HIPAA and Protecting Health Information in the 21st Century

Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging

Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However, its widespread application has been limited by the heavy resource burden of CMR interpretation. Here, to address this challenge, we developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients. We propose a two-stage paradigm consisting of noninvasive cine-based CVD screening followed by cine and late gadolinium enhancement-based diagnosis. The screening and diagnostic models achieved high performance (area under the curve of 0.988 ± 0.3% and 0.991 ± 0.0%, respectively) in both internal and external datasets. Furthermore, the diagnostic model outperformed cardiologists in diagnosing pulmonary arterial hypertension, demonstrating the ability of artificial intelligence-enabled CMR to detect previously unidentified CMR features. This proof-of-concept study holds the potential to substantially advance the efficiency and scalability of CMR interpretation, thereby improving CVD screening and diagnosis.

Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature

Artificial intelligence (AI) has the ability to completely transform the healthcare industry by enhancing diagnosis, treatment, and resource allocation. To ensure patient safety and equitable access to healthcare, it also presents ethical and practical issues that need to be carefully addressed. Its integration into healthcare is a crucial topic. To realize its full potential, however, the ethical issues around data privacy, prejudice, and transparency, as well as the practical difficulties posed by workforce adaptability and statutory frameworks, must be addressed. While there is growing knowledge about the advantages of AI in healthcare, there is a significant lack of knowledge about the moral and practical issues that come with its application, particularly in the setting of emergency and critical care. The majority of current research tends to concentrate on the benefits of AI, but thorough studies that investigate the potential disadvantages and ethical issues are scarce. The purpose of our article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers. In order to responsibly and successfully integrate AI in these important healthcare domains, policymakers and healthcare professionals must collaborate to create strong regulatory frameworks, safeguard data privacy, remove prejudice, and give healthcare workers the necessary training.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Public Health Informatics and the Perioperative Physician: Looking to the Future

The role of informatics in public health has increased over the past few decades, and the coronavirus disease 2019 (COVID-19) pandemic has underscored the critical importance of aggregated, multicenter, high-quality, near-real-time data to inform decision-making by physicians, hospital systems, and governments. Given the impact of the pandemic on perioperative and critical care services (eg, elective procedure delays; information sharing related to interventions in critically ill patients; regional bed-management under crisis conditions), anesthesiologists must recognize and advocate for improved informatic frameworks in their local environments. Most anesthesiologists receive little formal training in public health informatics (PHI) during clinical residency or through continuing medical education. The COVID-19 pandemic demonstrated that this knowledge gap represents a missed opportunity for our specialty to participate in informatics-related, public health-oriented clinical care and policy decision-making. This article briefly outlines the background of PHI, its relevance to perioperative care, and conceives intersections with PHI that could evolve over the next quarter century.

Artificial intelligence and urology: ethical considerations for urologists and patients

The use of artificial intelligence (AI) in medicine and in urology specifically has increased over the past few years, during which time it has enabled optimization of patient workflow, increased diagnostic accuracy and enhanced computer analysis of radiological and pathological images. However, before further use of AI is undertaken, possible ethical issues need to be evaluated to improve understanding of this technology and to protect patients and providers. Possible ethical issues that require consideration when applying AI in clinical practice include patient safety, cybersecurity, transparency and interpretability of the data, inclusivity and equity, fostering responsibility and accountability, and the preservation of providers' decision-making and autonomy. Ethical principles for the application of AI to health care and in urology are proposed to guide urologists, patients and regulators to improve use of AI technologies and guide policy-making.

Identifying facilitators of and barriers to the adoption of dynamic consent in digital health ecosystems: a scoping review

BACKGROUND

Conventional consent practices face ethical challenges in continuously evolving digital health environments due to their static, one-time nature. Dynamic consent offers a promising solution, providing adaptability and flexibility to address these ethical concerns. However, due to the immaturity of the concept and accompanying technology, dynamic consent has not yet been widely used in practice. This study aims to identify the facilitators of and barriers to adopting dynamic consent in real-world scenarios.

METHODS

This scoping review, conducted in December 2022, adhered to the PRISMA Extension for Scoping Reviews guidelines, focusing on dynamic consent within the health domain. A comprehensive search across Web of Science, PubMed, and Scopus yielded 22 selected articles based on predefined inclusion and exclusion criteria.

RESULTS

The facilitators for the adoption of dynamic consent in digital health ecosystems were the provision of multiple consent modalities, personalized alternatives, continuous communication, and the dissemination of up-to-date information. Nevertheless, several barriers, such as consent fatigue, the digital divide, complexities in system implementation, and privacy and security concerns, needed to be addressed. This study also investigated current technological advancements and suggested considerations for further research aimed at resolving the remaining challenges surrounding dynamic consent.

CONCLUSIONS

Dynamic consent emerges as an ethically advantageous method for digital health ecosystems, driven by its adaptability and support for continuous, two-way communication between data subjects and consumers. Ethical implementation in real-world settings requires the development of a robust technical framework capable of accommodating the diverse needs of stakeholders, thereby ensuring ethical integrity and data privacy in the evolving digital health landscape.

Navigating Informed Consent Requirements and Expectations in Cluster Randomized Trials: Research Ethics Board Members' and Researchers' Views

Informed consent is a cornerstone of ethical human research. However, as cluster randomized trials (CRTs) are increasingly popular to evaluate health service interventions, especially as health systems aspire toward the learning health system, questions abound how research teams and research ethics boards (REBs) should navigate intertwining consent and data-use considerations. Methodological and ethical questions include who constitute the participants, whose and what types of consent are necessary, and how data from people who have not consented to participation should be managed to optimize the balance of trust in the research enterprise, respect for persons, the promotion of data integrity, and the pursuit of the public good in the research arena. In this paper, we report the findings and lessons learned from a qualitative study examining how researchers and REB members consider the ethical dimensions of when data can be collected and used in CRTs in the evolving research landscape.

Creating High Fidelity Synthetic Pelvis Radiographs Using Generative Adversarial Networks: Unlocking the Potential of Deep Learning Models Without Patient Privacy Concerns

BACKGROUND

In this work, we applied and validated an artificial intelligence technique known as generative adversarial networks (GANs) to create large volumes of high-fidelity synthetic anteroposterior (AP) pelvis radiographs that can enable deep learning (DL)-based image analyses, while ensuring patient privacy.

METHODS

AP pelvis radiographs with native hips were gathered from an institutional registry between 1998 and 2018. The data was used to train a model to create 512 × 512 pixel synthetic AP pelvis images. The network was trained on 25 million images produced through augmentation. A set of 100 random images (50/50 real/synthetic) was evaluated by 3 orthopaedic surgeons and 2 radiologists to discern real versus synthetic images. Two models (joint localization and segmentation) were trained using synthetic images and tested on real images.

RESULTS

The final model was trained on 37,640 real radiographs (16,782 patients). In a computer assessment of image fidelity, the final model achieved an "excellent" rating. In a blinded review of paired images (1 real, 1 synthetic), orthopaedic surgeon reviewers were unable to correctly identify which image was synthetic (accuracy = 55%, Kappa = 0.11), highlighting synthetic image fidelity. The synthetic and real images showed equivalent performance when they were assessed by established DL models.

CONCLUSION

This work shows the ability to use a DL technique to generate a large volume of high-fidelity synthetic pelvis images not discernible from real imaging by computers or experts. These images can be used for cross-institutional sharing and model pretraining, further advancing the performance of DL models without risk to patient data safety.

LEVEL OF EVIDENCE

Level III.

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

INTRODUCTION

Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools.

RESEARCH SIGNIFICANCE

This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies.

MATERIALS AND METHODS

The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application.

RESULTS

Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust.

CONCLUSION

AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record

The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.

What quantifies good primary care in the United States? A review of algorithms and metrics using real-world data

Primary care physicians (PCPs) play an indispensable role in providing comprehensive care and referring patients for specialty care and other medical services. As the COVID-19 outbreak disrupts patient access to care, understanding the quality of primary care is critical at this unprecedented moment to support patients with complex medical needs in the primary care setting and inform policymakers to redesign our primary care system. The traditional way of collecting information from patient surveys is time-consuming and costly, and novel data collection and analysis methods are needed. In this review paper, we describe the existing algorithms and metrics that use the real-world data to qualify and quantify primary care, including the identification of an individual's likely PCP (identification of plurality provider and major provider), assessment of process quality (for example, appropriate-care-model composite measures), and continuity and regularity of care index (including the interval index, variance index and relative variance index), and highlight the strength and limitation of real world data from electronic health records (EHRs) and claims data in determining the quality of PCP care. The EHR audits facilitate assessing the quality of the workflow process and clinical appropriateness of primary care practices. With extensive and diverse records, administrative claims data can provide reliable information as it assesses primary care quality through coded information from different providers or networks. The use of EHRs and administrative claims data may be a cost-effective analytic strategy for evaluating the quality of primary care.

ResNetFed: Federated Deep Learning Architecture for Privacy-Preserving Pneumonia Detection from COVID-19 Chest Radiographs

Personal health data is subject to privacy regulations, making it challenging to apply centralized data-driven methods in healthcare, where personalized training data is frequently used. Federated Learning (FL) promises to provide a decentralized solution to this problem. In FL, siloed data is used for the model training to ensure data privacy. In this paper, we investigate the viability of the federated approach using the detection of COVID-19 pneumonia as a use case. 1411 individual chest radiographs, sourced from the public data repository COVIDx8 are used. The dataset contains radiographs of 753 normal lung findings and 658 COVID-19 related pneumonias. We partition the data unevenly across five separate data silos in order to reflect a typical FL scenario. For the binary image classification analysis of these radiographs, we propose ResNetFed, a pre-trained ResNet50 model modified for federation so that it supports Differential Privacy. In addition, we provide a customized FL strategy for the model training with COVID-19 radiographs. The experimental results show that ResNetFed clearly outperforms locally trained ResNet50 models. Due to the uneven distribution of the data in the silos, we observe that the locally trained ResNet50 models perform significantly worse than ResNetFed models (mean accuracies of 63% and 82.82%, respectively). In particular, ResNetFed shows excellent model performance in underpopulated data silos, achieving up to +34.9 percentage points higher accuracy compared to local ResNet50 models. Thus, with ResNetFed, we provide a federated solution that can assist the initial COVID-19 screening in medical centers in a privacy-preserving manner.

An Assessment of Perspectives and Concerns Among Research Participants of Childbearing Age Regarding the Health-Relatedness of Data, Online Data Privacy, and Donating Data to Researchers: Survey Study

BACKGROUND

The June 2022 US Supreme Court decision to ban abortion care in Dobbs v Jackson Women's Health Organization sparked ominous debate about the privacy and safety of women and families of childbearing age with digital footprints who actively engage in family planning, including abortion and miscarriage care.

OBJECTIVE

To assess the perspectives of a subpopulation of research participants of childbearing age regarding the health-relatedness of their digital data, their concerns about the use and sharing of personal data online, and their concerns about donating data from various sources to researchers today or in the future.

METHODS

An 18-item electronic survey was developed using Qualtrics and administered to adults (aged ≥18 years) registered in the ResearchMatch database in April 2021. Individuals were invited to participate in the survey regardless of health status, race, gender, or any other mutable or immutable characteristics. Descriptive statistical analyses were conducted using Microsoft Excel and manual queries (single layer, bottom-up topic modeling) and used to categorize illuminating quotes from free-text survey responses.

RESULTS

A total of 470 participants initiated the survey and 402 completed and submitted the survey (for an 86% completion rate). Nearly half the participants (189/402, 47%) self-reported to be persons of childbearing age (18 to 50 years). Most participants of childbearing age agreed or strongly agreed that social media data, email data, text message data, Google search history data, online purchase history data, electronic medical record data, fitness tracker and wearable data, credit card statement data, and genetic data are health-related. Most participants disagreed or strongly disagreed that music streaming data, Yelp review and rating data, ride-sharing history data, tax records and other income history data, voting history data, and geolocation data are health-related. Most (164/189, 87%) participants were concerned about fraud or abuse based on their personal information, online companies and websites sharing information with other parties without consent, and online companies and websites using information for purposes that are not explicitly stated in their privacy policies. Free-text survey responses showed that participants were concerned about data use beyond scope of consent; exclusion from health care and insurance; government and corporate mistrust; and data confidentiality, security, and discretion.

CONCLUSIONS

Our findings in light of Dobbs and other related events indicate there are opportunities to educate research participants about the health-relatedness of their digital data. Developing strategies and best privacy practices for discretion regarding digital-footprint data related to family planning should be a priority for companies, researchers, families, and other stakeholders.

Adaptive interventions for opioid prescription management and consumption monitoring

OBJECTIVES

While opioid addiction, treatment, and recovery are receiving attention, not much has been done on adaptive interventions to prevent opioid use disorder (OUD). To address this, we identify opioid prescription and opioid consumption as promising targets for adaptive interventions and present a design framework.

MATERIALS AND METHODS

Using the framework, we designed Smart Prescription Management (SPM) and Smart Consumption Monitoring (SCM) interventions. The interventions are evaluated using analytical modeling and secondary data on doctor shopping, opioid overdose, prescription quality, and cost components.

RESULTS

SPM was most effective (30-90% improvement, for example, prescriptions reduced from 18 to 1.8 per patient) for extensive doctor shopping and reduced overdose events and mortality. Opioid adherence was improved and the likelihood of addiction declined (10-30%) as the response rate to SCM was increased. There is the potential for significant incentives ($2267-$3237) to be offered for addressing severe OUD.

DISCUSSION

The framework and designed interventions adapt to changing needs and conditions of the patients to become an important part of global efforts in preventing OUD. To the best of our knowledge, this is the first paper on adaptive interventions for preventing OUD by addressing both prescription and consumption.

CONCLUSION

SPM and SCM improved opioid prescription and consumption while reducing the risk of opioid addiction. These interventions will assist in better prescription decisions and in managing opioid consumption leading to desirable outcomes. The interventions can be extended to other substance use disorders and to study complex scenarios of prescription and nonprescription opioids in clinical studies.

Successful pathways to liver transplant for undocumented immigrants

Liver transplant (LT) for undocumented immigrants presents numerous challenges. Although the United Network for Organ Sharing has implemented multiple policy changes to lessen the disparities in LT throughout the years, undocumented immigrants remain especially marginalized and disadvantaged when compared with other populations. Since 2013, the Mount Sinai Hospital's Recanati Miller Transplant Institute has transplanted 16 undocumented immigrants with successful outcomes. Here, we will share our experience of evaluating, caring for, and transplanting these patients and also highlight our team's mission to ensure that this population has equitable access to lifesaving medical treatment.

Methodological Challenges in Spatial and Contextual Exposome-Health Studies

The concept of the exposome encompasses the totality of exposures from a variety of external and internal sources across an individual's life course. The wealth of existing spatial and contextual data makes it appealing to characterize individuals' external exposome to advance our understanding of environmental determinants of health. However, the spatial and contextual exposome is very different from other exposome factors measured at the individual-level as spatial and contextual exposome data are more heterogenous with unique correlation structures and various spatiotemporal scales. These distinctive characteristics lead to multiple unique methodological challenges across different stages of a study. This article provides a review of the existing resources, methods, and tools in the new and developing field for spatial and contextual exposome-health studies focusing on four areas: (1) data engineering, (2) spatiotemporal data linkage, (3) statistical methods for exposome-health association studies, and (4) machine- and deep-learning methods to use spatial and contextual exposome data for disease prediction. A critical analysis of the methodological challenges involved in each of these areas is performed to identify knowledge gaps and address future research needs.

An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors

The continuously increasing number of mobile devices actively being used in the world amounted to approximately 6.8 billion by 2022. Consequently, this implies a substantial increase in the amount of personal data collected, transported, processed, and stored. The authors of this paper designed and implemented an integrated personal health data management system, which considers data-driven software and hardware sensors, comprehensive data privacy techniques, and machine-learning-based algorithmic models. It was determined that there are very few relevant and complete surveys concerning this specific problem. Therefore, the current scientific research was considered, and this paper comprehensively analyzes the importance of deep learning techniques that are applied to the overall management of data collected by data-driven soft sensors. This survey considers aspects that are related to demographics, health and body parameters, and human activity and behaviour pattern detection. Additionally, the relatively complex problem of designing and implementing data privacy mechanisms, while ensuring efficient data access, is also discussed, and the relevant metrics are presented. The paper concludes by presenting the most important open research questions and challenges. The paper provides a comprehensive and thorough scientific literature survey, which is useful for any researcher or practitioner in the scope of data-driven soft sensors and privacy techniques, in relation to the relevant machine-learning-based models.

Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine

Since 1978, with the first IVF (in vitro fertilization) baby birth in Manchester (England), more than eight million IVF babies have been born throughout the world, and many new techniques and discoveries have emerged in reproductive medicine. To summarize the modern technology and progress in reproductive medicine, all scientific papers related to reproductive medicine, especially papers related to reproductive translational medicine, were fully searched, manually curated and reviewed. Results indicated whether male reproductive medicine or female reproductive medicine all have made significant progress, and their markers have experienced the progress from karyotype analysis to single-cell omics. However, due to the lack of comprehensive databases, especially databases collecting risk exposures, disease markers and models, prevention drugs and effective treatment methods, the application of the latest precision medicine technologies and methods in reproductive medicine is limited.

A Survey of Research Participants’ Privacy-Related Experiences and Willingness to Share Real-World Data with Researchers

Background: Real-world data (RWD) privacy is an increasingly complex topic within the scope of personalized medicine, as it implicates several sources of data. Objective: To assess how privacy-related experiences, when adjusted for age and education level, may shape adult research participants’ willingness to share various sources of real-world data with researchers. Methods: An electronic survey was conducted in April 2021 among adults (≥18 years of age) registered in ResearchMatch, a national health research registry. Descriptive analyses were conducted to assess survey participant demographics. Logistic regression was conducted to assess the association between participants’ five distinct privacy-related experiences and their willingness to share each of the 19 data sources with researchers, adjusting for education level and age range. Results: A total of 598 ResearchMatch adults were contacted and 402 completed the survey. Most respondents were over the age of 51 years (49% total) and held a master’s or bachelor’s degree (63% total). Over half of participants (54%) had their account accessed by someone without their permission. Almost half of participants (49%) reported the privacy of their personal information being violated. Analyses showed that, when adjusted for age range and education level, participants whose reputations were negatively affected as a result of information posted online were more likely to share electronic medical record data (OR = 2.074, 95% CI: 0.986–4.364) and genetic data (OR = 2.302, 95% CI: 0.894–5.93) versus those without this experience. Among participants who had an unpleasant experience as a result of giving out information online, those with some college/associates/trade school compared to those with a doctoral or other terminal degree were significantly more willing to share genetic data (OR = 1.064, 95% CI: 0.396–2.857). Across all privacy-related experiences, participants aged 18 to 30 were significantly more likely than those over 60 years to share music streaming data, ridesharing history data, and voting history data. Additionally, across all privacy-related experiences, those with a high school education were significantly more likely than those with a doctorate or other terminal degree to share credit card statement data. Conclusions: This study offers the first insights into how privacy-related experiences, adjusted for age range and education level, may shape ResearchMatch participants’ willingness to share several sources of real-world data sources with precision medicine researchers. Future work should further explore these insights.

Bringing student health and Well-Being onto a health system EHR: the benefits of integration in the COVID-19 era

ObjectiveTo detail the implementation, benefits and challenges of onboarding campus-based health services onto a health system's electronic health record.ParticipantsUC San Diego Student Health and Well-Being offers medical services to over 39,000 students. UC San Diego Health is an academic medical center.Methods20 workstreams and 9 electronic modules, systems, or interfaces were converted to new electronic systems.Results36,023 student-patient medical records were created. EHR-integration increased security while creating visibility to 19,700 shared patient visits and records from 236 health systems across the country over 6 months. Benefits for the COVID-19 response included access to screening tools, decision support, telehealth, patient alerting system, reporting and analytics, COVID-19 dashboard, and increased testing capabilities.ConclusionIntegration of an interoperable EHR between neighboring campus-based health services and an affiliated academic medical center can streamline case management, improve quality and safety, and increase access to valuable health resources in times of need. Pertinent examples during the COVID-19 pandemic included uninterrupted and safe provision of clinical services through access to existing telehealth platforms and increased testing capacity.

Should Alexa diagnose Alzheimer's?: Legal and ethical issues with at-home consumer devices

Voice-based AI-powered digital assistants, such as Alexa, Siri, and Google Assistant, present an exciting opportunity to translate healthcare from the hospital to the home. But building a digital, medical panopticon can raise many legal and ethical challenges if not designed and implemented thoughtfully. This paper highlights the benefits and explores some of the challenges of using digital assistants to detect early signs of cognitive impairment, focusing on issues such as consent, bycatching, privacy, and regulatory oversight. By using a fictional but plausible near-future hypothetical, we demonstrate why an "ethics-by-design" approach is necessary for consumer-monitoring tools that may be used to identify health concerns for their users.

Understanding the Security and Privacy Concerns About the Use of Identifiable Health Data in the Context of the COVID-19 Pandemic: Survey Study of Public Attitudes Toward COVID-19 and Data-Sharing

Background

The COVID-19 pandemic increased the availability and use of population and individual health data to optimize tracking and analysis of the spread of the virus. Many health care services have had to rapidly digitalize in order to maintain the continuity of care provision. Data collection and dissemination have provided critical support for defending against the spread of the virus since the beginning of the pandemic; however, little is known about public perceptions of and attitudes toward the use, privacy, and security of data.

Objective

The goal of this study is to better understand people’s willingness to share data in the context of the COVID-19 pandemic.

Methods

A web-based survey was conducted on individuals’ use of and attitudes toward health data for individuals aged 18 years and older, and in particular, with a reported diagnosis of a chronic health condition placing them at the highest risk of severe COVID-19.

Results

In total, 4764 individuals responded to this web-based survey, of whom 4674 (98.1%) reported a medical diagnosis of at least 1 health condition (3 per person on average), with type 2 diabetes (n=2974, 62.7%), hypertension (n=2147, 45.2%), and type 1 diabetes (n=1299, 27.4%) being most prominent in our sample. In general, more people are comfortable with sharing anonymized data than personally identifiable data. People reported feeling comfortable sharing data that were able to benefit others; 66% (3121 respondents) would share personal identifiable data if its primary purpose was deemed beneficial for the health of others. Almost two-thirds (n=3026; 63.9%) would consent to sharing personal, sensitive health data with government or health authority organizations. Conversely, over a quarter of respondents (n=1297, 27.8%) stated that they did not trust any organization to protect their data, and 54% (n=2528) of them reported concerns about the implications of sharing personal information. Almost two-thirds (n=3054, 65%) of respondents were concerned about the provisions of appropriate legislation that seeks to prevent data misuse and hold organizations accountable in the case of data misuse.

Conclusions

Although our survey focused mainly on the views of those living with chronic health conditions, the results indicate that data sensitivity is highly contextual. More people are more comfortable with sharing anonymized data rather than personally identifiable data. Willingness to share data also depended on the receiving body, highlighting trust as a key theme, in particular who may have access to shared personal health data and how they may be used in the future. The nascency of legal guidance in this area suggests a need for humanitarian guidelines for data responsibility during disaster relief operations such as pandemics and for involving the public in their development.

Personal Health Metrics Data Management Using Symmetric 5G Data Channels

The integrated collection of personal health data represents a relevant research topic, which is enhanced further by the development of next-generation mobile networks that can be used in order to transport the acquired medical data. The gathering of personal health data has become recently feasible using relevant wearable personal devices. Nevertheless, these devices do not possess sufficient computational power, and do not offer proper local data storage capabilities. This paper presents an integrated personal health metrics data management system, which considers a virtualized symmetric 5G data transportation system. The personal health data are acquired using a client application component, which is normally deployed on the user’s mobile device, regardless it is a smartphone, smartwatch, or another kind of personal mobile device. The collected data are securely transported to the cloud data processing components, using a virtualized 5G infrastructure and homomorphically encrypted data packages. The system has been comprehensively assessed through the consideration of a real-world use case, which is presented.

HIPAA and Access to Medical Information by Medical Examiner and Coroner Offices

Often, medical staff and sometimes their attorneys mistakenly believe that HIPAA prevents disclosure of medical records to medical examiner and coroner offices. Medical examiner and coroner government offices are not covered entities. Moreover, HIPAA specifically allows disclosure to law enforcement, public health, and medical examiner and coroners. However, state and Joint Commission requirements may further impact disclosures.

A comprehensive mobile health intervention to prevent and manage the complexities of opioid use

OBJECTIVES

The Opioid Use crisis continues to be an epidemic with multiple known influencing and interacting factors. With the need for suitable opioid use interventions, we present a conceptual design of an m-health intervention that addresses the various known interacting factors of opioid use and corresponding evidence-based practices. The visualization of the opioid use complexities is presented as the "Opioid Cube".

METHODS

Following Stage 0 to Stage IA of the NIH Stage Model, we used guidelines and extant health intervention literature on opioid apps to inform the Opioid Intervention (O-INT) design. We present our design using system architecture, algorithms, and user interfaces to integrate multiple functions including decision support. We evaluate the proposed O-INT using analytical modeling.

RESULTS

The conceptual design of O-INT supports the concept of collaborative care, by providing connections between the patient, healthcare professionals, and their family members. The evaluation of O-INT shows a preference for specific functions, such as overdose detection and potential for high system reliability with minimal side effects. The Opioid Cube provides a visualization of various opioid use states and their influencing and interacting factors.

CONCLUSIONS

O-INT is a promising design with a holistic approach to manage opioid use and prevent and treat misuse. With several needed functionalities, O-INT design serves as a decision support system for patients, healthcare professionals, researchers, and policy makers. Together, O-INT and the Opioid Cube may serve as a foundation for development and adoption of highly effective m-health interventions for opioid use.

Senior High School Students' Knowledge and Attitudes Toward Information on Their Health in the Kumasi Metropolis

The study examines senior high school students' understanding and attitudes toward information on their health in the Kumasi Metropolis. Multiple sampling techniques (convenient and simple random sampling techniques) were used in the study. A questionnaire was used to collect data from 391 respondents for the study. Frequencies and percentages were used to analyze the sociodemographic data. Again, the study used Pearson's correlation coefficient to show the degree of relationship between the level of knowledge of health information and attitudes toward seeking and sharing health information. The study found students' knowledge of the causes and symptoms of malaria, cholera, and Sexually Transmitted Infections (STIs) to be appreciably high as a result of readings from textbooks and health professionals. Again, the study found that the students preferred sharing their health information with friends than their parents and schools' authorities. The study further found that the major sources of students' health information included health professionals and textbooks. Lastly, even though some of the students claimed internet sources to their health information, it was not a major source to the student body at large. The study recommends strong health systems on the campuses of senior high schools as they have become communities on their own as a result of the emergence of the free senior high school program. The monitored positive peer-counseling group should also be encouraged by the schools' management and by extension the counseling units for the students to share views on themselves, particularly on health issues where they deem fit.

Population health science as a unifying foundation for translational clinical and public health research

Separated both in academics and practice since the Rockefeller Foundation effort to “liberate” public health from perceived subservience to clinical medicine a century ago, research in public health and clinical medicine have evolved separately. Today, translational research in population health science offers a means of fostering their convergence, with potentially great benefit to both domains. Although evidence that the two fields need not and should not be entirely distinct in their methods and goals has been accumulating for over a decade, the prodigious efforts of biomedical and social sciences over the past year to address the COVID-19 pandemic has placed this unifying approach to translational research in both fields in a new light. Specifically, the coalescence of clinical and population-level strategies to control disease and novel uses of population-level data and tools in research relating to the pandemic have illuminated a promising future for translational research.

We exploit this unique window to re-examine how translational research is conducted and where it may be going. We first discuss the transformation that has transpired in the research firmament over the past two decades and the opportunities these changes afford. Next, we present some of the challenges—technical, cultural, legal, and ethical— that need attention if these opportunities are to be successfully exploited. Finally, we present some recommendations for addressing these challenges.

•

Population datasets with extensive biologic, medical and social information augur a new approach for translational research.

•

Important questions about health throughout the life course could be answered were relevant person-level data accessible.

•

Technical, cultural, and legal constraints presently limit the ability to actualize this vision.

•

Analysis of these limitations in the context of the recent pandemic reveals each could be overcome with proper attention.

Addressing the clinical unmet needs in primary Sjögren’s Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts

•

Data sharing can address open issues and clinical unmet needs in rare diseases.

•

Data curation enhanced the cohort data quality in primary Sjögrens Syndrome (pSS).

•

Semantic analysis yielded 7,156 harmonized patients across 21 cohorts in pSS.

•

Federated tree ensembles yield explainable AI models for lymphoma development.

•

Salivary gland swelling & cryoglobulinemia increase the risk for lymphomagenesis.

For many decades, the clinical unmet needs of primary Sjögren’s Syndrome (pSS) have been left unresolved due to the rareness of the disease and the complexity of the underlying pathogenic mechanisms, including the pSS-associated lymphomagenesis process. Here, we present the HarmonicSS cloud-computing exemplar which offers beyond the state-of-the-art data analytics services to address the pSS clinical unmet needs, including the development of lymphoma classification models and the identification of biomarkers for lymphomagenesis. The users of the platform have been able to successfully interlink, curate, and harmonize 21 regional, national, and international European cohorts of 7,551 pSS patients with respect to the ethical and legal issues for data sharing. Federated AI algorithms were trained across the harmonized databases, with reduced execution time complexity, yielding robust lymphoma classification models with 85% accuracy, 81.25% sensitivity, 85.4% specificity along with 5 biomarkers for lymphoma development. To our knowledge, this is the first GDPR compliant platform that provides federated AI services to address the pSS clinical unmet needs.

Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice

An increasing number of artificial intelligence (AI) systems are being proposed in ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as their potential benefits at the different stages of patient care. Despite achieving close or even superior performance to that of experts, there is a critical gap between development and integration of AI systems in ophthalmic practice. This work focuses on the importance of trustworthy AI to close that gap. We identify the main aspects or challenges that need to be considered along the AI design pipeline so as to generate systems that meet the requirements to be deemed trustworthy, including those concerning accuracy, resiliency, reliability, safety, and accountability. We elaborate on mechanisms and considerations to address those aspects or challenges, and define the roles and responsibilities of the different stakeholders involved in AI for ophthalmic care, i.e., AI developers, reading centers, healthcare providers, healthcare institutions, ophthalmological societies and working groups or committees, patients, regulatory bodies, and payers. Generating trustworthy AI is not a responsibility of a sole stakeholder. There is an impending necessity for a collaborative approach where the different stakeholders are represented along the AI design pipeline, from the definition of the intended use to post-market surveillance after regulatory approval. This work contributes to establish such multi-stakeholder interaction and the main action points to be taken so that the potential benefits of AI reach real-world ophthalmic settings.

Ethical issues in biomedical research using electronic health records: a systematic review

Digitization of a health record changes its accessibility. An electronic health record (EHR) can be accessed by multiple authorized users. Health information from EHRs contributes to learning healthcare systems' development. The objective of this systematic review is to answer a question: What are ethical issues concerning research using EHRs in the literature? We searched Medline Ovid, Embase and Scopus for publications concerning ethical issues of research use of EHRs. We employed the constant comparative method to retrieve common ethical themes. We descriptively summarized empirical studies. The study reveals the breadth, depth, and complexity of ethical problems associated with research use of EHRs. The central ethical question that emerges from the review is how to manage access to EHRs. Managing accessibility consists of interconnected and overlapping issues: streamlining research access to EHRs, minimizing risk, engaging and educating patients, as well as ensuring trustworthy governance of EHR data. Most of the ethical problems concerning EHR-based research arise from rapid cultural change. The framing of concepts of privacy, as well as individual and public dimensions of beneficence, are changing. We are currently living in the middle of this transition period. Human emotions and mental habits, as well as laws, are lagging behind technological developments. In the medical tradition, individual patient's health has always been in the center. Transformation of healthcare care, its digitalization, seems to have some impacts on our perspective of health care ethics, research ethics and public health ethics.

Re-evaluating standards of human subjects protection for sensitive health data in social media networks

This study addresses ethical questions about conducting health science research using network data from social media platforms. We provide examples of ethically problematic areas related to participant consent, expectation of privacy, and social media networks. Further, to illustrate how researchers can maintain ethical integrity while leveraging social media networks, we describe a study that demonstrates the ability to use social media to identify individuals affected by cancer. We discuss best practices and ethical guidelines for studying social media network data, including data collection, analysis, and reporting.

Improving domain adaptation in de-identification of electronic health records through self-training

OBJECTIVE

De-identification is a fundamental task in electronic health records to remove protected health information entities. Deep learning models have proven to be promising tools to automate de-identification processes. However, when the target domain (where the model is applied) is different from the source domain (where the model is trained), the model often suffers a significant performance drop, commonly referred to as domain adaptation issue. In de-identification, domain adaptation issues can make the model vulnerable for deployment. In this work, we aim to close the domain gap by leveraging unlabeled data from the target domain.

MATERIALS AND METHODS

We introduce a self-training framework to address the domain adaptation issue by leveraging unlabeled data from the target domain. We validate the effectiveness on 4 standard de-identification datasets. In each experiment, we use a pair of datasets: labeled data from the source domain and unlabeled data from the target domain. We compare the proposed self-training framework with supervised learning that directly deploys the model trained on the source domain.

RESULTS

In summary, our proposed framework improves the F1-score by 5.38 (on average) when compared with direct deployment. For example, using i2b2-2014 as the training dataset and i2b2-2006 as the test, the proposed framework increases the F1-score from 76.61 to 85.41 (+8.8). The method also increases the F1-score by 10.86 for mimic-radiology and mimic-discharge.

CONCLUSION

Our work demonstrates an effective self-training framework to boost the domain adaptation performance for the de-identification task for electronic health records.

Approaches to Addressing Nonmedical Services and Care Coordination Needs for Older Adults

OBJECTIVE

Non-medical services care coordination for daily activities of living is crucial in improving older adults' health and enabling them to age in place, but little is known about specific practices and barriers in this space.

METHODS

Semi-structured interviews were conducted with 41 professionals serving older adults in greater Chicago, Illinois-which consists of diverse urban, suburban, and semi-rural communities-to contextualize non-medical services needs and care coordination processes.

RESULTS

In-home care, home-delivered meals, non-emergency transportation, and housing support were cited as the most commonly needed services, all requiring complex coordination support. Respondents noted a reliance on inefficient phone/fax usage for referral-making and cited major challenges in inter-professional communication, service funding/reimbursement, and HIPAA.

CONCLUSIONS

Non-medical services delivery for older adults is severely impacted by general siloing throughout the care continuum. Interventions are needed to enhance communication pathways and improve the salience and interdisciplinarity of non-medical services coordination for this population.

Health Information Privacy, Protection, and Use in the Expanding Digital Health Ecosystem: A Position Paper of the American College of Physicians

Technologic advancements and the evolving digital health landscape have offered innovative solutions to several of our health care system's issues as well as increased the number of digital interactions and type of personal health information that is generated and collected, both within and outside of traditional health care. This American College of Physicians' position paper discusses the state of privacy legislation and regulations, highlights existing gaps in health information privacy protections, and outlines policy principles and recommendations for the development of health information privacy and security protections that are comprehensive, transparent, understandable, adaptable, and enforceable. The principles and recommendations aim to improve on the privacy framework in which physicians have practiced for decades and expand similar privacy guardrails to entities not currently governed by privacy laws and regulations. The expanded privacy framework should protect personal health information from unauthorized, discriminatory, deceptive, or harmful uses and align with the principles of medical ethics, respect individual rights, and support the culture of trust necessary to maintain and improve care delivery.

ChartSweep: A HIPAA-compliant Tool to Automate Chart Review for Plastic Surgery Research

Retrospective chart review (RCR) is the process of manual patient data review to answer research questions. Large and heterogeneous datasets make the RCR process time-consuming, with potential to introduce errors. The authors therefore designed and developed ChartSweep to expedite the RCR process while remaining faithful to its methodological rigor. ChartSweep is an open-source tool that can be customized for use with any electronic health record system. ChartSweep was developed by the authors to extract information from electronic health records using the Python coding language. As proof-of-concept, the tool was tested in three studies: RCR1-Identification of subjects who underwent radiofrequency ablation in a cohort of patients who had undergone headache surgery (n = 172); RCR2-Identification of patients with a diagnosis of thoracic outlet syndrome in patients who underwent peripheral neuroplasty (n = 806); RCR3-Identification of patients with a history of implant illness or breast implant-associated anaplastic large cell lymphoma in patients who had undergone implant-based breast augmentation or reconstruction (n = 1133). Inter-rater reliability was assessed. ChartSweep reduced the time required to conduct RCR1 by 1315 minutes (21.9 hours), RCR2 by 1664 minutes (27.7 hours), and RCR3 by 2215 minutes (36.9 hours). Inter-rater reliability was uncompromised (k = 1.00). Open-source Python libraries as leveraged by ChartSweep significantly accelerate the RCR process in plastic surgery research. Quality of data review is not compromised. Further analyses with larger, heterogeneous study populations are required to further validate ChartSweep as a research tool.

Machine Learning in Clinical Psychology and Psychotherapy Education: A Mixed Methods Pilot Survey of Postgraduate Students at a Swiss University

Background: There is increasing use of psychotherapy apps in mental health care. Objective: This mixed methods pilot study aimed to explore postgraduate clinical psychology students' familiarity and formal exposure to topics related to artificial intelligence and machine learning (AI/ML) during their studies. Methods: In April-June 2020, we conducted a mixed-methods online survey using a convenience sample of 120 clinical psychology students enrolled in a two-year Masters' program at a Swiss University. Results: In total 37 students responded (response rate: 37/120, 31%). Among respondents, 73% (n = 27) intended to enter a mental health profession, and 97% reported that they had heard of the term "machine learning." Students estimated 0.52% of their program would be spent on AI/ML education. Around half (46%) reported that they intended to learn about AI/ML as it pertained to mental health care. On 5-point Likert scale, students "moderately agreed" (median = 4) that AI/M should be part of clinical psychology/psychotherapy education. Qualitative analysis of students' comments resulted in four major themes on the impact of AI/ML on mental healthcare: (1) Changes in the quality and understanding of psychotherapy care; (2) Impact on patient-therapist interactions; (3) Impact on the psychotherapy profession; (4) Data management and ethical issues. Conclusions: This pilot study found that postgraduate clinical psychology students held a wide range of opinions but had limited formal education on how AI/ML-enabled tools might impact psychotherapy. The survey raises questions about how curricula could be enhanced to educate clinical psychology/psychotherapy trainees about the scope of AI/ML in mental healthcare.

CE: Nursing Orientation to Data Science and Machine Learning

ABSTRACT

Nurses collect, use, and produce data every day in countless ways, such as when assessing and treating patients, performing administrative functions, and engaging in strategic planning in their organizations and communities. These data are aggregated into large data sets in health care systems, public and private databases, and academic research settings. In recent years the machines used in this work (computer hardware) have become increasingly able to analyze large data sets, or "big data," at high speed. Data scientists use machine learning tools to aid in analyzing this big data, such as data amassed from large numbers of electronic health records. In health care, predictions for patient outcomes has become a focus of research using machine learning. It's important for nurses and nurse administrators to understand how machine learning has changed our ways of thinking about data and turning data into knowledge that can improve patient care. This article provides an orientation to machine learning and data science, offers an understanding of current challenges and opportunities, and describes the nursing implications for nurses in various roles.

Patient privacy and autonomy: a comparative analysis of cases of ethical dilemmas in China and the United States

Background

Respect for patients’ autonomy is usually considered to be an important ethical principle in Western countries; privacy is one of the implications of such respect. Healthcare professionals frequently encounter ethical dilemmas during their practice. The past few decades have seen an increased use of courts to resolve intractable ethical dilemmas across both the developed and the developing world. However, Chinese and American bioethics differ largely due to the influence of Chinese Confucianism and Western religions, respectively, and there is a dearth of comparative studies that explore cases of ethical dilemmas between China and the United States.

Methods

This paper discusses four typical cases with significant social impact. First, it compares two cases concerning patient privacy: the “Shihezi University Hospital Case”, in which a patient was used as a clinical teaching object without her permission, and the “New York-Presbyterian Hospital Case”, in which the hospital allowed the filming of a patient’s treatment without his consent. Second, it compares two cases regarding patient autonomy and potentially life-saving medical procedures: the “Case of Ms. L”, concerning a cohabitant’s refusal to sign a consent form for a pregnant woman’s caesarean, and the “Case of Mrs. V”, concerning a hospital’s insistence upon a blood transfusion for a dissenting patient. This paper introduces the supporting and opposing views for each case and discusses their social impact. It then compares and analyses the differences between China and the United States from cultural and legislative perspectives.

Conclusions

Ethical dilemmas have often occurred in China due to the late development of bioethics. However, the presence of bioethics earlier in the US than in China has not spared the US of ethical dilemmas. This paper highlights lessons and inspiration from the cases for healthcare professionals and introduces readers to the role and weight of privacy and autonomy in China and in the US from the perspectives of different cultures, religions and laws.

Updating HIPAA for the electronic medical record era

With advances in technology, patients increasingly expect to access their health information on their phones and computers seamlessly, whenever needed, to meet their clinical needs. The 1996 passage of the Health Insurance Portability and Accountability Act (HIPAA), modifications made by the Health Information Technology for Economic and Clinical Health Act (HITECH), and the recent 21st Century Cures Act (Cures) promise to make patients' health information available to them without special effort and at no cost. However, inconsistencies among these policies' definitions of what is included in "health information", widespread variation in electronic health record system capabilities, and differences in local health system policies around health data release have created a confusing landscape for patients, health care providers, and third parties who reuse health information. In this article, we present relevant regulatory history, describe challenges to health data portability and fluidity, and present the authors' policy recommendations for lawmakers to consider so that the vision of HIPAA, HITECH, and Cures may be fulfilled.

The Competing Demands of Patient Privacy and Clinical Research

Privacy and confidentiality of personal medical information are cornerstones of ethical clinical care and ethical research. But real-world research has challenged traditional ways of thinking about privacy and confidentiality of information. In today's world of "big data" and learning health care systems, researchers and others are combining multiple sources of information to address complex problems. We present a case study that highlights the ethical concerns that arise when a patient who is employed by an academic medical center learns through a research invitational letter that her private information was accessed at this center without her consent. We discuss the ethical challenges of balancing patient privacy with advancing clinical research and ask, what level of privacy and confidentiality can and should patients expect from their clinician providers, fellow research colleagues, and institutions?

Ethics and informatics in the age of COVID-19: challenges and recommendations for public health organization and public policy

The COVID-19 pandemic response in the United States has exposed significant gaps in information systems and processes that prevent timely clinical and public health decision-making. Specifically, the use of informatics to mitigate the spread of SARS-CoV-2, support COVID-19 care delivery, and accelerate knowledge discovery bring to the forefront issues of privacy, surveillance, limits of state powers, and interoperability between public health and clinical information systems. Using a consensus-building process, we critically analyze informatics-related ethical issues in light of the pandemic across 3 themes: (1) public health reporting and data sharing, (2) contact tracing and tracking, and (3) clinical scoring tools for critical care. We provide context and rationale for ethical considerations and recommendations that are actionable during the pandemic and conclude with recommendations calling for longer-term, broader change (beyond the pandemic) for public health organization and policy reform.

Precision medicine in the era of artificial intelligence: implications in chronic disease management

Aberrant metabolism is the root cause of several serious health issues, creating a huge burden to health and leading to diminished life expectancy. A dysregulated metabolism induces the secretion of several molecules which in turn trigger the inflammatory pathway. Inflammation is the natural reaction of the immune system to a variety of stimuli, such as pathogens, damaged cells, and harmful substances. Metabolically triggered inflammation, also called metaflammation or low-grade chronic inflammation, is the consequence of a synergic interaction between the host and the exposome-a combination of environmental drivers, including diet, lifestyle, pollutants and other factors throughout the life span of an individual. Various levels of chronic inflammation are associated with several lifestyle-related diseases such as diabetes, obesity, metabolic associated fatty liver disease (MAFLD), cancers, cardiovascular disorders (CVDs), autoimmune diseases, and chronic lung diseases. Chronic diseases are a growing concern worldwide, placing a heavy burden on individuals, families, governments, and health-care systems. New strategies are needed to empower communities worldwide to prevent and treat these diseases. Precision medicine provides a model for the next generation of lifestyle modification. This will capitalize on the dynamic interaction between an individual's biology, lifestyle, behavior, and environment. The aim of precision medicine is to design and improve diagnosis, therapeutics and prognostication through the use of large complex datasets that incorporate individual gene, function, and environmental variations. The implementation of high-performance computing (HPC) and artificial intelligence (AI) can predict risks with greater accuracy based on available multidimensional clinical and biological datasets. AI-powered precision medicine provides clinicians with an opportunity to specifically tailor early interventions to each individual. In this article, we discuss the strengths and limitations of existing and evolving recent, data-driven technologies, such as AI, in preventing, treating and reversing lifestyle-related diseases.

Ethical and Legal Implications of Remote Monitoring of Medical Devices

Policy Points Millions of life-sustaining implantable devices collect and relay massive amounts of digital health data, increasingly by using user-downloaded smartphone applications to facilitate data relay to clinicians via manufacturer servers. Our analysis of health privacy laws indicates that most US patients may have little access to their own digital health data in the United States under the Health Insurance Portability and Accountability Act Privacy Rule, whereas the EU General Data Protection Regulation and the California Consumer Privacy Act grant greater access to device-collected data. Our normative analysis argues for consistently granting patients access to the raw data collected by their implantable devices.

CONTEXT

Millions of life-sustaining implantable devices collect and relay massive amounts of digital health data, increasingly by using user-downloaded smartphone applications to facilitate data relay to clinicians via manufacturer servers. Whether patients have either legal or normative claims to data collected by these devices, particularly in the raw, granular format beyond that summarized in their medical records, remains incompletely explored.

METHODS

Using pacemakers and implantable cardioverter-defibrillators (ICDs) as a clinical model, we outline the clinical ecosystem of data collection, relay, retrieval, and documentation. We consider the legal implications of US and European privacy regulations for patient access to either summary or raw device data. Lastly, we evaluate ethical arguments for or against providing patients access to data beyond the summaries presented in medical records.

FINDINGS

Our analysis of applicable health privacy laws indicates that US patients may have little access to their raw data collected and held by device manufacturers in the United States under the Health Insurance Portability and Accountability Act Privacy Rule, whereas the EU General Data Protection Regulation (GDPR) grants greater access to device-collected data when the processing of personal data falls under the GDPR's territorial scope. The California Consumer Privacy Act, the "little sister" of the GDPR, also grants greater rights to California residents. By contrast, our normative analysis argues for consistently granting patients access to the raw data collected by their implantable devices. Smartphone applications are increasingly involved in the collection, relay, retrieval, and documentation of these data. Therefore, we argue that smartphone user agreements are an emerging but potentially underutilized opportunity for clarifying both legal and ethical claims for device-derived data.

CONCLUSIONS

Current health privacy legislation incompletely supports patients' normative claims for access to digital health data.

Protecting health privacy even when privacy is lost

The standard approach to protecting privacy in healthcare aims to control access to personal information. We cannot regain control of information after it has been shared, so we must restrict access from the start. This 'control' conception of privacy conflicts with data-intensive initiatives like precision medicine and learning health systems, as they require patients to give up significant control of their information. Without adequate alternatives to the control-based approach, such data-intensive programmes appear to require a loss of privacy. This paper argues that the control view of privacy is shortsighted and overlooks important ways to protect health information even when widely shared. To prepare for a world where we no longer control our data, we must pursue three alternative strategies: obfuscate health data, penalise the misuse of health data and improve transparency around who shares our data and for what purposes. Prioritising these strategies is necessary when health data are widely shared both within and outside of the health system.

Patient Data-Sharing for AI: Ethical Challenges, Catholic Solutions

Recent news of Catholic and secular healthcare systems sharing electronic health record (EHR) data with technology companies for the purposes of developing artificial intelligence (AI) applications has drawn attention to the ethical and social challenges of such collaborations, including threats to patient privacy and confidentiality, undermining of patient consent, and lack of corporate transparency. Although the United States Catholic Conference of Bishops' Ethical and Religious Directives for Health Care Services (ERDs) address collaborations between US Catholic healthcare providers and other entities, the ERDs do not adequately address the novel concerns seen in EHR data-sharing for AI development. Neither does the Health Insurance Portability and Accountability Act (HIPAA) privacy rule. This article describes ethical and social problems observed in recent patient data-sharing collaborations with AI companies and analyzes them in light of the guiding principles of the ERDs as well as the 2020 Rome Call to AI Ethics (RCAIE) document recently released by the Vatican. While both the ERDs and RCAIE guiding principles can inform future collaborations, we suggest that the next revision of the ERDs should consider addressing data-sharing and AI more directly.

Summary

Electronic health record data-sharing with artificial intelligence developers presents unique ethical and social challenges that can be addressed with updated United States Catholic Conference of Bishops' Ethical and Religious Directives and guidance from the Vatican's 2020 Rome Call to AI Ethics.