Centralized institutions have been linked to a deceleration of progress, which is acutely felt in the field of longevity science-a critical discipline as aging is the #1 risk factor for most diseases. DeSci proposes a transformative model where decentralized autonomous organizations (DAOs) facilitate community-driven funding, promoting high-risk, high-reward research. DeSci, particularly within longevity research, could catalyze a paradigm shift towards an equitable, efficient, and progressive scientific future.
Open access
Health, Environment, Cognitive Aging
Health Systems, Economic Evaluations, Quality of Life
Melanie Swan, takashi kido, Eric Roland, Renato P. dos Santos
Health Agents are introduced as personalized AI health advisors for “healthcare by app” instead of “sickcare by appointment,” especially to target Healthy Longevity as a global wellness priority with two billion people estimated to be over 65 in 2050. Health Agents could allow physicians to oversee thousands of patients simultaneously, addressing the 50% of the world's population still not covered by essential health services. As AI Health Interfaces shift to continuous health monitoring (1000x/minute) with medical-grade smart-watches, pins, and wearables, individuals can customize the level of information viewed. As any genAI agent system, Health Agents “speak” natural language to humans and formal language (as Math Agents) to the computational infrastructure, possibly outputting the mathematics of personalized homeostatic health as part of their reinforcement learning agent behavior. Health Agents could deliver precision medicine as a service. Longevity may be achieved 80% with sleep, diet, exercise, and stress reduction, and 20% by medical intervention (metformin, rapamycin, NAD+/sirtuins, alpha-ketoglutarate, taurine), measured quantitatively with aging clocks, biomarkers, and hallmarks. Health Agents are a web3 genAI tool for automated health management, via Personalized Aging Clocks, digital-biological twins, and pathway2vec approaches, for human-AI intelligence amplification towards healthy longevity for global well-being.
Naresh Kshetri, Rahul Mishra, Mir Mehedi Rahman, Tanja Steigner
In the ever-evolving healthcare sector, the widespread adoption of Internet of Things and wearable technologies facilitates remote patient monitoring. However, the existing client/server infrastructure poses significant security and privacy challenges, necessitating strict adherence to healthcare data regulations. To combat these issues, a decentralized approach is imperative, and blockchain technology emerges as a compelling solution for strengthening Internet of Things and medical systems security. This paper introduces HNMblock, a model that elevates the realms of epidemiological monitoring, medical system security, and wellness enhancement. By harnessing the transparency and immutability inherent in blockchain, HNMblock empowers real-time, tamper-proof tracking of epidemiological data, enabling swift responses to disease outbreaks. Furthermore, it fortifies the security of medical systems through advanced cryptographic techniques and smart contracts, with a paramount focus on safeguarding patient privacy. HNMblock also fosters personalized health care, encouraging patient involvement and data-informed decision-making. The integration of blockchain within the healthcare domain, as exemplified by HNMblock, holds the potential to revolutionize data management, epidemiological surveillance, and wellness, as meticulously explored in this research article.
Planetary health is an emergent transdisciplinary field, focused on understanding and addressing the interactions of climate change and human health, which offers interventional challenges given its complexity. While various articles have assessed the use of blockchain (web3) technologies in health, little consideration has been given to the potential use of web3 for addressing planetary health. A scoping review to explore the intersection of web3 and planetary health was conducted. Seven databases (Ovid Medline, Global Health, Web of Science, Scopus, Geobase, ACM Digital Library, and IEEE Xplore) were searched for peer-reviewed literature using key terms relating to planetary health and blockchain. Findings were reported narratively. A total of 3245 articles were identified and screened, with 23 articles included in the final review. The health focus of the articles included pandemics and disease outbreaks, the health of vulnerable groups, population health, health financing, research and medicines use, environmental health, and the negative impacts of blockchain mining on human health. All articles included the use of blockchain technology, with others additionally incorporating smart contracts, the Internet of Things, artificial intelligence and machine learning. The application of web3 to planetary health can be broadly categorised across data, financing, identity, medicines and devices, and research. Shared values that emerged include equity, decentralisation, transparency and trust, and managing complexity. Web3 has the potential to facilitate approaches towards planetary health, with the use of tools and applications that are underpinned by shared values. Further research, particularly primary research into blockchain for public goods and planetary health, will allow this hypothesis to be better tested.
Genomic data provide tremendous healthcare prospects. Large genetic dataset clinical interpretations can enhance healthcare and enable personalized therapy. Genomic data, unlike traditional medical data, indirectly reveals information about offspring and relations of the data owner and remains valid after the owner dies, making genomic dataset sharing difficult. Genomic data must be controlled and owned. Blockchain technology might replace distributed systems to deliver safe and accountable infrastructure. Blockchain-based genomics infrastructure research is rising. In this review chapter, we analyze commercial and academic activities and discusses important potential and obstacles. Blockchain is an immutable transaction record that provides a secure, decentralized system. Users of the network verify transactions using cryptographically signed blocks. In this review, we tried to outline EHR and genetic data exchange issues. Second, we explain why blockchain technology is suited for genomics and healthcare applications. Thirdly, we explain the Ethereum-based blockchain structure, which is better for genomic data–sharing platforms. Fourthly, we assess blockchain-based EHR and genomic data–sharing systems, evaluate their pros and cons, and classify them using various criteria. Finally, we discuss open concerns and provide our advice. In conclusion, blockchain technology may help life sciences and healthcare diagnose, monitor, and treat diseases by combining-omics data with other data types.
Christopher P. Albertyn, Svitlana Surodina, Bo Tan, Tina Woods · 7 authors
UNSTRUCTURED Global healthcare systems need to evolve to ensure optimal, safe, and ethical utilisation of health data and the latest digital technologies, such as Artificial Intelligence (AI), Privacy Enhancing Technologies (PETs), and Distributed Ledger Technologies (DLTs), to meet the challenges of a global ageing population. Simultaneously, the increasing capabilities of remote measurement technologies and the proliferation of 5G networks demonstrates that digital technologies are now more accessible to a much larger population, offering an opportunity for decentralised democratised health data use that supports individual agency. Given the significant international human and economic cost of cognitive decline and dementia, we propose that a person-centred decentralised health data ecosystem, underpinned by these emerging technologies and opportunities, would reduce burden on cognitive healthcare systems by intervening earlier, accelerate clinical research innovation in dementia, and extend cognitive healthspan. Crucially, we argue for the importance of including the individual, as well as other key stakeholders, in the development, continuing operation, and as a shared beneficiary of any potential accrued value emerging from this ecosystem.
Open access
Health, Environment, Cognitive Aging
Artificial Intelligence in Healthcare and Education
There is increasing recognition about health-oriented datasets that could be regarded as intangible assets: distinct assets with future economic benefits but without physical properties. While health-oriented datasets - particularly health records - are ascribed monetary value on the black market, there are few established methods for assessing the value for legitimate research and business purposes. The emergence of blockchain has created new commercial opportunities for transferring assets without intermediaries. Therefore, blockchain is proposed as a medium by which research datasets could be transacted to provide future value. For authorized individuals to verify their transactions, blockchain methodologies offer security, auditability, and transparency. The authors share data valuation methodologies consistent with accounting principles and include discussions of black market valuation of health data. Furthermore, this article describes blockchain-based methods of managing real-time payment/micropayment strategies.
Minsu Kim, Sungho Lee, Chanwon Park, Jemin Lee · 5 authors
The Age of Information (AoI) is a recently proposed metric for quantifying data freshness in real-time status monitoring systems, where timeliness is of importance. In this article, the problem of characterizing and controlling the AoI is studied in the context of blockchain-enabled monitoring networks (BeMNs). In BeMN, status updates from sources are transmitted and recorded in a blockchain. To investigate the statistical characteristics of the AoI in BeMN, the transmission latency and the consensus latency are first rigorously modeled. Then, the average AoI, the AoI violation probability, and the peak AoI violation probability are derived in a closed form so as to quantify the performance of BeMN. Furthermore, a simplified form is derived for the AoI violation probability, and it is shown that this quantity can capture the upper or lower bounds of the actual AoI violation probability. Simulation results show that each BeMN parameters (i.e., target successful transmission probability, block size, and timeout) can have conflicting effects on the AoI-related performance. Subsequently, design insights are provided to maintain the freshness of the status data in BeMN. Then, experimental results with a real Hyperledger Fabric platform further validate the accuracy of our modeling and analysis.
The purpose of this descriptive research paper is to initiate discussions on the use of innovative technologies and their potential to support the research and development of pan-Canadian monitoring and surveillance activities associated with environmental impacts on health and within the health system. Its primary aim is to provide a review of disruptive technologies and their current uses in the environment and in healthcare. Drawing on extensive experience in population-level surveillance through the use of technology, knowledge from prior projects in the field, and conducting a review of the technologies, this paper is meant to serve as the initial steps toward a better understanding of the research area. In doing so, we hope to be able to better assess which technologies might best be leveraged to advance this unique intersection of health and environment. This paper first outlines the current use of technologies at the intersection of public health and the environment, in particular, Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT). The paper provides a description for each of these technologies, along with a summary of their current applications, and a description of the challenges one might face with adopting them. Thereafter, a high-level reference architecture, that addresses the challenges of the described technologies and could potentially be incorporated into the pan-Canadian surveillance system, is conceived and presented.
Blockchain technology is a decentralized system of recording data and performing transactions which is increasingly being used across many industries, including healthcare. It has several unique features like the validation of transaction processes, prevention of systems failure from any single point of transaction, and approval of data sharing with optimal security, to name a few. At the hospital level, blockchain technologies are used in the electronic medical records systems, insurance claims, billing management, and so on. Moreover, this technology is helpful to manage logistic and human resources to achieve the quality of care in learning health systems. In many countries, blockchain is being used to promote patient-centered care by sharing patient data for remote monitoring and management. Furthermore, blockchain technology has the potential to strengthen disease surveillance systems in cases of disease outbreaks resulting in local and global health emergencies. In such conditions, blockchain can be used to identify health security concerns, analyze preventive measures, and facilitate decision-making processes to act rapidly and effectively. Despite its limitations, research, and practice based on blockchain technology have shown promises to strengthen health systems around the world with a potential to reduce the global burden of diseases, mortality, morbidity, and economic costs.
What does an anecdote about John Snow have to do with modern-day epidemiology? And why use it to introduce an issue of the Journal highlighting the challenges of studying disease risks associated with low dose environmental exposures? In this issue, Lilienfeld describes John Snow giving expert-witness testimony on behalf of industry (1). Besides being interesting on a historical basis, this incident raises several issues that are pertinent today. Lilienfeld's paper and the accompanying commentary by Vandenbroucke (2) deal directly or indirectly with the role and responsibilities of expert witnesses, the extrapolation of data on health effects from high dose exposures to low dose exposures, the importance of epidemiology to the development of public health policy, the current debates on environmental justice (3), and the use of the precautionary principle (4) in standard-setting. Furthermore, if faced with an issue similar to that faced by Snow—namely, local residents' being worried about health consequences associated with emanations from factories—would modern-day environmental epidemiologists be any better positioned to carry out appropriate studies and reach sound conclusions? Snow can be seen at once as victim and perpetrator of sins that are common in epidemiology in general and in environmental epidemiology in particular. Was Snow victimized by the medical establishment, including The Lancet, for expressing views that were not commonly held by the scientists of the day? Were his peers outraged because of the reactionary social position he was taking (as suggested by Vandenbroucke)? On the other hand, was he as guilty as proponents of the miasma theory for trying to apply his theory of disease transmission to all situations without allowing for the possibility of multiple disease pathways? Did he fall into the trap of equating the absence of data with an absence of effect? When Snow contended that emanations from the bone-boiling factories were not causing ill health in the community at large, he invoked arguments that are often raised when unexpected health effects are encountered following supposed low dose exposures. One argument is that such health effects are implausible given what we know about high dose exposures. In this instance, Snow noted that the factory workers were not dying and therefore health effects in the community at large were not plausible. A related argument is that, even if workers are dying or suffering other health effects, because of the distance from the exposure source, the exposure levels in the community are probably too low to plausibly affect health. Health effects of low dose exposures are often seen as implausible, even in the face of accumulated consistent evidence. Such arguments have frequently been invoked in environmental epidemiology. Examples of low dose exposures that have been deemed implausible contributors to disease risk based on what is known about high dose exposures include passive smoking, residential radon exposure, childhood lead exposure, electromagnetic fields, and residence near nuclear facilities. If one begins with a fixed idea of what is plausible, arguments regarding susceptible subgroups, inverse dose rate, hormesis, multiple pathways, multifactor etiologies, and complex exposures (e.g., the different constituents of sidestream and mainstream smoke) are untenable. But how do we know that the factory workers were not dying or suffering other ill effects? Snow cited no studies. All too often the absence of data is argued as proof of no effect. This issue becomes especially difficult when regulatory decisions are being made. In the absence of evidence, can something be considered safe? While science is important, it is ultimately social forces, as much as science, that guide regulators in decision-making. Snow's statements and the questions that were put to him call to mind some of the fundamental difficulties inherent in environmental epidemiology. Today, there are numerous examples of residents who live near potential environmental hazards claiming health effects that can never be proven beyond a reasonable doubt. Although the “gold standard” is an unbiased risk estimate with precise confidence limits, studies focused on overt health effects are invariably underpowered because of the small numbers of residents in the neighborhoods of interest. Other creative approaches to assessment of subclinical health effects are more costly and difficult to implement, but even these studies are often too small for conclusive results. Yet, what is the right thing to do? If we wait for strong scientific evidence before we act—if we require proof that workers are dying or evidence of overt illness in the community—have we waited too long? Few clusters are ever resolved with the identification of a causal link between some localized exposure and disease. While many apparent clusters may be artifacts, what is the real cost of the true hazards that cannot be proven? These were the issues facing Parliament when Snow testified on behalf of industry. What is the role of the epidemiologist in this quagmire? In Snow's London, the living conditions of people near the factories were likely to have been dismal. There were no doubt residents who perceived their symptoms as being related to the smells—smells that, if nothing else, impacted the quality of life. Policy-makers must balance “doing the right thing” with regard to human suffering and quality of life with the financial costs of doing so. Epidemiology can only go so far in providing the answers. It is this political and social tug-of-war that makes environmental epidemiology especially difficult. On the one hand, there are well—funded industries with a financial stake in the outcome of such research. As Vandenbroucke notes (2), these industries often are in a position to exploit the many weaknesses that epidemiologists are trained to identify in their own studies and in the work of others to cast potentially damaging results in a more favorable light. On the other hand, there are environmental groups committed to proving that a particular environmental exposure can be linked to a variety of personal complaints; these groups may be motivated by the possibility of effecting social change through science or by the prospect of receiving needed medical attention or financial compensation. Those who attempt to work in this arena often find themselves and their research attacked from all directions. Environmental epidemiology is difficult to conduct today for other reasons as well. Adequate tools with which to measure and quantify exposures are lacking. Studies are often unable to detect meaningful effects because exposures are low, infrequent, or difficult to measure with certainty. How many investigators are willing to tackle this problem? In the case of the bone-boiling factories, would research linking questionnaire data on symptoms to factory releases be believed? Would a study relating distance from the factory to disease be sufficient evidence of effect? What health effects would be plausible based on known biologic mechanisms? How well could those effects be measured, and could they be measured objectively? Is there a biomarker of exposure? If a biomarker exists, does it measure relevant past exposures? Is the measure unaffected by current health status—particularly the disease under study? In addition to Lilienfeld's historical report and Vandenbroucke's commentary, this issue of the Journal features papers that illustrate various aspects of the difficulties faced in studying health effects of environmental exposures. Several of these include innovative attempts to improve the quality of such research. The paper by Viel et al. (5) may come closest to what many may think of as environmental epidemiology. The authors have examined the spatial distribution of soft tissue sarcomas and non-Hodgkin's lymphomas around an incinerator with high dioxin emissions. Their results are suggestive but need to be followed by studies incorporating more rigorous exposure assessment—perhaps a biologic measure of exposure such as that used in the study of polychlorinated biphenyls and breast cancer reported by Zheng et al. (6). Other studies described in this issue used a variety of approaches to exposure assessment. Rondeau et al. (7) linked estimates of levels of aluminum and silica in drinking water to risks of dementia and Alzheimer's disease. Laden et al. (8) used questionnaire data on use of electric blankets to estimate exposure to electromagnetic fields, and Gustavsson et al. (9) used questionnaire data and expert assessment by industrial hygienists to classify environmental and occupational exposures. Radiation workers are one of the few groups for which historical records of personal exposure typically are available. Dupree-Ellis et al. (10) took advantage of such records to estimate cumulative external radiation exposure. Several of the papers evaluate methods for assessing exposure. For example, Oglesby et al. (11) average individual-level annoyance scores to estimate community-level exposure to air pollution. The authors propose that this measure better accounts for exposure variability than data from fixed-site monitoring stations. This is an interesting twist in a field where much work is based on linking data from monitoring stations with population-level mortality statistics. The measure seems to be easy to operationalize, and it correlates well with monitoring station data, although its ultimate utility may be limited. The real gold standard—a more precise direct measure of individual exposure, rather than another indirect measure—is what is needed. Hwang et al. (12) propose an alternative modeling approach whereby air pollution monitoring station data are used to ascribe exposures to individuals with and without school absences due to respiratory disease. Auvinen et al. (13) compare several possible methods for measuring and classifying exposure to electromagnetic fields. This is a topic that has been hurt by the lack of consensus on the best and most appropriate exposure measure, and results tend to vary for studies employing different exposure metrics. The paper by Karagas et al. (14) attempts to link a biologic measure, arsenic in toenails, with an environmental measure of arsenic in water. The toenail measure is likely to reflect total body burden, but it appears to correlate with water only when water levels are high. This presents an interesting regulatory dilemma. The best epidemiologic research may be based on a direct measure of body burden such as levels in toenails, whereas it is water levels that need to be regulated. Studies of toenail arsenic levels may not shed direct light on the link between water levels and disease. As these papers demonstrate, technological advances are making possible a wide range of new study designs and strategies to better assess both exposures and outcomes. Although progress has been made, research in environmental epidemiology is far from perfect. As epidemiologists face pressures and criticisms from industry, regulatory bodies, and other scientific disciplines, it is important to not lose sight of the lessons from John Snow.