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Yearbook of Medical Informatics Aug 2017To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select the best papers published in 2016. A bibliographic... (Review)
Review
To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select the best papers published in 2016. A bibliographic search using a combination of MeSH and free terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selection of best papers. Among the 452 papers published in 2016 in the various areas of CRI and returned by the query, the full review process selected four best papers. The authors of the first paper utilized a comprehensive representation of the patient medical record and semi-automatically labeled training sets to create phenotype models via a machine learning process. The second selected paper describes an open source tool chain securely connecting ResearchKit compatible applications (Apps) to the widely-used clinical research infrastructure Informatics for Integrating Biology and the Bedside (i2b2). The third selected paper describes the FAIR Guiding Principles for scientific data management and stewardship. The fourth selected paper focuses on the evaluation of the risk of privacy breaches in releasing genomics datasets. A major trend in the 2016 publications is the variety of research on "real-world data" - healthcare-generated data, person health data, and patient-reported outcomes -highlighting the opportunities provided by new machine learning techniques as well as new potential risks of privacy breaches.
Topics: Biomedical Research; Clinical Trials as Topic; Confidentiality; Humans; Medical Informatics; Observational Studies as Topic
PubMed: 29063566
DOI: 10.15265/IY-2017-024 -
Journal of Personalized Medicine Aug 2022While the rapid growth of mobile mental health applications has offered an avenue of support unbridled by physical distance, time, and cost, the digitalization of... (Review)
Review
While the rapid growth of mobile mental health applications has offered an avenue of support unbridled by physical distance, time, and cost, the digitalization of traditional interventions has also triggered doubts surrounding their effectiveness and safety. Given the need for a more comprehensive and up-to-date understanding of mobile mental health apps in traditional treatment, this umbrella review provides a holistic summary of their key potential and pitfalls. A total of 36 reviews published between 2014 and 2022-including systematic reviews, meta-analyses, scoping reviews, and literature reviews-were identified from the Cochrane library, Medline (via PubMed Central), and Scopus databases. The majority of results supported the key potential of apps in helping to (1) provide timely support, (2) ease the costs of mental healthcare, (3) combat stigma in help-seeking, and (4) enhance therapeutic outcomes. Our results also identified common themes of apps' pitfalls (i.e., challenges faced by app users), including (1) user engagement issues, (2) safety issues in emergencies, (3) privacy and confidentiality breaches, and (4) the utilization of non-evidence-based approaches. We synthesize the potential and pitfalls of mental health apps provided by the reviews and outline critical avenues for future research.
PubMed: 36143161
DOI: 10.3390/jpm12091376 -
Current Opinion in Psychology Dec 2020Mental healthcare providers increasingly use technology for psychotherapy services. This progress enables professionals to communicate, store information, and rely on... (Review)
Review
Mental healthcare providers increasingly use technology for psychotherapy services. This progress enables professionals to communicate, store information, and rely on digital software and hardware. Emails, text messaging, telepsychology/telemental health therapy, electronic medical records, cloud-based storage, apps/applications, and assessments are now available within the provision of services. Of those mentioned, some are directly utilized for psychotherapy while others indirectly aid providers. Whereas professionals previously wrote notes locally, technology has empowered providers to work more efficiently with third-party services and solutions. However, the implementation of these advancements in mental healthcare involves consequences to digital privacy and might increase clients' risk of unintended breaches of confidentiality. This manuscript reviews common technologies, considers the vulnerabilities therein, and proposes suggestions to strengthen privacy.
Topics: Confidentiality; Electronic Health Records; Humans; Mental Health Services; Privacy; Technology
PubMed: 32361651
DOI: 10.1016/j.copsyc.2020.03.012 -
Annals of the New York Academy of... Jan 2017Accessing and integrating human genomic data with phenotypes are important for biomedical research. Making genomic data accessible for research purposes, however, must... (Review)
Review
Accessing and integrating human genomic data with phenotypes are important for biomedical research. Making genomic data accessible for research purposes, however, must be handled carefully to avoid leakage of sensitive individual information to unauthorized parties and improper use of data. In this article, we focus on data sharing within the scope of data accessibility for research. Current common practices to gain biomedical data access are strictly rule based, without a clear and quantitative measurement of the risk of privacy breaches. In addition, several types of studies require privacy-preserving linkage of genotype and phenotype information across different locations (e.g., genotypes stored in a sequencing facility and phenotypes stored in an electronic health record) to accelerate discoveries. The computer science community has developed a spectrum of techniques for data privacy and confidentiality protection, many of which have yet to be tested on real-world problems. In this article, we discuss clinical, technical, and ethical aspects of genome data privacy and confidentiality in the United States, as well as potential solutions for privacy-preserving genotype-phenotype linkage in biomedical research.
Topics: Computational Biology; Computer Security; Data Mining; Genetic Privacy; Genomics; Humans; Informed Consent; Medical Record Linkage; Risk Management; United States
PubMed: 27681358
DOI: 10.1111/nyas.13259 -
International Journal of Environmental... Aug 2023Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the... (Review)
Review
Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health records (EHRs) to be trained locally without sending patients data to the central server. This allows healthcare decisions and diagnoses based on datasets from all participants, as well as streamlining other healthcare processes. In terms of user data privacy, this technology allows collaborative training without the need of sharing the local data with the central server. However, there are privacy challenges in FL arising from the fact that the model updates are shared between the client and the server which can be used for re-generating the client's data, breaching privacy requirements of applications in domains like healthcare. In this paper, we have conducted a review of the literature to analyse the existing privacy and security enhancement methods proposed for FL in healthcare systems. It has been identified that the research in the domain focuses on seven techniques: Differential Privacy, Homomorphic Encryption, Blockchain, Hierarchical Approaches, Peer to Peer Sharing, Intelligence on the Edge Device, and Mixed, Hybrid and Miscellaneous Approaches. The strengths, limitations, and trade-offs of each technique were discussed, and the possible future for these seven privacy enhancement techniques for healthcare FL systems was identified.
Topics: Humans; Privacy; Blockchain; Computer Communication Networks; Electronic Health Records; Delivery of Health Care
PubMed: 37569079
DOI: 10.3390/ijerph20156539 -
BMJ Health & Care Informatics Dec 2021Different stakeholders may hold varying attitudes towards artificial intelligence (AI) applications in healthcare, which may constrain their acceptance if AI developers... (Review)
Review
OBJECTIVES
Different stakeholders may hold varying attitudes towards artificial intelligence (AI) applications in healthcare, which may constrain their acceptance if AI developers fail to take them into account. We set out to ascertain evidence of the attitudes of clinicians, consumers, managers, researchers, regulators and industry towards AI applications in healthcare.
METHODS
We undertook an exploratory analysis of articles whose titles or abstracts contained the terms 'artificial intelligence' or 'AI' and 'medical' or 'healthcare' and 'attitudes', 'perceptions', 'opinions', 'views', 'expectations'. Using a snowballing strategy, we searched PubMed and Google Scholar for articles published 1 January 2010 through 31 May 2021. We selected articles relating to non-robotic clinician-facing AI applications used to support healthcare-related tasks or decision-making.
RESULTS
Across 27 studies, attitudes towards AI applications in healthcare, in general, were positive, more so for those with direct experience of AI, but provided certain safeguards were met. AI applications which automated data interpretation and synthesis were regarded more favourably by clinicians and consumers than those that directly influenced clinical decisions or potentially impacted clinician-patient relationships. Privacy breaches and personal liability for AI-related error worried clinicians, while loss of clinician oversight and inability to fully share in decision-making worried consumers. Both clinicians and consumers wanted AI-generated advice to be trustworthy, while industry groups emphasised AI benefits and wanted more data, funding and regulatory certainty.
DISCUSSION
Certain expectations of AI applications were common to many stakeholder groups from which a set of dependencies can be defined.
CONCLUSION
Stakeholders differ in some but not all of their attitudes towards AI. Those developing and implementing applications should consider policies and processes that bridge attitudinal disconnects between different stakeholders.
Topics: Artificial Intelligence; Attitude; Delivery of Health Care; Humans; Names
PubMed: 34887331
DOI: 10.1136/bmjhci-2021-100450 -
Expert Review of Clinical Immunology 2015Online social networks are used to connect with friends and family members, and increasingly, to stay up-to-date with the latest news and developments in allergy and... (Review)
Review
Online social networks are used to connect with friends and family members, and increasingly, to stay up-to-date with the latest news and developments in allergy and immunology. As communication is a central part of healthcare delivery, the utilization of such networking channels in allergy and immunology will continue to grow. There are inherent risks to online social networks related to breaches of patient confidentiality, professionalism and privacy. Malpractice and liability risks should also be considered. There is a paucity of information in the literature on how social network interventions affect patient outcomes. The allergy and immunology community should direct future studies towards investigating how the use of social networks and other technology tools and services can improve patient care.
Topics: Allergy and Immunology; Blogging; Confidentiality; Humans; Internet; Patient Education as Topic; Physician-Patient Relations; Practice Guidelines as Topic; Quality Improvement; Social Support
PubMed: 26163316
DOI: 10.1586/1744666X.2015.1065731 -
Blockchain in Healthcare Today 2023Integrating personal health records (PHRs) and electronic health records (EHRs) facilitates the provision of novel services to individuals, researchers, and healthcare...
UNLABELLED
Integrating personal health records (PHRs) and electronic health records (EHRs) facilitates the provision of novel services to individuals, researchers, and healthcare practitioners. Simultaneously, integrating healthcare data leads to complexities arising from the structural and semantic heterogeneity within the data. The subject of healthcare data evokes strong emotions due to concerns surrounding privacy breaches. Blockchain technology is employed to address the issue of patient data privacy in inter-organizational processes, as it facilitates patient data ownership and promotes transparency in its usage. At the same time, blockchain technology creates new challenges for e-healthcare systems, such as data privacy, observability, and online enforceability. This article proposes designing and formalizing automatic conflict resolution techniques in decentralized e-healthcare systems. The present study expounds upon our concepts by employing a running case study centered around preventive and personalized healthcare domains.
PLAIN LANGUAGE SUMMARY
This paper suggests using blockchain technology for privacy concerns in integrating personal health records and electronic health records in decentralized e-healthcare systems. This report focuses on designing automatic conflict resolution techniques to ensure patient data ownership, transparency, and privacy in inter-organizational processes. This paper proposes designing automatic conflict resolution techniques in decentralized e-healthcare systems, which can improve inter-organizational processes in healthcare. Using blockchain technology to integrate personal and electronic health records can ensure patient data ownership and promote transparency in data usage, addressing privacy concerns in healthcare systems. This paper emphasizes the importance of data privacy and protection in healthcare systems, highlighting the need for compliance with laws and regulations. The research results, including the proof-of-concept prototype, can provide practical insights into implementing conflict resolution techniques in decentralized e-healthcare systems.
PubMed: 38187955
DOI: 10.30953/bhty.v6.276 -
Science and Engineering Ethics Apr 2019This article proposes a new definition of information security, the 'Appropriate Access' definition. Apart from providing the basic criteria for a definition-correct...
This article proposes a new definition of information security, the 'Appropriate Access' definition. Apart from providing the basic criteria for a definition-correct demarcation and meaning concerning the state of security-it also aims at being a definition suitable for any information security perspective. As such, it bridges the conceptual divide between so-called 'soft issues' of information security (those including, e.g., humans, organizations, culture, ethics, policies, and law) and more technical issues. Because of this it is also suitable for various analytical purposes, such as analysing possible security breaches, or for studying conflicting attitudes on security in an organization. The need for a new definition is demonstrated by pointing to a number of problems for the standard definition type of information security-the so-called CIA definition. Besides being too broad as well as too narrow, it cannot properly handle the soft issues of information security, nor recognize the contextual and normative nature of security.
Topics: Access to Information; Computer Security; Confidentiality; Ethics; Humans; Information Storage and Retrieval; Policy; Privacy
PubMed: 29143269
DOI: 10.1007/s11948-017-9992-1 -
Sensors (Basel, Switzerland) Mar 2023The advent of Artificial Intelligence (AI) and the Internet of Things (IoT) have recently created previously unimaginable opportunities for boosting clinical and patient...
The advent of Artificial Intelligence (AI) and the Internet of Things (IoT) have recently created previously unimaginable opportunities for boosting clinical and patient services, reducing costs and improving community health. Yet, a fundamental challenge that the modern healthcare management system faces is storing and securely transferring data. Therefore, this research proposes a novel Lionized remora optimization-based serpent (LRO-S) encryption method to encrypt sensitive data and reduce privacy breaches and cyber-attacks from unauthorized users and hackers. The LRO-S method is the combination of hybrid metaheuristic optimization and improved security algorithm. The fitness functions of lion and remora are combined to create a new algorithm for security key generation, which is provided to the serpent encryption algorithm. The LRO-S technique encrypts sensitive patient data before storing it in the cloud. The primary goal of this study is to improve the safety and adaptability of medical professionals' access to cloud-based patient-sensitive data more securely. The experiment's findings suggest that the secret keys generated are sufficiently random and one of a kind to provide adequate protection for the data stored in modern healthcare management systems. The proposed method minimizes the time needed to encrypt and decrypt data and improves privacy standards. This study found that the suggested technique outperformed previous techniques in terms of reducing execution time and is cost-effective.
Topics: Humans; Artificial Intelligence; Computer Security; Algorithms; Privacy; Delivery of Health Care
PubMed: 37050672
DOI: 10.3390/s23073612