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Clinicians should encourage disclosure between intimate partners but should maintain confidentiality in cases where there is no prospect of harm to the partner and/or offspring. In cases where a member of a couple refuses to disclose relevant health information to the other partner and there exists a risk of harm to the unaware partner and/or offspring, clinicians may refuse to offer care and should decline to treat if full informed consent is not possible because of the lack of disclosure. This document replaces the previously published document of the same name, last published in 2018. [ABSTRACT FROM AUTHOR]

Cybersecurity has seen an increasing frequency and impact of cyberattacks and exposure of Protected Health Information (PHI). The uptake of an Electronic Medical Record (EMR), the exponential adoption of Internet of Things (IoT) devices, and the impact of the COVID-19 pandemic has increased the threat surface presented for cyberattack by the healthcare sector. Within healthcare generally and, more specifically, within anaesthesia and Intensive Care, there has been an explosion in wired and wireless devices used daily in the care of almost every patient—the Internet of Medical Things (IoMT); ventilators, anaesthetic machines, infusion pumps, pacing devices, organ support and a plethora of monitoring modalities. All of these devices, once connected to a hospital network, present another opportunity for a malevolent party to access the hospital systems, either to gain PHI for financial, political or other gain or to attack the systems directly to cause erroneous monitoring, altered settings of any device and even to access the EMR via this IoMT window. This exponential increase in the IoMT and the increasing wireless connectivity of anaesthesia and ICU devices as well as implantable devices presents a real and present danger to patient safety. There has, at the same time, been a chronic underfunding of cybersecurity in healthcare. This lack of cybersecurity investment has left the sector exposed, and with the monetisation of PHI, the introduction of technically unsecure IoT devices for monitoring and direct patient care, the healthcare sector is presenting itself for further devastating cyberattacks or breaches of PHI. Coupled with the immense strain that the COVID-19 pandemic has placed on healthcare and the changes in working patterns of many caregivers, this has further amplified the exposure of the sector to cyberattacks. [ABSTRACT FROM AUTHOR]

The recent advancements in cyber technology have facilitated the global electronic sharing of healthcare data among various healthcare professionals, including clinicians, researchers, business administrators, and others. Fog computing is gaining popularity as a means of disseminating information across industries, especially in medicine and healthcare, due to its capability to manage vast amounts of information and data. The evolution of fog computing now allows it to be adaptable to various budget constraints within healthcare organizations, thanks to technological progress. This adaptation includes features like real-time data breach alerts and automated surveillance. As new communication and computing models emerge, fresh security and privacy concerns arise within medical and health-related data. Existing data privacy and security protocols have proven inadequate in preventing data leaks and theft attacks, especially those orchestrated by malicious parties. While medical data is stored in the cloud, legitimate entities’ authorized access to medical documents remains a significant concern. Therefore, this article designs an authentication and authorization scheme that safeguards the privacy of users’ medical information documents. In response to this challenge, we propose an innovative approach that leverages Decoy information technology and User Behavior profiling. This methodology aims to enhance the security of authorized data access within the Fog environment, ensuring the confidentiality of medical data. The results show that the proposed protocol is more secure against unauthorized access and security attacks and more computationally efficient than its counterparts.

The advent of cellular network technology has increased the use of photography in the clinical setting. This article reviews several areas regarding protected health information (PHI) and the use of video: the 1996 Health Insurance Portability and Accountability Act (HIPAA); The Joint Commission requirements for the use of images; areas of concern for exchanging PHI with law enforcement at the bedside, and the need for the development of formal guidelines regarding the use of video in the clinical setting. This article reviews several areas regarding protected health information (PHI), as well as the use of video, areas of concern for exchanging PHI with law enforcement at the bedside, and the need for the development of formal guidelines regarding the use of video in the clinical setting. [ABSTRACT FROM AUTHOR]

Patient Electronic Health Records (EHRs) contain valuable clinical data that is useful for medical research and public health inquires. However, patient privacy regulation and improper resource sharing risks limit access to EHR medical data for research and public health purposes. In this paper, we introduce an end-to-end security solution that addresses both concerns and facilitates the sharing of patient EHR data over an unsecured third-party server using a leveled homomorphic encryption (LHE) scheme. Time testing for aggregating queries and linear computations was carried out using an HPE ProLiant DL580 Gen 10 server with an Intel Xeon Platinum 8280 Processor. [ABSTRACT FROM AUTHOR]

Andrew NG, a leading philosopher in the field of Artificial Intelligence (AI) once quoted “AI is the new electricity” which has the potential to transform and drive every industry. The most important driving factor for the AI transformation will be data. Clive Humby, a data science entrepreneur was once quoted saying “data is the new oil” and data analytics being the “combustion engine” will drive the AI led innovations. The rapid rise of Artificial Intelligence technologies in the past decade, has inspired industries to invest in every opportunity for integrating AI solutions to their products. Research, development, and innovation in the field of AI are shaping various industries like automobile, manufacturing, finance, retail, supply chain management, and education among others. The healthcare industry has also been adopting the ways of AI into various workflows within the domain. With the evolution in computing and processing powers coupled with hardware modernizations, the adoption of AI looks more feasible than ever. Research and Innovations are happening in almost every field of healthcare and hospital workflows with the target of making healthcare processes more efficient & accessible, increase the overall state of healthcare, reduce physician stress levels, and increase the patient satisfaction levels. The conventional ways in which healthcare and clinical workflows have been operating are now starting to see the change with the integration of many data driven AI solutions. The digital innovations are making life easy for healthcare professionals allowing them to spend more time listening to the problems of patients and consequently increasing the patient satisfaction levels. However, there are limitations and concerns on security of Protected Health Information which have to be addressed for a seamless amalgamation of AI systems into the healthcare domain. Many papers have been published which mostly talk about one particular field/problem in the healthcare domain. No publications have covered the opportunities provided by AI technologies to the entire healthcare domain. This review paper discusses in detail about the progress AI has been able to make in the healthcare domain holistically and what the future of AI looks like. The paper also discusses about the implementation opportunities various AI technologies like Machine Learning, Deep Learning, Reinforcement Learning, Natural Language Processing, Computer Vision provide in different fields of healthcare and clinical workflows and how Artificial Intelligence systems will boost the capabilities of healthcare professionals in restoring the human touch in patient-physician encounters. A physician’s intuition and judgement will always remain better suited since each case, each health condition, and each person is unique in its own way, but AI methods can help enhance the accuracy of diagnosis, assist physicians in making improved and precise clinical decisions.

Over 90% of all biomedical AI research is stalled due to its inability to access real world protected health information to complete the required clinical validation and valuation prior to its deployment. With AI forecasted to improve patient outcomes by 40% and costs by 50% the new paradigm of zero trust must be adopted and leveraged to avoid the existential crisis facing biomedical AI research. Join us and learn how confidential computing and blockchain meet the requirements of zero trust.

The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The performance of such a machine learning model relies largely on the proper selection of features. The objective of this study was to investigate the utility of various features in a conditional-random-field (CRF)-based NER model. Natural language processing (NLP) toolkits were used to annotate the protected health information (PHI) from a total of 10,239 radiology reports that were divided into seven types. Multiple features were extracted by the toolkit and the NER models were built using these features and their combinations. A total of 10 features were extracted and the performance of the models was evaluated based on their precision, recall, and F1-score. The best-performing features were n-gram, prefix-suffix, word embedding, and word shape. These features outperformed others across all types of reports. The dataset we used was large in volume and divided into multiple types of reports. Such a diverse dataset made sure that the results were not subject to a small number of structured texts from where a machine learning model can easily learn the features. The manual de-identification of large-scale clinical reports is impractical. This study helps to identify the best-performing features for building an NER model for automatic de-identification from a wide array of features mentioned in the literature. [ABSTRACT FROM AUTHOR]

To address rapidly growing data breach incidents effectively, healthcare providers need to identify various insider and outsider threats, analyze the vulnerabilities of their internal security systems, and develop more appropriate data security measures against the threats. While there have been studies on trends of data breach incidents, there is a lack of research on the analysis of descriptive contents posted on the data breach reporting website of the U.S. Department of Health and Human Services (HHS) Office for Civil Rights (OCR). Hence, this study develops a novel approach to the analysis of descriptive data breach information with the use of text mining and visualization. Insider threats, vulnerabilities, breach incidents, impacts, and responses to the breaches are analyzed for three data breach types. [ABSTRACT FROM AUTHOR]

BackgroundElectronic health record (EHR) systems contain a large volume of texts, including visit notes, discharge summaries, and various reports. To protect the confidentiality of patients, these records often need to be fully de-identified before circulating for secondary use. Machine learning (ML) based named entity recognition (NER) model has emerged as a popular technique of automatic de-identification.ObjectiveThe performance of a machine learning model highly depends on the selection of appropriate features. The objective of this study was to investigate the usability of multiple features in building a conditional random field (CRF) based clinical de-identification NER model.MethodsUsing open-source natural language processing (NLP) toolkits, we annotated protected health information (PHI) in 1,500 pathology reports and built supervised NER models using multiple features and their combinations. We further investigated the dependency of a model's performance on the size of training data.ResultsAmong the 10 feature extractors explored in this study, n-gram, prefix–suffix, word embedding, and word shape performed the best. A model using combination of these four feature sets yielded precision, recall, and F1-score for each PHI as follows: NAME (0.80; 0.79; 0.80), LOCATION (0.85; 0.83; 0.84), DATE (0.86; 0.79; 0.82), HOSPITAL (0.96; 0.93; 0.95), ID (0.99; 0.82; 0.90), and INITIALS (0.97; 0.49; 0.65). We also found that the model's performance becomes saturated when the training data size is beyond 200.ConclusionManual de-identification of large-scale data is an impractical procedure since it is time-consuming and subject to human errors. Analysis of the NER model's performance in this study sheds light on a semi-automatic clinical de-identification pipeline for enterprise-wide data warehousing.

Abstract Background De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confidentiality. The clinical natural language processing (NLP) community has invested great efforts in developing methods and corpora for de-identification of clinical notes. These annotated corpora are valuable resources for developing automated systems to de-identify clinical text at local hospitals. However, existing studies often utilized training and test data collected from the same institution. There are few studies to explore automated de-identification under cross-institute settings. The goal of this study is to examine deep learning-based de-identification methods at a cross-institute setting, identify the bottlenecks, and provide potential solutions. Methods We created a de-identification corpus using a total 500 clinical notes from the University of Florida (UF) Health, developed deep learning-based de-identification models using 2014 i2b2/UTHealth corpus, and evaluated the performance using UF corpus. We compared five different word embeddings trained from the general English text, clinical text, and biomedical literature, explored lexical and linguistic features, and compared two strategies to customize the deep learning models using UF notes and resources. Results Pre-trained word embeddings using a general English corpus achieved better performance than embeddings from de-identified clinical text and biomedical literature. The performance of deep learning models trained using only i2b2 corpus significantly dropped (strict and relax F1 scores dropped from 0.9547 and 0.9646 to 0.8568 and 0.8958) when applied to another corpus annotated at UF Health. Linguistic features could further improve the performance of de-identification in cross-institute settings. After customizing the models using UF notes and resource, the best model achieved the strict and relaxed F1 scores of 0.9288 and 0.9584, respectively. Conclusions It is necessary to customize de-identification models using local clinical text and other resources when applied in cross-institute settings. Fine-tuning is a potential solution to re-use pre-trained parameters and reduce the training time to customize deep learning-based de-identification models trained using clinical corpus from a different institution.

Background: Health care organizations worldwide are faced with an increasing number of cyberattacks and threats to their critical infrastructure. These cyberattacks cause significant data breaches in digital health information systems, which threaten patient safety and privacy., Objective: From a sociotechnical perspective, this paper explores why digital health care systems are vulnerable to cyberattacks and provides sociotechnical solutions through a systematic literature review (SLR)., Methods: An SLR using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was conducted by searching 6 databases (PubMed, Web of Science, ScienceDirect, Scopus, Institute of Electrical and Electronics Engineers, and Springer) and a journal (Management Information Systems Quarterly) for articles published between 2012 and 2022 and indexed using the following keywords: "(cybersecurity OR cybercrime OR ransomware) AND (healthcare) OR (cybersecurity in healthcare)." Reports, review articles, and industry white papers that focused on cybersecurity and health care challenges and solutions were included. Only articles published in English were selected for the review., Results: In total, 5 themes were identified: human error, lack of investment, complex network-connected end-point devices, old legacy systems, and technology advancement (digitalization). We also found that knowledge applications for solving vulnerabilities in health care systems between 2012 to 2022 were inconsistent., Conclusions: This SLR provides a clear understanding of why health care systems are vulnerable to cyberattacks and proposes interventions from a new sociotechnical perspective. These solutions can serve as a guide for health care organizations in their efforts to prevent breaches and address vulnerabilities. To bridge the gap, we recommend that health care organizations, in partnership with educational institutions, develop and implement a cybersecurity curriculum for health care and intelligence information sharing through collaborations; training; awareness campaigns; and knowledge application areas such as secure design processes, phase-out of legacy systems, and improved investment. Additional studies are needed to create a sociotechnical framework that will support cybersecurity in health care systems and connect technology, people, and processes in an integrated manner., (©Pius Ewoh, Tero Vartiainen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.05.2024.)

The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The performance of such a machine learning model relies largely on the proper selection of features. The objective of this study was to investigate the utility of various features in a conditional-random-field (CRF)-based NER model. Natural language processing (NLP) toolkits were used to annotate the protected health information (PHI) from a total of 10,239 radiology reports that were divided into seven types. Multiple features were extracted by the toolkit and the NER models were built using these features and their combinations. A total of 10 features were extracted and the performance of the models was evaluated based on their precision, recall, and F1-score. The best-performing features were n-gram, prefix-suffix, word embedding, and word shape. These features outperformed others across all types of reports. The dataset we used was large in volume and divided into multiple types of reports. Such a diverse dataset made sure that the results were not subject to a small number of structured texts from where a machine learning model can easily learn the features. The manual de-identification of large-scale clinical reports is impractical. This study helps to identify the best-performing features for building an NER model for automatic de-identification from a wide array of features mentioned in the literature.

To address rapidly growing data breach incidents effectively, healthcare providers need to identify various insider and outsider threats, analyze the vulnerabilities of their internal security systems, and develop more appropriate data security measures against the threats. While there have been studies on trends of data breach incidents, there is a lack of research on the analysis of descriptive contents posted on the data breach reporting website of the U.S. Department of Health and Human Services (HHS) Office for Civil Rights (OCR). Hence, this study develops a novel approach to the analysis of descriptive data breach information with the use of text mining and visualization. Insider threats, vulnerabilities, breach incidents, impacts, and responses to the breaches are analyzed for three data breach types.

Their risk analysis considered data anonymity (improved by employing quasi-identifiers to distinguish individuals within a database) and data diversity (the number of distinct values for a sensitive attribute within a group). Artificial intelligence, data science, ethics, protected health information, reidentification Reidentification of Protected Health Information: Can the Risk Be Quantified?. [Extracted from the article]

For the secondary use of clinical documents, it is necessary to de-identify protected health information (PHI) in documents. However, the difficulty lies in the fact that there are few publicly annotated PHI documents. To solve this problem, in this study, we propose a filtered bidirectional encoder representation from transformers (BERT)-based method that predicts a masked word and validates the word again through a similarity filter to construct augmented sentences. The proposed method effectively performs data augmentation. The results show that the augmentation method based on filtered BERT improved the performance of the model. This suggests that our method can effectively improve the performance of the model in the limited data environment. [ABSTRACT FROM AUTHOR]

It is imperative in a medical domain that protection of information does not allow an individual to be overlooked. In medical domain, research community encourages use of real-time datasets for research purposes. These real-time datasets contain structured and unstructured (natural language free text) information that can be useful to researchers in various disciplines including computational linguistics. On the other hand, these real-time datasets cannot be distributed without anonymization of Protected Health Information (PHI). The information of PHI (such as Name, age, address, etc.) that can identify an individual is unethical. Therefore, we present a rule-based Natural Language Processing (NLP) anonymization system using a challenging corpus containing medical narratives and ICD-10 codes (medical codes). This anonymization module can be used for pre-processing the corpus containing identifiable information. The corpus used in this research contains '2534' PHIs in '1984' medical records in total. 15% of the labelled corpus was used for improvement of guidelines in the identification and classification of PHI groups and 85% was held for the evaluation. Our anonymization system follows two step process: (1) Identification and cataloging PHIs with four PHI categories ('Patients Name', 'Doctors Name', 'Other Name [Names other than patients and doctors]', 'Place Name'), (2) Anonymization of PHIs by replacing identified PHIs with their respective PHI categories. Our method uses basic language processing, dictionaries, rules and heuristics to identify, classify and anonymize PHIs with PHI categories. We use standard metrics for evaluation and our system outperforms against human annotated gold standard with 100% of F-measure by increasing 39% from baseline results, which proves the reliability of data usage for research. [ABSTRACT FROM AUTHOR]

Objectives: Patients with primary HIV-1 infection (PHI) are a particular population, giving important insight about ongoing evolution of transmitted drug resistance-associated mutation (TDRAM) prevalence, HIV diversity and clustering patterns. We describe these evolutions of PHI patients diagnosed in France from 2014 to 2016.Methods: A total of 1121 PHI patients were included. TDRAMs were characterized using the 2009 Stanford list and the French ANRS algorithm. Viral subtypes and recent transmission clusters (RTCs) were also determined.Results: Patients were mainly MSM (70%) living in the Paris area (42%). TDRAMs were identified among 10.8% of patients and rose to 18.6% when including etravirine and rilpivirine TDRAMs. Prevalences of PI-, NRTI-, first-generation NNRTI-, second-generation NNRTI- and integrase inhibitor-associated TDRAMs were 2.9%, 5.0%, 4.0%, 9.4% and 5.4%, respectively. In a multivariable analysis, age >40 years and non-R5 tropic viruses were associated with a >2-fold increased risk of TDRAMs. Regarding HIV diversity, subtype B and CRF02_AG (where CRF stands for circulating recombinant form) were the two main lineages (56% and 20%, respectively). CRF02_AG was associated with higher viral load than subtype B (5.83 versus 5.40 log10 copies/mL, P=0.004). We identified 138 RTCs ranging from 2 to 14 patients and including overall 41% from the global population. Patients in RTCs were younger, more frequently born in France and more frequently MSM.Conclusions: Since 2007, the proportion of TDRAMs has been stable among French PHI patients. Non-B lineages are increasing and may be associated with more virulent CRF02_AG strains. The presence of large RTCs highlights the need for real-time cluster identification to trigger specific prevention action to achieve better control of the epidemic. [ABSTRACT FROM AUTHOR]

This article addresses the rules and rights governing health information and why elders and their representatives and advocates should access and maintain copies of their personal health information. While barriers to information-sharing remain, public policy and technology are making it easier for individuals to exercise their information rights. This article gives older adults, their families, caregivers, and legal designees advice on how to proactively access information, and offers perspectives about evolving health information privacy challenges and how older adults can guard against risks of harmful use of personal health information. [ABSTRACT FROM AUTHOR]

Objective: This article aims to provide a primer on blockchain technology and implementation considerations for blockchain at healthcare institutions. Results: After research and interviews, we developed a primer and a high-level implementation guide for healthcare systems exploring the use of blockchain technology. Conclusions: The use of blockchain technology in health care is at a promising stage in development but blockchain-based applications are yet to be demonstrated as a viable platform for exchanging and reviewing information. Healthcare systems should be cautiously optimistic regarding the potential of blockchain and do a thorough business and technical diligence that is driven by targeted use cases to be successful.