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1.
Clin Obstet Gynecol ; 64(2): 392-397, 2021 06 01.
Artigo em Inglês | MEDLINE | ID: mdl-33904844

RESUMO

While telemedicine had been utilized in varying ways over the last several years, it has dramatically accelerated in the era of the COVID-19 pandemic. In this article we describe the privacy issues, in relation to the barriers to care for health care providers and barriers to the obstetric patient, licensing and payments for telehealth services, technological issues and language barriers. While there may be barriers to the use of telehealth services this type of care is feasible and the barriers are surmountable.


Assuntos
Barreiras de Comunicação , Acesso aos Serviços de Saúde , Obstetrícia , Privacidade , Telemedicina , Feminino , Health Insurance Portability and Accountability Act , Acesso aos Serviços de Saúde/ética , Acesso aos Serviços de Saúde/legislação & jurisprudência , Acesso aos Serviços de Saúde/organização & administração , Humanos , Internet , Licenciamento , Obstetrícia/ética , Obstetrícia/legislação & jurisprudência , Obstetrícia/métodos , Obstetrícia/organização & administração , Gravidez , Privacidade/legislação & jurisprudência , Tecnologia , Telemedicina/ética , Telemedicina/legislação & jurisprudência , Telemedicina/métodos , Telemedicina/organização & administração , Estados Unidos
2.
Sensors (Basel) ; 21(7)2021 Mar 28.
Artigo em Inglês | MEDLINE | ID: mdl-33800574

RESUMO

Obesity is a major public health problem worldwide, and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposure at the individual, community, and societal levels. However, monitoring and evaluating such changes is very challenging. The EU Horizon 2020 project "Big Data against Childhood Obesity (BigO)" aims at gathering large-scale data from a large number of children using different sensor technologies to create comprehensive obesity prevalence models for data-driven predictions about specific policies on a community. It further provides real-time monitoring of the population responses, supported by meaningful real-time data analysis and visualisations. Since BigO involves monitoring and storing of personal data related to the behaviours of a potentially vulnerable population, the data representation, security, and access control are crucial. In this paper, we briefly present the BigO system architecture and focus on the necessary components of the system that deals with data access control, storage, anonymisation, and the corresponding interfaces with the rest of the system. We propose a three-layered data warehouse architecture: The back-end layer consists of a database management system for data collection, de-identification, and anonymisation of the original datasets. The role-based permissions and secured views are implemented in the access control layer. Lastly, the controller layer regulates the data access protocols for any data access and data analysis. We further present the data representation methods and the storage models considering the privacy and security mechanisms. The data privacy and security plans are devised based on the types of collected personal, the types of users, data storage, data transmission, and data analysis. We discuss in detail the challenges of privacy protection in this large distributed data-driven application and implement novel privacy-aware data analysis protocols to ensure that the proposed models guarantee the privacy and security of datasets. Finally, we present the BigO system architecture and its implementation that integrates privacy-aware protocols.


Assuntos
Big Data , Segurança Computacional , Criança , Confidencialidade , Data Warehousing , Assistência à Saúde , Humanos , Privacidade
4.
Sensors (Basel) ; 21(9)2021 Apr 21.
Artigo em Inglês | MEDLINE | ID: mdl-33919018

RESUMO

Real-word errors are characterized by being actual terms in the dictionary. By providing context, real-word errors are detected. Traditional methods to detect and correct such errors are mostly based on counting the frequency of short word sequences in a corpus. Then, the probability of a word being a real-word error is computed. On the other hand, state-of-the-art approaches make use of deep learning models to learn context by extracting semantic features from text. In this work, a deep learning model were implemented for correcting real-word errors in clinical text. Specifically, a Seq2seq Neural Machine Translation Model mapped erroneous sentences to correct them. For that, different types of error were generated in correct sentences by using rules. Different Seq2seq models were trained and evaluated on two corpora: the Wikicorpus and a collection of three clinical datasets. The medicine corpus was much smaller than the Wikicorpus due to privacy issues when dealing with patient information. Moreover, GloVe and Word2Vec pretrained word embeddings were used to study their performance. Despite the medicine corpus being much smaller than the Wikicorpus, Seq2seq models trained on the medicine corpus performed better than those models trained on the Wikicorpus. Nevertheless, a larger amount of clinical text is required to improve the results.


Assuntos
Idioma , Semântica , Humanos , Processamento de Linguagem Natural , Privacidade , Probabilidade
5.
Sensors (Basel) ; 21(7)2021 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-33916309

RESUMO

Internet of Vehicles (IoV) has the potential to enhance road-safety with environment sensing features provided by embedded devices and sensors. This benignant feature also raises privacy issues as vehicles announce their fine-grained whereabouts mainly for safety requirements, adversaries can leverage this to track and identify users. Various privacy-preserving schemes have been designed and evaluated, for example, mix-zone, encryption, group forming, and silent-period-based techniques. However, they all suffer inherent limitations. In this paper, we review these limitations and propose WHISPER, a safety-aware location privacy-preserving scheme that adjusts the transmission range of vehicles in order to prevent continuous location monitoring. We detail the set of protocols used by WHISPER, then we compare it against other privacy-preserving schemes. The results show that WHISPER outperformed the other schemes by providing better location privacy levels while still fulfilling road-safety requirements.


Assuntos
Segurança Computacional , Privacidade , Conscientização , Internet
6.
Sensors (Basel) ; 21(8)2021 Apr 19.
Artigo em Inglês | MEDLINE | ID: mdl-33921625

RESUMO

With the recent advances in mobile technologies, biometric verification is being adopted in many smart devices as a means for authenticating their owners. As biometric data leakage may cause stringent privacy issues, many proposals have been offered to guarantee the security of stored biometric data, i.e., biometric template. One of the most promising solutions is the use of a remote server that stores the template in an encrypted form and performs a biometric comparison on the ciphertext domain, using recently proposed functional encryption (FE) techniques. However, the drawback of this approach is that considerable computation is required for the inner-pairing product operation used for the decryption procedure of the underlying FE, which is performed in the authentication phase. In this paper, we propose an enhanced method to accelerate the inner-pairing product computation and apply it to expedite the decryption operation of FE and for faster remote biometric verification. The following two important observations are the basis for our improvement-one of the two arguments for the decryption operation does not frequently change over authentication sessions, and we only need to evaluate the product of multiple pairings, rather than individual pairings. From the results of our experiments, the proposed method reduces the time required to compute an inner-pairing product by 30.7%, compared to the previous best method. With this improvement, the time required for biometric verification is expected to decrease by up to 10.0%, compared to a naive method.


Assuntos
Identificação Biométrica , Aceleração , Biometria , Segurança Computacional , Computadores , Privacidade
7.
Sensors (Basel) ; 21(8)2021 Apr 19.
Artigo em Inglês | MEDLINE | ID: mdl-33921738

RESUMO

In the current epoch of smart homes and cities, personal data such as patients' names, diseases and addresses are often violated. This is frequently associated with the safety of the electronic health records (EHRs) of patients. EHRs have numerous benefits worldwide, but at present, EHR information is subject to considerable security and privacy issues. This paper proposes a way to provide a secure solution to these issues. Previous sophisticated techniques dealing with the protection of EHRs usually make data inaccessible to patients. These techniques struggle to balance data confidentiality, patient demand and constant interaction with provider data. Blockchain technology solves the above problems since it distributes information in a transactional and decentralized manner. The usage of blockchain technology could help the health sector to balance the accessibility and privacy of EHRs. This paper proposes a blockchain security framework (BSF) to effectively and securely store and keep EHRs. It presents a safe and proficient means of acquiring medical information for doctors, patients and insurance agents while protecting the patient's data. This work aims to examine how our proposed framework meets the security needs of doctors, patients and third parties and how the structure addresses safety and confidentiality concerns in the healthcare sector. Simulation outcomes show that this framework efficiently protects EHR data.


Assuntos
Blockchain , Segurança Computacional , Confidencialidade , Registros Eletrônicos de Saúde , Humanos , Privacidade
8.
Sensors (Basel) ; 21(8)2021 Apr 16.
Artigo em Inglês | MEDLINE | ID: mdl-33923842

RESUMO

Presently modern technology makes a significant contribution to the transition from traditional healthcare to smart healthcare systems. Mobile health (mHealth) uses advances in wearable sensors, telecommunications and the Internet of Things (IoT) to propose a new healthcare concept centered on the patient. Patients' real-time remote continuous health monitoring, remote diagnosis, treatment, and therapy is possible in an mHealth system. However, major limitations include the transparency, security, and privacy of health data. One possible solution to this is the use of blockchain technologies, which have found numerous applications in the healthcare domain mainly due to theirs features such as decentralization (no central authority is needed), immutability, traceability, and transparency. We propose an mHealth system that uses a private blockchain based on the Ethereum platform, where wearable sensors can communicate with a smart device (a smartphone or smart tablet) that uses a peer-to-peer hypermedia protocol, the InterPlanetary File System (IPFS), for the distributed storage of health-related data. Smart contracts are used to create data queries, to access patient data by healthcare providers, to record diagnostic, treatment, and therapy, and to send alerts to patients and medical professionals.


Assuntos
Blockchain , Internet das Coisas , Telemedicina , Humanos , Monitorização Fisiológica , Privacidade
9.
Sensors (Basel) ; 21(8)2021 Apr 20.
Artigo em Inglês | MEDLINE | ID: mdl-33924024

RESUMO

Healthcare is now an important part of daily life because of rising consciousness of health management. Medical professionals can know users' health condition if they are able to access information immediately. Telemedicine systems, which provides long distance medical communication and services, is a multi-functional remote medical service that can help patients in bed in long-distance communication environments. As telemedicine systems work in public networks, privacy preservation issue of sensitive and private transmitted information is important. One of the means of proving a user's identity are user-controlled single sign-on (UCSSO) authentication scheme, which can establish a secure communication channel using authenticated session keys between the users and servers of telemedicine systems, without threats of eavesdropping, impersonation, etc., and allow patients access to multiple telemedicine services with a pair of identity and password. In this paper, we proposed a smartcard-based user-controlled single sign-on (SC-UCSSO) for telemedicine systems that not only remains above merits but achieves privacy preservation and enhances security and performance compared to previous schemes that were proved with BAN logic and automated validation of internet security protocols and applications (AVISPA).


Assuntos
Privacidade , Telemedicina , Comunicação , Segurança Computacional , Confidencialidade , Humanos , Sistemas de Informação
10.
Sensors (Basel) ; 21(9)2021 Apr 28.
Artigo em Inglês | MEDLINE | ID: mdl-33925161

RESUMO

Owing to progressive population aging, elderly people (aged 65 and above) face challenges in carrying out activities of daily living, while placement of the elderly in a care facility is expensive and mentally taxing for them. Thus, there is a need to develop their own homes into smart homes using new technologies. However, this raises concerns of privacy and data security for users since it can be handled remotely. Hence, with advancing technologies it is important to overcome this challenge using privacy-preserving and non-intrusive models. For this review, 235 articles were scanned from databases, out of which 31 articles pertaining to in-home technologies that assist the elderly in living independently were shortlisted for inclusion. They described the adoption of various methodologies like different sensor-based mechanisms, wearables, camera-based techniques, robots, and machine learning strategies to provide a safe and comfortable environment to the elderly. Recent innovations have rendered these technologies more unobtrusive and privacy-preserving with increasing use of environmental sensors and less use of cameras and other devices that may compromise the privacy of individuals. There is a need to develop a comprehensive system for smart homes which ensures patient safety, privacy, and data security; in addition, robots should be integrated with the existing sensor-based platforms to assist in carrying out daily activities and therapies as required.


Assuntos
Atividades Cotidianas , Privacidade , Idoso , Envelhecimento , Segurança Computacional , Humanos , Tecnologia
11.
Sci Eng Ethics ; 27(2): 23, 2021 03 29.
Artigo em Inglês | MEDLINE | ID: mdl-33779818

RESUMO

At the beginning of the COVID-19 pandemic, high hopes were placed on digital contact tracing. Digital contact tracing apps can now be downloaded in many countries, but as further waves of COVID-19 tear through much of the northern hemisphere, these apps are playing a less important role in interrupting chains of infection than anticipated. We argue that one of the reasons for this is that most countries have opted for decentralised apps, which cannot provide a means of rapidly informing users of likely infections while avoiding too many false positive reports. Centralised apps, in contrast, have the potential to do this. But policy making was influenced by public debates about the right app configuration, which have tended to focus heavily on privacy, and are driven by the assumption that decentralised apps are "privacy preserving by design". We show that both types of apps are in fact vulnerable to privacy breaches, and, drawing on principles from safety engineering and risk analysis, compare the risks of centralised and decentralised systems along two dimensions, namely the probability of possible breaches and their severity. We conclude that a centralised app may in fact minimise overall ethical risk, and contend that we must reassess our approach to digital contact tracing, and should, more generally, be cautious about a myopic focus on privacy when conducting ethical assessments of data technologies.


Assuntos
Confidencialidade/ética , Busca de Comunicante/ética , Busca de Comunicante/métodos , Armazenamento e Recuperação da Informação/métodos , Aplicativos Móveis , Privacidade , /epidemiologia , Política de Saúde , Humanos , Armazenamento e Recuperação da Informação/ética , Saúde Pública , Smartphone
12.
JMIR Mhealth Uhealth ; 9(3): e23728, 2021 03 30.
Artigo em Inglês | MEDLINE | ID: mdl-33783362

RESUMO

BACKGROUND: The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user's data on that user's device. By keeping data on users' devices, federated learning protects users' private data from data leaks and breaches on the researcher's central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. OBJECTIVE: We review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. METHODS: We applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject. RESULTS: In a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. CONCLUSIONS: Our findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods.


Assuntos
Privacidade , Telemedicina , Simulação por Computador , Humanos , Aprendizado de Máquina , Projetos de Pesquisa
13.
Eur J Health Law ; 28(1): 81-101, 2021 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-33652383

RESUMO

This article reflects on COVID-19 restrictions imposed on elders in Ireland through the lens of the right to private and family life (Article 8 ECHR), focusing on stay at home orders and recommendations advising elders to avoid social contact. Furthermore, we examine restrictions on visiting nursing homes given the high death toll in that setting. In our analysis, we zero in on the principles of foreseeability and proportionality, highlighting areas of concern and aspects that we submit should be considered in a proportionality assessment. Ultimately, we argue that it is a mistake to view the COVID-19 pandemic solely as an emergency. In this manner, the solutions suggested through the law - restrictions on movement and visitation bans - are too narrow and fail to address the underlying structures, such as, issues in the healthcare system, the limited home help for elderly and poor conditions in nursing homes.


Assuntos
/prevenção & controle , Surtos de Doenças/legislação & jurisprudência , Família , Isolamento de Pacientes/legislação & jurisprudência , Privacidade , Visitas a Pacientes/legislação & jurisprudência , Idoso , Instituição de Longa Permanência para Idosos/normas , Humanos , Irlanda/epidemiologia , Casas de Saúde/normas
14.
Eur Rev Med Pharmacol Sci ; 25(5): 2449-2456, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33755984

RESUMO

Countries responded to the COVID-19 pandemic with various levels of restrictions and lockdown in an effort to save lives and prevent the saturation and collapse of national health systems. Unfortunately, the blockades have entailed hefty socioeconomic costs. In order to contrast the spread of the virus, states have used contact tracing technology, in the form of mobile phone applications designed to track close contacts of those infected with COVID-19. Recent research has shown the effectiveness of this solution, particularly when used in conjunction with manual tracking. Nonetheless, the contact tracing app raises concerns due to the potential privacy implications. The authors have delved into the European legislation that protects privacy through the principles of proportionality and minimization, arguing that in order to quickly resolve the pandemic caused by COVID-19, one cannot blindly trust the exclusive help of technology. Instead, we need the involvement of health personnel, scientists, and no less importantly, the citizenry's sense of solidarity and the duty to abide by the rules of social distancing, the use of protective devices and hygiene rules to protect public health.


Assuntos
Busca de Comunicante/métodos , Infecções por Coronavirus/prevenção & controle , /epidemiologia , Telefone Celular , Confidencialidade , Infecções por Coronavirus/epidemiologia , Humanos , Aplicativos Móveis , Pandemias/prevenção & controle , Privacidade , Tecnologia
15.
J World Fed Orthod ; 10(1): 9-13, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33642260

RESUMO

Recent advances in technology, growing patient demand, and the need for social distancing due to Coronavirus Disease 2019 has expedited adoption of teledentistry in orthodontics as a means of consulting and monitoring a patient without an in-office visit. However, a lack of computer literacy and knowledge of software choices, and concerns regarding patient safety and potential infringement of regulations can make venturing into this new technology intimidating. In this article, various types of teledentistry systems for orthodontic practices, implementation guidelines, and important regulatory considerations on the use of teledentistry for orthodontic purposes are discussed. A thorough evaluation of the intended use of the software should precede commitment to a service. Selected service should be Health Insurance Portability and Accountability Act compliant at minimum and a Business Associate Agreement should be in place for protection of privacy. Ensuring the compatibility of the designated clinic computer with the system's requirements and installation of all safeguards must follow. Appointments should be documented in the same manner as in-office visits and teledentistry patients must be located within the clinician's statutory license boundary. Informed consent forms should include teledentistry or a supplemental teledentistry consent form should be used. Malpractice insurance covers everything usual and customary under the provider's license but the need for cyber liability insurance increases with teledentistry.


Assuntos
/epidemiologia , Ortodontia , Telemedicina/métodos , Inteligência Artificial , Health Insurance Portability and Accountability Act , Humanos , Pandemias , Pneumonia Viral/epidemiologia , Privacidade/legislação & jurisprudência , Estados Unidos
16.
Conserv Biol ; 35(2): 437-446, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33749044

RESUMO

Social media data are being increasingly used in conservation science to study human-nature interactions. User-generated content, such as images, video, text, and audio, and the associated metadata can be used to assess such interactions. A number of social media platforms provide free access to user-generated social media content. However, similar to any research involving people, scientific investigations based on social media data require compliance with highest standards of data privacy and data protection, even when data are publicly available. Should social media data be misused, the risks to individual users' privacy and well-being can be substantial. We investigated the legal basis for using social media data while ensuring data subjects' rights through a case study based on the European Union's General Data Protection Regulation. The risks associated with using social media data in research include accidental and purposeful misidentification that has the potential to cause psychological or physical harm to an identified person. To collect, store, protect, share, and manage social media data in a way that prevents potential risks to users involved, one should minimize data, anonymize data, and follow strict data management procedure. Risk-based approaches, such as a data privacy impact assessment, can be used to identify and minimize privacy risks to social media users, to demonstrate accountability and to comply with data protection legislation. We recommend that conservation scientists carefully consider our recommendations in devising their research objectives so as to facilitate responsible use of social media data in conservation science research, for example, in conservation culturomics and investigations of illegal wildlife trade online.


Assuntos
Privacidade , Mídias Sociais , Conservação dos Recursos Naturais , Humanos
17.
Pac Symp Biocomput ; 26: 26-37, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33691001

RESUMO

Machine learning is powerful to model massive genomic data while genome privacy is a growing concern. Studies have shown that not only the raw data but also the trained model can potentially infringe genome privacy. An example is the membership inference attack (MIA), by which the adversary can determine whether a specific record was included in the training dataset of the target model. Differential privacy (DP) has been used to defend against MIA with rigorous privacy guarantee by perturbing model weights. In this paper, we investigate the vulnerability of machine learning against MIA on genomic data, and evaluate the effectiveness of using DP as a defense mechanism. We consider two widely-used machine learning models, namely Lasso and convolutional neural network (CNN), as the target models. We study the trade-off between the defense power against MIA and the prediction accuracy of the target model under various privacy settings of DP. Our results show that the relationship between the privacy budget and target model accuracy can be modeled as a log-like curve, thus a smaller privacy budget provides stronger privacy guarantee with the cost of losing more model accuracy. We also investigate the effect of model sparsity on model vulnerability against MIA. Our results demonstrate that in addition to prevent overfitting, model sparsity can work together with DP to significantly mitigate the risk of MIA.


Assuntos
Biologia Computacional , Privacidade , Genômica , Humanos , Aprendizado de Máquina , Redes Neurais de Computação
18.
Sci Adv ; 7(15)2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33712416

RESUMO

The efficacy of digital contact tracing against coronavirus disease 2019 (COVID-19) epidemic is debated: Smartphone penetration is limited in many countries, with low coverage among the elderly, the most vulnerable to COVID-19. We developed an agent-based model to precise the impact of digital contact tracing and household isolation on COVID-19 transmission. The model, calibrated on French population, integrates demographic, contact and epidemiological information to describe exposure and transmission of COVID-19. We explored realistic levels of case detection, app adoption, population immunity, and transmissibility. Assuming a reproductive ratio R = 2.6 and 50% detection of clinical cases, a ~20% app adoption reduces peak incidence by ~35%. With R = 1.7, >30% app adoption lowers the epidemic to manageable levels. Higher coverage among adults, playing a central role in COVID-19 transmission, yields an indirect benefit for the elderly. These results may inform the inclusion of digital contact tracing within a COVID-19 response plan.


Assuntos
/epidemiologia , Busca de Comunicante , Privacidade , Smartphone , Adulto , Idoso , Humanos
19.
Appl Clin Inform ; 12(2): 229-236, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33763847

RESUMO

BACKGROUND: Queensland, Australia has been successful in containing the COVID-19 pandemic. Underpinning that response has been a highly effective virus containment strategy which relies on identification, isolation, and contact tracing of cases. The dramatic emergence of the COVID-19 pandemic rendered traditional paper-based systems for managing contact tracing no longer fit for purpose. A rapid digital transformation of the public health contact tracing system occurred to support this effort. OBJECTIVES: The objectives of the digital transformation were to shift legacy systems (paper or standalone electronic systems) to a digitally enabled public health system, where data are centered around the consumer rather than isolated databases. The objective of this paper is to outline this case study and detail the lessons learnt to inform and give confidence to others contemplating digitization of public health systems in response to the COVID-19 pandemic. METHODS: This case study is set in Queensland, Australia. Universal health care is available. A multidisciplinary team was established consisting of clinical informaticians, developers, data strategists, and health information managers. An agile "pair-programming" approach was undertaken to application development and extensive change efforts were made to maximize adoption of the new digital workflows. Data governance and flows were changed to support rapid management of the pandemic. RESULTS: The digital coronavirus application (DCOVA) is a web-based application that securely captures information about people required to quarantine and creates a multiagency secure database to support a successful containment strategy. CONCLUSION: Most of the literature surrounding digital transformation allows time for significant consultation, which was simply not possible under crisis conditions. Our observation is that staff was willing to adopt new digital systems because the reason for change (the COVID-19 pandemic) was clearly pressing. This case study highlights just how critical a unified purpose, is to successful, rapid digital transformation.


Assuntos
/epidemiologia , Informática Médica , Pandemias , Saúde Pública , Confidencialidade , Comportamento Cooperativo , Política de Saúde , Humanos , Privacidade , Risco , Segurança , Interface Usuário-Computador
20.
JMIR Public Health Surveill ; 7(4): e26460, 2021 04 06.
Artigo em Inglês | MEDLINE | ID: mdl-33727212

RESUMO

The enormous pressure of the increasing case numbers experienced during the COVID-19 pandemic has given rise to a variety of novel digital systems designed to provide solutions to unprecedented challenges in public health. The field of algorithmic contact tracing, in particular, an area of research that had previously received limited attention, has moved into the spotlight as a crucial factor in containing the pandemic. The use of digital tools to enable more robust and expedited contact tracing and notification, while maintaining privacy and trust in the data generated, is viewed as key to identifying chains of transmission and close contacts, and, consequently, to enabling effective case investigations. Scaling these tools has never been more critical, as global case numbers have exceeded 100 million, as many asymptomatic patients remain undetected, and as COVID-19 variants begin to emerge around the world. In this context, there is increasing attention on blockchain technology as a part of systems for enhanced digital algorithmic contact tracing and reporting. By analyzing the literature that has emerged from this trend, the common characteristics of the designs proposed become apparent. An archetypal system architecture can be derived, taking these characteristics into consideration. However, assessing the utility of this architecture using a recognized evaluation framework shows that the added benefits and features of blockchain technology do not provide significant advantages over conventional centralized systems for algorithmic contact tracing and reporting. From our study, it, therefore, seems that blockchain technology may provide a more significant benefit in other areas of public health beyond contact tracing.


Assuntos
Algoritmos , Blockchain , Busca de Comunicante , Infecções por Coronavirus , Privacidade , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/epidemiologia , Feminino , Humanos , Masculino , Saúde Pública
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