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The COVID-19 epidemic has demonstrated the important role that data plays in the response to and management of public health emergencies. It has also heightened awareness of the role that ontologies play in the design of semantically precise data models that improve data interoperability among stakeholders. This paper surveys vocabularies and ontologies relevant to the task of achieving epidemic-related data interoperability. The paper first reviews 16 vocabularies and ontologies with respect to the use cases. Next it identifies patterns of knowledge that are common across multiple vocabularies and ontologies, followed by an analysis of patterns that are missing, based on the use cases. Conclusions show that existing vocabularies and ontologies provide significant coverage of the concepts underlying epidemic use cases, but there remain gaps in the coverage. More work is required to cover missing but significant concepts.
Subject(s)
COVID-19 , Semantics , Humans , COVID-19/epidemiology , KnowledgeABSTRACT
The ability of a health information exchange (HIE) to consolidate information, collected from multiple, disparate information systems, into a single, person-centric health record can provide a comprehensive and longitudinal representation of an individual's medical history. Shared, longitudinal health records can be leveraged to enhance the delivery of individual clinical care and provide opportunities to improve health outcomes at the population level. This chapter describes the clinical benefits imparted by the shared health record (SHR) component an HIE infrastructure. It also characterizes the potential public health benefits of the aggregate level, population health indicators calculated, stored, and distributed by a health management information system (HMIS) component. Tools for visualizing health indicators from the HMIS, including disease surveillance systems developed during the COVID-19 pandemic, are also described. Postpandemic components such as the SHR and HMIS will likely play critical roles in strengthening health information infrastructures in states and nations. © 2023 Elsevier Inc. All rights reserved.
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COVID-19 vaccination uptake has been suboptimal, even in high-risk populations. New approaches are needed to bring vaccination data to the groups leading outreach efforts. This article describes work to make state-level vaccination data more accessible by extending the Bulk Fast Healthcare Interoperability Resource (FHIR) standard to better support the repeated retrieval of vaccination data for coordinated outreach efforts. We also describe a corresponding low-foot-print software for population outreach that automates repeated checks of state-level immunization data and prioritizes outreach by social determinants of health. Together this software offers an integrated approach to addressing vaccination gaps. Several extensions to the Bulk FHIR protocol were needed to support bulk query of immunization records. These are described in detail. The results of a pilot study, using the outreach tool to target a population of 1500 patients are also described. The results confirmed the limitations of current patient-by-patient approach for querying state immunizations systems for population data and the feasibility of a Bulk FHIR approach.
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In today's world, digital technologies are advancing at a rapid pace. Almost every industry has benefited from this ongoing change. In the health sector, the digitization of medical records was proposed decades ago. Whereas some developed countries have successfully adopted and implemented Electronic Health Record (EHR) systems. Developing countries like India still heavily rely on paper-based medical records. Although there are a number of systems for electronic medical record management, they have issues related to interoperability, timely access, and storage. Due to poor infrastructure and design, the current systems are not robust for communicating and tracking medical records. The need for a better EHR system was highly emphasized during the COVID-19 pandemic. The two major shortcomings of the existing system are a lack of interoperability, which causes delays in sharing the information, and a lack of standardization, due to which the data quality of the data that is shared suffers. To mitigate these issues, we need a nationwide EHR system. Another issue is the lack of a ubiquitous UPI (Unique Patient Identifier). In a country like India, the second most populated country in the world, Aadhar is the best option for UPI, which can be used for creating a national EHR system. In this paper, we have presented a framework for a standardized, interoperable, and unified EHR system based on blockchain technology with Aadhar as the UPI. Using blockchain as the base of this model provides numerous advantages over a cloud-based system, like decentralization, better security, immutability, and traceability. © 2023 IEEE.
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This article describes eight guiding principles for the digital transformation of the health sector and identifies their relationship with the COVID-19 pandemic, as well as highlighting their importance to countries undergoing digital transformation processes. In the Region of the Americas, among other gaps, 30% of people do not have access to the Internet, which is why it is mandatory to develop policies and actions to deliver public health interventions equitably and sustainably to ensure that no one is left behind. The eight principles focus on the four areas of a sustainable health system - human, social, economic and environmental - and highlight the broader possibilities using digital technology to impact the sustainability of health systems.
Este artigo descreve oito princípios norteadores para a transformação digital do setor da saúde e identifica sua relação com a pandemia de COVID-19, além de destacar sua importância para os países que estão em processo de transformação digital. Na Região das Américas, entre outras lacunas, 30% das pessoas não têm acesso à Internet; portanto, é imprescindível desenvolver políticas e ações para realizar intervenções de saúde pública de maneira equitativa e sustentável, para assegurar que ninguém seja deixado para trás. Os oito princípios enfocam as quatro áreas de um sistema de saúde sustentável humana, social, econômica e ambiental e destacam possibilidades mais amplas do uso da tecnologia digital para impactar a sustentabilidade dos sistemas de saúde.
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This paper describes the continuation of a project that aims at establishing an interoperable annotation scheme for quantification phenomena as part of the ISO suite of standards for semantic annotation, known as the Semantic Annotation Framework. After a break, caused by the Covid-19 pandemic, the project was relaunched in early 2022 with a second working draft, which deals with certain issues in the annotation of quantification in a more satisfactory way than the original first working draft.
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The statistical community mobilised vigorously from the start of the 2020 SARS-CoV-2 pandemic, following the RSS's long tradition of offering our expertise to help society tackle important issues that require evidence-based decisions. This address aims to capture the highlights of our collective engagement in the pandemic, and the difficulties faced in delivering statistical design and analysis at pace and in communicating to the wider public the many complex issues that arose. I argue that these challenges gave impetus to fruitful new directions in the merging of statistical principles with constraints of agility, responsiveness and societal responsibilities. The lessons learned from this will strengthen the long-term impact of the discipline and of the Society. The need to evaluate policies even in emergency, and to strive for statistical interoperability in future disease surveillance systems is highlighted. In my final remarks, I look towards the future landscape for statistics in the fast-moving world of data science and outline a strategy of visible and growing engagement of the RSS with the data science ecosystem, building on the central position of statistics.
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Public interest in where food comes from and how it is produced, processed, and distributed has increased over the last few decades, with even greater focus emerging during the COVID-19 pandemic. Mounting evidence and experience point to disturbing weaknesses in our food systems' abilities to support human livelihoods and wellbeing, and alarming long-term trends regarding both the environmental footprint of food systems and mounting vulnerabilities to shocks and stressors. How can we tackle the "wicked problems” embedded in a food system? More specifically, how can convergent research programs be designed and resulting knowledge implemented to increase inclusion, sustainability, and resilience within these complex systems, support widespread contributions to and acceptance of solutions to these challenges, and provide concrete benchmarks to measure progress and understand tradeoffs among strategies along multiple dimensions? This article introduces and defines food systems informatics (FSI) as a tool to enhance equity, sustainability, and resilience of food systems through collaborative, user-driven interaction, negotiation, experimentation, and innovation within food systems. Specific benefits we foresee in further development of FSI platforms include the creation of capacity-enabling verifiable claims of sustainability, food safety, and human health benefits relevant to particular locations and products;the creation of better incentives for the adoption of more sustainable land use practices and for the creation of more diverse agro-ecosystems;the wide-spread use of improved and verifiable metrics of sustainability, resilience, and health benefits;and improved human health through better diets.
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Interest in leveraging blockchain technology to boost healthcare and e-health solutions has lately increased. Blockchain has proven to have enormous promise in a range of e-health industries because of its decentralized and reliable nature, including the secure exchange of electronic health records (EHRs) and database access management among numerous medical entities. A unique paradigm known as the "patient-centric approach” places the patient at the center of the healthcare system and gives them complete control over who has access to and can share their personal health information. Strong confidentiality and safety requirements are necessary for health information. Additionally, other concerns must be resolved, such as secrecy, interoperability, scalability, cost-effectiveness, and timeliness. This paper offers a patient-centric privacy-preserving framework for an efficient and safe medical record to address these problems. Based on three parameters transaction cost, execution time, and gas cost. Three blockchain platforms are compared by using the smart contract to find out the suitable platform for the implementation of this framework. Blockchain platforms served as a benchmark for the performance assessment of a designed framework. Although blockchain will not fix every issue in healthcare organizations, it will undoubtedly assist in dramatically reducing some of the most critical ones.
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With the global outbreak of COVID-19, hundreds of pneumonias caused by cold chain products occurred worldwide, which seriously threatened the safety of people's lives and properties. To effectively prevent product quality problems caused by cold chain logistics, it is urgent to establish a cold chain logistics traceability system with interoperability of heterogeneous systems, to record, share and track the temperature, location, time, and other specific information. The traditional cold chain logistics traceability systems have many problems, such as broken cold chains, untrustworthy data, and data tampering and sharing, which hinder the coordination and interaction efficiency of cold chain logistics traceability data. This paper creatively proposes a cold chain logistics traceability system framework based on the identification and resolution system for the Industrial Internet. It establishes a general cold chain logistics traceability identification data model. The system framework and data model can effectively solve the difficulties of multi-code identification and multi-source heterogeneous system interaction, to improve the efficiency of cold chain logistics traceability, and ensure the quality of cold chain logistics products. © 2022 ACM.
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Background: Clinical Trial Recruitment Support Systems can booster patient inclusion of clinical trials by automatically analyzing eligibility criteria based on electronic health records. However, missing interoperability has hindered introduction of those systems on a broader scale. Purpose(s): Our aim was to develop a recruitment support system based on FHIR R4 and evaluate its usage and features in a cardiology department. Methods/Implementation: Clinical conditions, anamnesis, examinations, allergies, medication, laboratory data and echocardiography results were imported as FHIR resources. Trial study nurses and physicians were enabled to add new and edit trial information and input inclusion and exclusion criteria using a web-browser user interface in the hospital intranet. All information were recorded on the server side as the FHIR resources ResearchStudy and Group . Eligibility criteria linked by the logical operation OR were represented by using multiple FHIR Group resources for enrollment. On the client side, eligibility criteria were transformed to a tree-like structure (see Figure 1). Upon user demand, all hospitalized and ambulatory patients in the cardiology department were instantly screened for trial eligibility using the FHIR eligibility criteria on the existing patients' FHIR resources. Furthermore, study personal was able to manually edit trial status (i.e. ineligible, on-study, ..) of patients, which was implemented using the FHIR resource ResearchSubject . Result(s): This implementation of a CTRSS based on FHIR R4 was evaluated in clinical practice: Beginning from 1st April 2021 the application was used as an additional patient screening tool for the four trials CLOSUREAF, FAIR-HF2, SPRIRIT-HF and TORCH-PLUS of the German Centre for Cardiovascular Research. As the COVID-19 pandemic is prohibiting any proper comparison of patient inclusion rates, efficacy of the recruitment support system was tested by comparing the numbers of patients identified by the recruitment support system and enrolled in a trial to the actual number of enrolled patients irrespective of the screening method from 1st April 2021 to 23rd November 2021. The system was able to identify 52 of 55 patients included in those four clinical trials. Conclusion(s): Use of FHIR for defining eligibility criteria of clinical trials may facilitate interoperability and allow automatic screening for eligible patients at multiple sites of different healthcare providers in the future. Upcoming changes in FHIR should allow easier description of OR -linked eligibility criteria. (Figure Presented).
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This chapter discusses the roles of decision support systems in public health as internal tools in public health operations and as external forces used to influence the healthcare system. In that later role, advanced by investments to counter the recent epidemic of opioid overdoses and the COVID-19 pandemic, public health decision support systems are beginning to have major impacts on the healthcare system. Public health decision support systems have many technically advanced features and use data integration, platform-independent implementations of decision-making logic, and advanced approaches for triggering of reporting of cases, based on national standards, to create tools to influence the health system. There is strong data to show the impacts of decision support systems on opioid prescribing and on uptake of vaccines (at a population level) and preliminary data on their value in decision making on vaccine administration at an individual level and for increased reporting of public health notifiable conditions. With deeper integration with electronic health record systems, based on the Fast Healthcare Interoperability Resource standards, and appropriate governmental policy backing, the ability for public health to influence the healthcare system through decision support techniques seems likely to increase over the next few years. © 2023 Elsevier Inc. All rights reserved.
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The rapid development of artificial intelligence, blockchain, and networks, and the sudden appearance of the metaverse, triggered by COVID-19, are both expected and concerned. Along with criticism that it is somewhat exaggerated, it is receiving a lot of attention in that it can provide a foundation for social and economic activities in the virtual world. In this paper, we present the new driving force that re-emerged the metaverse, and analysis on the related standardization status. By the analysis on that, we propose the interface and interworking model required to make the metaverse interoperable with each other. © 2023 Global IT Research Institute (GiRI).
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Policy Points Accurate and reliable data systems are critical for delivering the essential services and foundational capabilities of public health for a 21st -century public health infrastructure. Chronic underfunding, workforce shortages, and operational silos limit the effectiveness of America's public health data systems, with the country's anemic response to COVID-19 highlighting the results of long-standing infrastructure gaps. As the public health sector begins an unprecedented data modernization effort, scholars and policymakers should ensure ongoing reforms are aligned with the five components of an ideal public health data system: outcomes and equity oriented, actionable, interoperable, collaborative, and grounded in a robust public health system.
Subject(s)
COVID-19 , Health Care Reform , Humans , Public Health , Data Systems , Health PolicyABSTRACT
To limit the spread of COVID-19, thermal screening cameras were installed everywhere. These cameras observe many thermal faces. These thermal face data are generally used to monitor strange temperatures for COVID-19 screening or to maintain social distancing. Big data of Thermal face generated everywhere should be used in the more practical functions. We proposed a method to measure non-contact breathing signals using thermal face data. In addition, breathing signals data estimated from thermal face data was converted to DICOM waveform Information Object Definitions (IODs) for interoperability management of medical data. The proposed method was tested on a golden reference (chest belt) with a mean accuracy of 93.52 %. a proposed method that can extract breathing signals using thermal screening cameras that are widely available around the world and manage data as healthcare interoperability information can show important potential in the public, telemedicine field in the future. © 2022 IEEE.
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The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the nation must perceive the health data infrastructure to be a matter of national security. The nation must further invest significantly more in its health data infrastructure. Investments should include the education and training of the public health workforce as informaticians in this domain are currently limited. Finally, investments should strengthen and expand health data utilities that increasingly play a critical role in exchanging information across public health and healthcare organizations.
Subject(s)
COVID-19 , Medical Informatics , United States , Humans , Public Health , PandemicsABSTRACT
The COVID-19 pandemic has heightened the necessity for pervasive data and system interoperability to manage healthcare information and knowledge. There is an urgent need to better understand the role of interoperability in improving the societal responses to the pandemic. This paper explores data and system interoperability, a very specific area that could contribute to fighting COVID-19. Specifically, the authors propose a unified health information system framework to connect data, systems, and devices to increase interoperability and manage healthcare information and knowledge. A blockchain-based solution is also provided as a recommendation for improving the data and system interoperability in healthcare.
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Our case study explored a Local Resilience Forum's (LRF) civil contingency response to COVID‐19 in the United Kingdom. We undertook 19 semistructured ethnographic longitudinal interviews, between March 25, 2020 and February 17, 2021, with a Director of a Civil Contingencies Unit and a Chief Fire Officer who both played key roles within their LRF. Within these interviews, we focused on their strategic level decision‐making and how their relationship with national government impacted on local processes and outcomes. Using a form of grounded theory, our data describe the chronological evolution of an increasingly effective localized approach toward outbreak control and a growing resilience in dealing with concurrent emergency incidents. However, we also highlight how national government organizations imposed central control on aspects of the response in ways that undermined or misaligned with local preparedness. Thus, during emergencies, central governments can undermine the principle of subsidiarity and damage the ways in which LRFs can help scaffold local resilience. Our work contributes to the theoretical understanding of the social psychological factors that can shape the behaviour of responder agencies during a prolonged crisis. In particular, the implications of our analysis for advancing our conceptual understanding of strategic decision‐making during emergencies are discussed.
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Recently, smart medical devices have become preva-lent in remote monitoring of patients and the delivery of medication. The ongoing Covid-19 pandemic situation has boosted the upward trend of the popularity of smart medical devices in the healthcare system. Simultaneously, different device manufacturers and technologies compete for a share in a smart medical device's market, which forces the integration of diverse smart medical de-vices into a common healthcare ecosystem. Hence, modern unified healthcare communication systems (UHCSs) combine ISO/IEEE 11073 and Health Level Seven (HL7) communication standards to support smart medical devices' interoperability and their communication with healthcare providers. Despite their advantages in supporting various smart medical devices and communication technologies, these standards do not provide any security and suffer from vulnerabilities. Existing studies provide stand-alone security solutions to components of UHCSs and do not cover UHCSs holistically. In this paper, we perform a systematic threat analysis of UHCSs that relies on attack-defense tree (ADTree) formalisms. Considering the attack landscape and defense ecosys-tem, we build an ADTree for UHCSs and convert the ADTree to stochastic timed automata (STA) to perform quantitative analysis. Our analysis using UPPAAL SMC shows that the Man-in-the-Middle and unauthorized remote access attacks are the most probable attacks that a malicious entity could pursue, causing mistreatment to patients. We also extract valuable information about the top threats, the likelihood of performing different individual and simultaneous attacks, and the expected cost for attackers. © 2022 IEEE.