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1.
BMJ Open ; 12(11): e066650, 2022 11 16.
Artigo em Inglês | MEDLINE | ID: mdl-36385017

RESUMO

OBJECTIVE: To inform UK service development to support medical abortion at home, appropriate for person and context. DESIGN: Realist review SETTING/PARTICIPANTS: Peer-reviewed literature from 1 January 2000 to 9 December 2021, describing interventions or models of home abortion care. Participants included people seeking or having had an abortion. INTERVENTIONS: Interventions and new models of abortion care relevant to the UK. OUTCOME MEASURES: Causal explanations, in the form of context-mechanism-outcome configurations, to test and develop our realist programme theory. RESULTS: We identified 12 401 abstracts, selecting 944 for full text assessment. Our final review included 50 papers. Medical abortion at home is safe, effective and acceptable to most, but clinical pathways and user experience are variable and a minority would not choose this method again. Having a choice of abortion location remains essential, as some people are unable to have a medical abortion at home. Choice of place of abortion (home or clinical setting) was influenced by service factors (appointment number, timing and wait-times), personal responsibilities (caring/work commitments), geography (travel time/distance), relationships (need for secrecy) and desire for awareness/involvement in the process. We found experiences could be improved by offering: an option for self-referral through a telemedicine consultation, realistic information on a range of experiences, opportunities to personalise the process, improved pain relief, and choice of when and how to discuss contraception. CONCLUSIONS: Acknowledging the work done by patients when moving medical abortion care from clinic to home is important. Patients may benefit from support to: prepare a space, manage privacy and work/caring obligations, decide when/how to take medications, understand what is normal, assess experience and decide when and how to ask for help. The transition of this complex intervention when delivered outside healthcare environments could be supported by strategies that reduce surprise or anxiety, enabling preparation and a sense of control.


Assuntos
Aborto Induzido , Serviços de Assistência Domiciliar , Gravidez , Feminino , Humanos , Aborto Induzido/métodos , Instituições de Assistência Ambulatorial , Privacidade
2.
J Med Syst ; 46(12): 96, 2022 Nov 16.
Artigo em Inglês | MEDLINE | ID: mdl-36380246

RESUMO

Petabytes of health data are collected annually across the globe in electronic health records (EHR), including significant information stored as unstructured free text. However, the lack of effective mechanisms to securely share clinical text has inhibited its full utilization. We propose a new method, DataSifterText, to generate partially synthetic clinical free-text that can be safely shared between stakeholders (e.g., clinicians, STEM researchers, engineers, analysts, and healthcare providers), limiting the re-identification risk while providing significantly better utility preservation than suppressing or generalizing sensitive tokens. The method creates partially synthetic free-text data, which inherits the joint population distribution of the original data, and disguises the location of true and obfuscated words. Under certain obfuscation levels, the resulting synthetic text was sufficiently altered with different choices, orders, and frequencies of words compared to the original records. The differences were comparable to machine-generated (fully synthetic) text reported in previous studies. We applied DataSifterText to two medical case studies. In the CDC work injury application, using privacy protection, 60.9-86.5% of the synthetic descriptions belong to the same cluster as the original descriptions, demonstrating better utility preservation than the naïve content suppressing method (45.8-85.7%). In the MIMIC III application, the generated synthetic data maintained over 80% of the original information regarding patients' overall health conditions. The reported DataSifterText statistical obfuscation results indicate that the technique provides sufficient privacy protection (low identification risk) while preserving population-level information (high utility).


Assuntos
Registros Eletrônicos de Saúde , Privacidade , Humanos
3.
Brief Bioinform ; 23(6)2022 Nov 19.
Artigo em Inglês | MEDLINE | ID: mdl-36384083

RESUMO

BACKGROUND: Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in third-party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization. RESULTS: Here, we present SIGFRIED, which makes use of existing reference panels with a projection-based approach that simplifies kinship estimation in the admixed populations. We use simulated and real datasets to demonstrate the accuracy and efficiency of kinship estimation. We present a secure federated kinship estimation framework and implement a secure kinship estimator using homomorphic encryption-based primitives for computing relatedness between samples in two different sites while genotype data are kept confidential. Source code and documentation for our methods can be found at https://doi.org/10.5281/zenodo.7053352. CONCLUSIONS: Analysis of relatedness is fundamentally important for identifying relatives, in association studies, and for estimation of population-level estimates of inbreeding. As the awareness of individual and group genomic privacy is growing, privacy-preserving methods for the estimation of relatedness are needed. Presented methods alleviate the ethical and privacy concerns in the analysis of relatedness in admixed, historically isolated and underrepresented populations. SHORT ABSTRACT: Genetic relatedness is a central quantity used for finding relatives in databases, correcting biases in genome wide association studies and for estimating population-level statistics. Methods for estimating genetic relatedness have high computational requirements, and occasionally do not consider individuals from admixed ancestries. Furthermore, the ethical concerns around using genetic data and calculating relatedness are not considered. We present a projection-based approach that can efficiently and accurately estimate kinship. We implement our method using encryption-based techniques that provide provable security guarantees to protect genetic data while kinship statistics are computed among multiple sites.


Assuntos
Estudo de Associação Genômica Ampla , Privacidade , Humanos , Genótipo , Privacidade Genética , Genoma
4.
Sensors (Basel) ; 22(21)2022 Oct 24.
Artigo em Inglês | MEDLINE | ID: mdl-36365823

RESUMO

We present automatically parameterised Fully Homomorphic Encryption (FHE) for encrypted neural network inference and exemplify our inference over FHE-compatible neural networks with our own open-source framework and reproducible examples. We use the fourth generation Cheon, Kim, Kim, and Song (CKKS) FHE scheme over fixed points provided by the Microsoft Simple Encrypted Arithmetic Library (MS-SEAL). We significantly enhance the usability and applicability of FHE in deep learning contexts, with a focus on the constituent graphs, traversal, and optimisation. We find that FHE is not a panacea for all privacy-preserving machine learning (PPML) problems and that certain limitations still remain, such as model training. However, we also find that in certain contexts FHE is well-suited for computing completely private predictions with neural networks. The ability to privately compute sensitive problems more easily while lowering the barriers to entry can allow otherwise too-sensitive fields to begin advantaging themselves of performant third-party neural networks. Lastly, we show how encrypted deep learning can be applied to a sensitive real-world problem in agri-food, i.e., strawberry yield forecasting, demonstrating competitive performance. We argue that the adoption of encrypted deep learning methods at scale could allow for a greater adoption of deep learning methodologies where privacy concerns exist, hence having a large positive potential impact within the agri-food sector and its journey to net zero.


Assuntos
Segurança Computacional , Fragaria , Privacidade , Redes Neurais de Computação , Aprendizado de Máquina
5.
Sensors (Basel) ; 22(21)2022 Oct 26.
Artigo em Inglês | MEDLINE | ID: mdl-36365884

RESUMO

Although many studies have been devoted to integrating blockchain into IoT device management, access control, data integrity, security, and privacy, blockchain-facilitated IoT communication is still much less studied. Blockchain has great potential in decentralizing and securing IoT communications. In this paper, we propose an innovative IoT service platform powered by the consortium blockchain technology. The proposed platform abstracts machine-to-machine (M2M) and human-to-machine (H2M) communications into services provided by IoT devices. Then, it materializes the data exchange of the IoT network through smart contracts and blockchain transactions. Additionally, we introduce the auxiliary storage layer to the proposed platform to address various off-chain data storage needs. Our proof-of-concept implementation was tested against various workloads and connection sizes under different block configurations to evaluate the platform's transaction throughput, latency, and hardware utilization. The experimental results demonstrate that our solution can maintain high performance with a throughput of approximately 800 reads per second (RPS), 50-80 transactions per second (TPS), and a latency of 50 ms-2 s under light to moderate workloads. Our extensive evaluation of the performance impact of batch size, batch timeout, and connection size also provides valuable insights into the optimization of blockchain configuration for achieving high performance.


Assuntos
Blockchain , Humanos , Privacidade , Armazenamento e Recuperação da Informação
6.
Sensors (Basel) ; 22(21)2022 Oct 28.
Artigo em Inglês | MEDLINE | ID: mdl-36365952

RESUMO

In this paper, we propose a novel design, called MixNN, for protecting deep learning model structure and parameters since the model consists of several layers and each layer contains its own structure and parameters. The layers in a deep learning model of MixNN are fully decentralized. It hides communication address, layer parameters and operations, and forward as well as backward message flows among non-adjacent layers using the ideas from mix networks. MixNN has the following advantages: (i) an adversary cannot fully control all layers of a model, including the structure and parameters; (ii) even some layers may collude but they cannot tamper with other honest layers; (iii) model privacy is preserved in the training phase. We provide detailed descriptions for deployment. In one classification experiment, we compared a neural network deployed in a virtual machine with the same one using the MixNN design on the AWS EC2. The result shows that our MixNN retains less than 0.001 difference in terms of classification accuracy, while the whole running time of MixNN is about 7.5 times slower than the one running on a single virtual machine.


Assuntos
Aprendizado Profundo , Redes Neurais de Computação , Privacidade
7.
Sensors (Basel) ; 22(21)2022 Oct 28.
Artigo em Inglês | MEDLINE | ID: mdl-36365960

RESUMO

Federated learning is a type of privacy-preserving, collaborative machine learning. Instead of sharing raw data, the federated learning process cooperatively exchanges the model parameters and aggregates them in a decentralized manner through multiple users. In this study, we designed and implemented a hierarchical blockchain system using a public blockchain for a federated learning process without a trusted curator. This prevents model-poisoning attacks and provides secure updates of a global model. We conducted a comprehensive empirical study to characterize the performance of federated learning in our testbed and identify potential performance bottlenecks, thereby gaining a better understanding of the system.


Assuntos
Blockchain , Privacidade , Aprendizado de Máquina
8.
Sensors (Basel) ; 22(21)2022 Oct 29.
Artigo em Inglês | MEDLINE | ID: mdl-36365991

RESUMO

With the fast development of blockchain technology in the latest years, its application in scenarios that require privacy, such as health area, have become encouraged and widely discussed. This paper presents an architecture to ensure the privacy of health-related data, which are stored and shared within a blockchain network in a decentralized manner, through the use of encryption with the RSA, ECC, and AES algorithms. Evaluation tests were performed to verify the impact of cryptography on the proposed architecture in terms of computational effort, memory usage, and execution time. The results demonstrate an impact mainly on the execution time and on the increase in the computational effort for sending data to the blockchain, which is justifiable considering the privacy and security provided with the architecture and encryption.


Assuntos
Blockchain , Privacidade , Atenção à Saúde , Algoritmos , Tecnologia , Segurança Computacional
9.
Sensors (Basel) ; 22(21)2022 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-36366105

RESUMO

As the world is gradually moving towards digitization, forgery of vital digital documents has become relatively easy. Therefore, the need for efficient and secure verification and authentication practices of digital documents is also increasing. Self-sovereign identity (SSI) is a set of technologies that build on core concepts in identity management, blockchain technology, and cryptography. SSI enables entities to create fraud-proof verifiable credentials and instantly verify the authenticity of a digital credential. The online document verification solutions must deal with a myriad of issues in regard to privacy and security. Moreover, various challenging and tedious processes have made document verification overly complex and time-consuming which motivated us to conduct this research. This work presents a novel framework for online document verification based on SSI technology. The solution address the complexity and interoperability issues that are present in the current digital document verification systems. We look at a particular use case, i.e., document verification in online loan processing and evaluate how this proposed approach can make an impact on the existing system. Our solution based on SSI standards replaces the intermediary and enables trust between players in the ecosystem. The technology also holds the potential to make the system more efficient, interoperable, and privacy-preserving.


Assuntos
Blockchain , Ecossistema , Privacidade , Confiança , Tecnologia
10.
Sensors (Basel) ; 22(21)2022 Nov 07.
Artigo em Inglês | MEDLINE | ID: mdl-36366261

RESUMO

Smart home technologies have attracted more users in recent years due to significant advancements in their underlying enabler components, such as sensors, actuators, and processors, which are spreading in various domains and have become more affordable. However, these IoT-based solutions are prone to data leakage; this privacy issue has motivated researchers to seek a secure solution to overcome this challenge. In this regard, wireless signal eavesdropping is one of the most severe threats that enables attackers to obtain residents' sensitive information. Even if the system encrypts all communications, some cyber attacks can still steal information by interpreting the contextual data related to the transmitted signals. For example, a "fingerprint and timing-based snooping (FATS)" attack is a side-channel attack (SCA) developed to infer in-home activities passively from a remote location near the targeted house. An SCA is a sort of cyber attack that extracts valuable information from smart systems without accessing the content of data packets. This paper reviews the SCAs associated with cyber-physical systems, focusing on the proposed solutions to protect the privacy of smart homes against FATS attacks in detail. Moreover, this work clarifies shortcomings and future opportunities by analyzing the existing gaps in the reviewed methods.


Assuntos
Segurança Computacional , Privacidade , Confidencialidade , Tecnologia sem Fio , Tecnologia
11.
J Med Internet Res ; 24(11): e41750, 2022 11 04.
Artigo em Inglês | MEDLINE | ID: mdl-36331535

RESUMO

The federal Trusted Exchange Framework and Common Agreement (TEFCA) aims to reduce fragmentation of patient records by expanding query-based health information exchange with nationwide connectivity for diverse purposes. TEFCA provides a common agreement and security framework allowing clinicians, and possibly insurance company staff, public health officials, and other authorized users, to query for health information about hundreds of millions of patients. TEFCA presents an opportunity to weave information exchange into the fabric of our national health information economy. We define 3 principles to promote patient autonomy and control within TEFCA: (1) patients can query for data about themselves, (2) patients can know when their data are queried and shared, and (3) patients can configure what is shared about them. We believe TEFCA should address these principles by the time it launches. While health information exchange already occurs on a large scale today, the launch of TEFCA introduces a major, new, and cohesive component of 21st-century US health care information infrastructure. We strongly advocate for a substantive role for the patient in TEFCA, one that will be a model for other systems and policies.


Assuntos
Troca de Informação em Saúde , Health Insurance Portability and Accountability Act , Estados Unidos , Humanos , Privacidade , Confidencialidade , Segurança Computacional
12.
PLoS One ; 17(11): e0276442, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36350919

RESUMO

Patients often provide untruthful information about their health to avoid embarrassment, evade treatment, or prevent financial loss. Privacy disclosures (e.g. HIPAA) intended to dissuade privacy concerns may actually increase patient lying. We used new mouse tracking-based technology to detect lies through mouse movement (distance and time to response) and patient answer adjustment in an online controlled study of 611 potential patients, randomly assigned to one of six treatments. Treatments differed in the notices patients received before health information was requested, including notices about privacy, benefits of truthful disclosure, and risks of inaccurate disclosure. Increased time or distance of device mouse movement and greater adjustment of answers indicate less truthfulness. Mouse tracking revealed a significant overall effect (p<0.001) by treatment on the time to reach their final choice. The control took the least time indicating greater truthfulness and the privacy + risk group took the longest indicating least truthfulness. Privacy, risk, and benefit disclosure statements led to greater lying. These differences were moderated by gender. Mouse tracking results largely confirmed the answer adjustment lie detection method with an overall treatment effect (p < .0001) and gender differences (p < .0001) on truthfulness. Privacy notices led to decreased patient honesty. Privacy notices should perhaps be administered well before personal health disclosure is requested to minimize patient untruthfulness. Mouse tracking and answer adjustment appear to be health care lie-detection methods to enhance optimal diagnosis and treatment.


Assuntos
Confidencialidade , Privacidade , Revelação , Coleta de Dados , Atenção à Saúde
13.
J Med Internet Res ; 24(11): e36553, 2022 Nov 04.
Artigo em Inglês | MEDLINE | ID: mdl-36331530

RESUMO

BACKGROUND: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. OBJECTIVE: This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. METHODS: This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. RESULTS: We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. CONCLUSIONS: This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. TRIAL REGISTRATION: PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590.


Assuntos
Inteligência Ambiental , Inteligência Artificial , Humanos , Idoso , Revisões Sistemáticas como Assunto , Tecnologia , Privacidade
14.
N Engl J Med ; 387(21): 1913-1916, 2022 Nov 24.
Artigo em Inglês | MEDLINE | ID: mdl-36409487

Assuntos
Privacidade , Humanos
15.
Sci Rep ; 12(1): 20210, 2022 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-36418501

RESUMO

The edge computing paradigm has recently drawn significant attention from industry and academia. Due to the advantages in quality-of-service metrics, namely, latency, bandwidth, energy efficiency, privacy, and security, deploying artificial intelligence (AI) models at the network edge has attracted widespread interest. Edge-AI has seen applications in diverse domains that involve large amounts of data. However, poor dataset quality plagues this compute regime owing to numerous data corruption sources, including missing data. As such systems are increasingly being deployed in mission-critical applications, mitigating the effects of corrupted data becomes important. In this work, we propose a strategy based on data imputation using neural inversion, DINI. It trains a surrogate model and runs data imputation in an interleaved fashion. Unlike previous works, DINI is a model-agnostic framework applicable to diverse deep learning architectures. DINI outperforms state-of-the-art methods by at least 10.7% in average imputation error. Applying DINI to mission-critical applications can increase prediction accuracy to up to 99% (F1 score of 0.99), resulting in significant gains compared to baseline methods.


Assuntos
Inteligência Artificial , Benchmarking , Humanos , Inversão Cromossômica , Indústrias , Privacidade
16.
Rev. bioét. derecho ; 56: 29-54, Nov. 2022.
Artigo em Espanhol | IBECS | ID: ibc-210235

RESUMO

En ausencia de instrumentos internacionales que establezcan pautas comunes sobre los acuerdos de gestación por sustitución transfronteriza, los ordenamientos que los prohíben o los consideran nulos han tenido que enfrentarse a la cuestión de sus efectos, lo que ya ha dado lugar a varios pronunciamientos del Tribunal Europeo de Derechos Humanos. A partir del precedente Mennesson c. Francia(2014), el trabajo analiza las diferentes aproximaciones al fenómeno que han sido objeto de escrutinio por parte del TEDH. El trabajo presta una atención especial, por su mayor frecuencia, a los casos que tienen origen en la negativa de un estado a reconocer la filiación resultante de un acuerdo de gestación por sustitución celebrado fuera de sus fronteras y, en particular, al peso otorgado a la exigencia de vínculo genético entre el menor y al menos un progenitor de intención. Los más recientes Valdís Fjölnisdóttir y otros c. Islandia(2021) y A.M. c. Noruega(2022) evidencian quelimitar el reconocimiento de efectos de estos acuerdos a los casos en que existe dicho vínculo no es coherentecon el interés superior de los menores que resultan de los mismos, en especial cuando su adopción ya no es posible.(AU)


A manca d’instruments internacionals que estableixin pautes comunes sobre els acords de gestació per substitució transfronterera, els ordenaments que els prohibeixen o els consideren nuls s’han hagut d’enfrontar a la qüestió dels seus efectes, el que ja ha donat lloc a diversos pronunciamentsper part del Tribunal Europeu de Drets Humans. A partir del precedent Mennesson c. França (2014), el treball analitza les diferents aproximacions al fenomen que han estat objecte d’escrutini per part del TEDH. El treball posa una atenció especial, atesa la seva major freqüència, en els casos que s’originen en la negativa d’un estat a reconèixer la filiació resultant d’un acord de gestació per substitució celebrat fora de les seves fronteres i, en particular, en el pes atorgat a l’exigència de vincle genètic entre el menor i com a mínim un progenitor d’intenció. Els més recents Valdís Fjölnisdóttir i altres c. Islàndia (2021) i A.M. c. Noruega (2022) evidencien que limitar el reconeixement d’efectes d’aquests acords als casos d’existència del mencionat vincle no és coherent amb l’interès superior dels menors que en resulten, en especial quan la seva adopció ja no és possible.(AU)


In the absence of international instruments establishing common guidelines for cross-border surrogacy agreements, jurisdictions that prohibit them or consider them null and void have been confronted with the question of their effects, which has already led to several rulings by the European Court of Human Rights. Based onthe leading case Mennesson v. France (2014), this paper analyses the different approaches to the phenomenon which have been scrutinized by the ECtHR. Due to their greater frequency, the paper pays special attention to cases arising from a state’s refusal to recognize parenthood resulting froma surrogacy arrangement concluded outside its borders and, in particular, to the weight given to the requirement of a genetic link between the child and at least one intended parent. The more recent Valdís Fjölnisdóttir and other v. Iceland(2021) and A.M. v. Norway(2022) make it clear that limiting the recognition of the effects of these arrangements to cases where such a link exist is not consistent with the best interests of the resulting children, especially when adoption is no longer possible.(AU)


Assuntos
Humanos , Direitos Humanos , Gravidez , Direito Internacional , Cooperação Internacional , Família , Adoção , Defesa da Criança e do Adolescente , Custódia da Criança , Privacidade , Bioética , Ética , Princípios Morais
17.
BMC Res Notes ; 15(1): 337, 2022 Oct 31.
Artigo em Inglês | MEDLINE | ID: mdl-36316778

RESUMO

OBJECTIVE: The aim of this study was to determine whether a secure, privacy-preserving record linkage (PPRL) methodology can be implemented in a scalable manner for use in a large national clinical research network. RESULTS: We established the governance and technical capacity to support the use of PPRL across the National Patient-Centered Clinical Research Network (PCORnet®). As a pilot, four sites used the Datavant software to transform patient personally identifiable information (PII) into de-identified tokens. We queried the sites for patients with a clinical encounter in 2018 or 2019 and matched their tokens to determine whether overlap existed. We described patient overlap among the sites and generated a "deduplicated" table of patient demographic characteristics. Overlapping patients were found in 3 of the 6 site-pairs. Following deduplication, the total patient count was 3,108,515 (0.11% reduction), with the largest reduction in count for patients with an "Other/Missing" value for Sex; from 198 to 163 (17.6% reduction). The PPRL solution successfully links patients across data sources using distributed queries without directly accessing patient PII. The overlap queries and analysis performed in this pilot is being replicated across the full network to provide additional insight into patient linkages among a distributed research network.


Assuntos
Registros Eletrônicos de Saúde , Privacidade , Humanos , Registro Médico Coordenado/métodos , Bases de Dados Factuais , Assistência Centrada no Paciente
18.
Stud Health Technol Inform ; 299: 20-29, 2022 Nov 03.
Artigo em Inglês | MEDLINE | ID: mdl-36325843

RESUMO

In this paper, we present on ongoing research to use a socio-technical privacy, information security and cybersecurity model to support the design, development and delivery processes of health care services for aging in place. The current research in gerontechnological services development is reviewed, and experiences from the use of serious games to evaluate the model are outlined.


Assuntos
Vida Independente , Privacidade , Segurança Computacional
19.
Stud Health Technol Inform ; 299: 104-117, 2022 Nov 03.
Artigo em Inglês | MEDLINE | ID: mdl-36325851

RESUMO

From beginning to today, pHealth has been a data driven service that collects and uses personal health information (PHI) for personal health services and personalized healthcare. As a result, pHealth services use intensively ICT technology, sensors, computers and mathematical algorithms. In past, pHealth applications were focused to certain health or sickness related problem, but in today they use mobile devices, wireless networks, Web-technology and Cloud platforms. In future, pHealth uses information systems that are highly distributed, dynamic, increasingly autonomous, multi-stakeholder data driven eco-system having ability to monitor anywhere person's regular life, movements and health related behaviours. Because privacy and trust are pre-requirements for successful pHealth, this development raises huge privacy and trust challenges to be solved. Researchers have shown that current privacy approaches and solutions used in pHealth do not offer acceptable level of privacy, and trust is only an illusion. This indicates, that today's privacy models and technology shall not be moved to the future pHealth. The authors have analysed interesting new privacy and trust ideas published in journals, and found that they seem to be effective but offer only a partial solution. To solve this weakness, the authors used a holistic system view to aspects impacting privacy and trust in pHealth, and created a template that can be used in planning and development future pHealth services. The authors also propose a tentative solution for future trustworthy pHealth. It combines privacy as personal property and trust as legal binding fiducial duty approaches, and uses a Blockchain-based smart contract solution to store person's privacy and trust requirements and service providers' promises.


Assuntos
Registros de Saúde Pessoal , Privacidade , Humanos , Confiança , Computadores , Computadores de Mão
20.
Stud Health Technol Inform ; 299: 53-62, 2022 Nov 03.
Artigo em Inglês | MEDLINE | ID: mdl-36325846

RESUMO

The population aging has facilitated a growing number of welfare technologies and smart home solutions. These technologies enable clinical staff and health care professionals to provide health services in an intelligent way with the trend of patient-centric digital health platforms. As one of the health services, response center service is facing new challenges when connected with welfare technologies, such as false alarms, security threats, privacy leakage, etc. This paper introduces the mechanism of the response center and the role it plays in healthcare. We conduct an exploratory study to find out the benefits and challenges of the response center service from the results of a structured interview. Based on the findings, we identify the required services to improve the intelligent response center mechanism.


Assuntos
Atenção à Saúde , Serviços de Assistência Domiciliar , Humanos , Privacidade , Serviços de Saúde
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