Sharing Is Caring-Data Sharing Initiatives in Healthcare

GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics

BACKGROUND

Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe aim to strike a balance between data security, privacy, and the imperative for access.

RESULTS

We present the Gemelli Generator - Real World Data (GEN-RWD) Sandbox, a modular multi-agent platform designed for distributed analytics in healthcare. Its primary objective is to empower external researchers to leverage hospital data while upholding privacy and ownership, obviating the need for direct data sharing. Docker compatibility adds an extra layer of flexibility, and scalability is assured through modular design, facilitating combinations of Proxy and Processor modules with various graphical interfaces. Security and reliability are reinforced through components like Identity and Access Management (IAM) agent, and a Blockchain-based notarisation module. Certification processes verify the identities of information senders and receivers.

CONCLUSIONS

The GEN-RWD Sandbox architecture achieves a good level of usability while ensuring a blend of flexibility, scalability, and security. Featuring a user-friendly graphical interface catering to diverse technical expertise, its external accessibility enables personnel outside the hospital to use the platform. Overall, the GEN-RWD Sandbox emerges as a comprehensive solution for healthcare distributed analytics, maintaining a delicate equilibrium between accessibility, scalability, and security.

The Integration and Impact of Artificial Intelligence in Otolaryngology-Head and Neck Surgery: Navigating the Last Mile

Incorporating artificial Intelligence and machine learning into otolaryngology requires careful data handling, security, and ethical considerations. Success depends on interdisciplinary cooperation, consistent innovation, and regulatory compliance to improve clinical outcomes, provider experience, and operational effectiveness.

Emergency department documentation of legal intervention injuries at a Washington, DC, hospital

BACKGROUND

The U.S. Centers for Disease Control and Prevention (CDC) defines legal intervention injuries as injuries caused by law enforcement agents in the course of official duties. Public health databases utilize International Classification of Diseases, 10th Revision (ICD-10), coding to collect these data through the "Y35" family ICD-10 code. Prior studies report deficiencies in public health recording of fatal legal intervention injuries. Few studies have characterized nonfatal injuries. This study investigates emergency department (ED) capture of legal intervention injury diagnostic coding.

METHODS

A retrospective chart review was performed on ED encounter data from January 1, 2017, to June 30, 2019, at an academic hospital in Washington, DC. Charts were identified using a keyword search program for "police." Chart abstracters reviewed the flagged charts and abstracted those that met injury definition. Primary outcomes included injury severity, patient demographics, and documented ICD-10 codes. One sample proportion testing was performed comparing sample census ED data.

RESULTS

A total of 340 encounters had sufficient descriptions of legal intervention injuries. A total of 259 had descriptions consistent with the patient specifier of "suspect." Hospital coders recorded 74 charts (28.6%) with the Y35 family legal intervention injury code. A total of 212 involved a Black patient. A total of 122 patients had Medicaid and 94 were uninsured. Black patients made up a higher proportion of individuals in the "suspect identified legal intervention injury" group than the total population (0.819 vs. 0.609, p < 0.01, 95% CI 0.772-0.866). Patients with Medicaid or who were uninsured made up substantial proportions as well (0.471 vs. 0.175, p < 0.01, 95% CI 0.410-0.532 for Medicaid patients and 0.363 vs. 0.155, p < 0.01, 95% CI 0.304-0.424 for the uninsured patients).

CONCLUSION

A large proportion of nonfatal legal intervention injuries remain unreported. Black and low-income patients are disproportionately affected. More research is needed but benefits from interprofessional data sharing, injury pattern awareness, and diagnostic coding guidance may improve reporting.

Now Is the Time to Strengthen Government-Academic Data Infrastructures to Jump-Start Future Public Health Crisis Response

During public health crises, the significance of rapid data sharing cannot be overstated. In attempts to accelerate COVID-19 pandemic responses, discussions within society and scholarly research have focused on data sharing among health care providers, across government departments at different levels, and on an international scale. A lesser-addressed yet equally important approach to sharing data during the COVID-19 pandemic and other crises involves cross-sector collaboration between government entities and academic researchers. Specifically, this refers to dedicated projects in which a government entity shares public health data with an academic research team for data analysis to receive data insights to inform policy. In this viewpoint, we identify and outline documented data sharing challenges in the context of COVID-19 and other public health crises, as well as broader crisis scenarios encompassing natural disasters and humanitarian emergencies. We then argue that government-academic data collaborations have the potential to alleviate these challenges, which should place them at the forefront of future research attention. In particular, for researchers, data collaborations with government entities should be considered part of the social infrastructure that bolsters their research efforts toward public health crisis response. Looking ahead, we propose a shift from ad hoc, intermittent collaborations to cultivating robust and enduring partnerships. Thus, we need to move beyond viewing government-academic data interactions as 1-time sharing events. Additionally, given the scarcity of scholarly exploration in this domain, we advocate for further investigation into the real-world practices and experiences related to sharing data from government sources with researchers during public health crises.

Charting Tomorrow's Healthcare: A Traditional Literature Review for an Artificial Intelligence-Driven Future

Electronic health record (EHR) systems have developed over time in parallel with general advancements in mainstream technology. As artificially intelligent (AI) systems rapidly impact multiple societal sectors, it has become apparent that medicine is not immune from the influences of this powerful technology. Particularly appealing is how AI may aid in improving healthcare efficiency with note-writing automation. This literature review explores the current state of EHR technologies in healthcare, specifically focusing on possibilities for addressing EHR challenges through the automation of dictation and note-writing processes with AI integration. This review offers a broad understanding of existing capabilities and potential advancements, emphasizing innovations such as voice-to-text dictation, wearable devices, and AI-assisted procedure note dictation. The primary objective is to provide researchers with valuable insights, enabling them to generate new technologies and advancements within the healthcare landscape. By exploring the benefits, challenges, and future of AI integration, this review encourages the development of innovative solutions, with the goal of enhancing patient care and healthcare delivery efficiency.

From "wading through treacle" to "making haste slowly": A comprehensive yet parsimonious model of drivers and challenges to implementing patient data sharing projects based on an EPaCCS evaluation and four pre-existing literature reviews

Conceptually, this study aimed to 1) identify the challenges and drivers encountered by England's Electronic Palliative Care Coordination System (EPaCCS) projects in the context of challenges and drivers in other projects on data sharing for individual care (also referred to as Health Information Exchange, HIE) and 2) organise them in a comprehensive yet parsimonious framework. The study also had a strong applied goal: to derive specific and non-trivial recommendations for advancing data sharing projects, particularly ones in early stages of development and implementation. Primary data comprised 40 in-depth interviews with 44 healthcare professionals, patients, carers, project team members and decision makers in Cambridgeshire, UK. Secondary data were extracted from four pre-existing literature reviews on Health Information Exchange and Health Information Technology implementation covering 135 studies. Thematic and framework analysis underpinned by "pluralist" coding were the main analytical approaches used. We reduced an initial set of >1,800 parameters into >500 challenges and >300 drivers to implementing EPaCCS and other data sharing projects. Less than a quarter of the 800+ parameters were associated primarily with the IT solution. These challenges and drivers were further condensed into an action-guiding, strategy-informing framework of nine types of "pure challenges", four types of "pure drivers", and nine types of "oppositional or ambivalent forces". The pure challenges draw parallels between patient data sharing and other broad and complex domains of sociotechnical or social practice. The pure drivers differ in how internal or external to the IT solution and project team they are, and thus in the level of control a project team has over them. The oppositional forces comprise pairs of challenges and drivers where the driver is a factor serving to resolve or counteract the challenge. The ambivalent forces are factors perceived simultaneously as a challenge and a driver depending on context, goals and perspective. The framework is distinctive in its emphasis on: 1) the form of challenges and drivers; 2) ambivalence, ambiguity and persistent tensions as fundamental forces in the field of innovation implementation; and 3) the parallels it draws with a variety of non-IT, non-health domains of practice as a source of fruitful learning. Teams working on data sharing projects need to prioritise further the shaping of social interactions and structural and contextual parameters in the midst of which their IT tools are implemented. The high number of "ambivalent forces" speaks of the vital importance for data sharing projects of skills in eliciting stakeholders' assumptions; managing conflict; and navigating multiple needs, interests and worldviews.

Decentralizing Health Care: History and Opportunities of Web3

This paper explores the relationship between the development of the internet and health care, highlighting their parallel growth and mutual influence. It delves into the transition from the early, static days of Web 1.0, akin to siloed physician expertise in health care, to the more interactive and patient-centric era of Web 2.0, which was accompanied by advancements in medical technologies and patient engagement. This paper then focuses on the emerging era of Web3-the decentralized web-which promises a transformative shift in health care, particularly in how patient data are managed, accessed, and used. This shift toward Web3 involves using blockchain technology for decentralized data storage to enhance patient data access, control, privacy, and value. This paper also examines current applications and pilot projects demonstrating Web3's practical use in health care and discusses key questions and considerations for its successful implementation.

Toward a common standard for data and specimen provenance in life sciences

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

Standardizing the Head and Neck Cancer Treatment and Research

Head and neck cancer requires a multidisciplinary approach, with standardized care being essential for consistent, high-quality treatment. Standardization involves evidence-based guidelines and protocols, and collaboration is necessary for research and improving outcomes. However, collaboration can be challenging due to various barriers. Collaboration can improve care by facilitating sharing of knowledge, access to technology, clinical trials, data sharing, funding and education. To improve collaboration, a shared vision, communication channels, guidelines, centralized database, training programs, culture of collaboration and funding should be established.

Challenges in mapping European rare disease databases, relevant for ML-based screening technologies in terms of organizational, FAIR and legal principles: scoping review

Background

Given the increased availability of data sources such as hospital information systems, electronic health records, and health-related registries, a novel approach is required to develop artificial intelligence-based decision support that can assist clinicians in their diagnostic decision-making and shorten rare disease patients' diagnostic odyssey. The aim is to identify key challenges in the process of mapping European rare disease databases, relevant to ML-based screening technologies in terms of organizational, FAIR and legal principles.

Methods

A scoping review was conducted based on the PRISMA-ScR checklist. The primary article search was conducted in three electronic databases (MEDLINE/Pubmed, Scopus, and Web of Science) and a secondary search was performed in Google scholar and on the organizations' websites. Each step of this review was carried out independently by two researchers. A charting form for relevant study analysis was developed and used to categorize data and identify data items in three domains - organizational, FAIR and legal.

Results

At the end of the screening process, 73 studies were eligible for review based on inclusion and exclusion criteria with more than 60% (n = 46) of the research published in the last 5 years and originated only from EU/EEA countries. Over the ten-year period (2013-2022), there is a clear cycling trend in the publications, with a peak of challenges reporting every four years. Within this trend, the following dynamic was identified: except for 2016, organizational challenges dominated the articles published up to 2018; legal challenges were the most frequently discussed topic from 2018 to 2022. The following distribution of the data items by domains was observed - (1) organizational (n = 36): data accessibility and sharing (20.2%); long-term sustainability (18.2%); governance, planning and design (17.2%); lack of harmonization and standardization (17.2%); quality of data collection (16.2%); and privacy risks and small sample size (11.1%); (2) FAIR (n = 15): findable (17.9%); accessible sustainability (25.0%); interoperable (39.3%); and reusable (17.9%); and (3) legal (n = 33): data protection by all means (34.4%); data management and ownership (22.9%); research under GDPR and member state law (20.8%); trust and transparency (13.5%); and digitalization of health (8.3%). We observed a specific pattern repeated in all domains during the process of data charting and data item identification - in addition to the outlined challenges, good practices, guidelines, and recommendations were also discussed. The proportion of publications addressing only good practices, guidelines, and recommendations for overcoming challenges when mapping RD databases in at least one domain was calculated to be 47.9% (n = 35).

Conclusion

Despite the opportunities provided by innovation - automation, electronic health records, hospital-based information systems, biobanks, rare disease registries and European Reference Networks - the results of the current scoping review demonstrate a diversity of the challenges that must still be addressed, with immediate actions on ensuring better governance of rare disease registries, implementing FAIR principles, and enhancing the EU legal framework.

Proceedings from the CIHLMU 2022 Symposium: "Availability of and Access to Quality Data in Health"

Data is an essential tool for valid and reliable healthcare management. Access to high-quality data is critical to ensuring the early identification of problems, the design of appropriate interventions, and the effective implementation and evaluation of health intervention outcomes. During the COVID-19 pandemic, the need for strong information systems and the value of producing high-quality data for timely response and tracking resources and progress have been very evident across countries. The availability of and access to high-quality data at all levels of the health systems of low and middle-income countries is a challenge, which is exacerbated by multiple parallels and poorly integrated data sources, a lack of data-sharing standards and policy frameworks, their weak enforcement, and inadequate skills among those handling data. Completeness, accuracy, integrity, validity, and timeliness are challenges to data availability and use. "Big Data" is a necessity and a challenge in the current complexities of health systems. In transitioning to digital systems with proper data standards and policy frameworks for privacy protection, data literacy, ownership, and data use at all levels of the health system, skill enhancement of the staff is critical. Adequate funding for strengthening routine information systems and periodic surveys and research, and reciprocal partnerships between high-income countries and low- and middle-income countries in data generation and use, should be prioritized by the low- and middle-income countries to foster evidence-based healthcare practices.

Physicians differ in their perceptions of sensitive medical records: Survey and interview study

Physician categorizations of electronic health record (EHR) data (e.g., depression) into sensitive data categories (e.g., Mental Health) and their perspectives on the adequacy of the categories to classify medical record data were assessed. One thousand data items from patient EHR were classified by 20 physicians (10 psychiatrists paired with ten non-psychiatrist physicians) into data categories via a survey. Cluster-adjusted chi square tests and mixed models were used for analysis. 10 items were selected per each physician pair (100 items in total) for discussion during 20 follow-up interviews. Interviews were thematically analyzed. Survey item categorization yielded 500 (50.0%) agreements, 175 (17.5%) disagreements, 325 (32.5%) partial agreements. Categorization disagreements were associated with physician specialty and implied patient history. Non-psychiatrists selected significantly (p = .016) more data categories than psychiatrists when classifying data items. The endorsement of Mental Health and Substance Use categories were significantly (p = .001) related for both provider types. During thematic analysis, Encounter Diagnosis (100%), Problems (95%), Health Concerns (90%), and Medications (85%) were discussed the most when deciding the sensitivity of medical information. Most (90.0%) interview participants suggested adding additional data categories. Study findings may guide the evolution of digital patient-controlled granular data sharing technology and processes.

Quantitative approaches to guide epilepsy surgery from intracranial EEG

Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients.

Patient data for commercial companies? An ethical framework for sharing patients' data with for-profit companies for research

BACKGROUND

Research using data from medical care promises to advance medical science and improve healthcare. Academia is not the only sector that expects such research to be of great benefit. The research-based health industry is also interested in so-called 'real-world' health data to develop new drugs, medical technologies or data-based health applications. While access to medical data is handled very differently in different countries, and some empirical data suggest people are uncomfortable with the idea of companies accessing health information, this paper aims to advance the ethical debate about secondary use of medical data generated in the public healthcare sector by for-profit companies for medical research (ReuseForPro).

METHODS

We first clarify some basic concepts and our ethical-normative approach, then discuss and ethically evaluate potential claims and interests of relevant stakeholders: patients as data subjects in the public healthcare system, for-profit companies, the public, and physicians and their healthcare institutions. Finally, we address the tensions between legitimate claims of different stakeholders in order to suggest conditions that might ensure ethically sound ReuseForPro.

RESULTS

We conclude that there are good reasons to grant for-profit companies access to medical data if they meet certain conditions: among others they need to respect patients' informational rights and their actions need to be compatible with the public's interest in health benefit from ReuseForPro.

Talking Ethics Early in Health Data Public Private Partnerships

Data access and data sharing are vital to advance medicine. A growing number of public private partnerships are set up to facilitate data access and sharing, as private and public actors possess highly complementary health data sets and treatment development resources. However, the priorities and incentives of public and private organizations are frequently in conflict. This has complicated partnerships and sparked public concerns around ethical issues such as trust, justice or privacy-in turn raising an important problem in business and data ethics: how can ethical theory inform the practice of public and private partners to mitigate misaligned incentives, and ensure that they can deliver societally beneficial innovation? In this paper, we report on the development of the Swiss Personalized Health Network's ethical guidelines for health data sharing in public private partnerships. We describe the process of identifying ethical issues and engaging core stakeholders to incorporate their practical reality on these issues. Our report highlights core ethical issues in health data public private partnerships and provides strategies for how to overcome these in the Swiss health data context. By agreeing on and formalizing ethical principles and practices at the beginning of a partnership, partners and society can benefit from a relationship built around a mutual commitment to ethical principles. We present this summary in the hope that it will contribute to the global data sharing dialogue.

From big data to better patient outcomes

Among medical specialties, laboratory medicine is the largest producer of structured data and must play a crucial role for the efficient and safe implementation of big data and artificial intelligence in healthcare. The area of personalized therapies and precision medicine has now arrived, with huge data sets not only used for experimental and research approaches, but also in the "real world". Analysis of real world data requires development of legal, procedural and technical infrastructure. The integration of all clinical data sets for any given patient is important and necessary in order to develop a patient-centered treatment approach. Data-driven research comes with its own challenges and solutions. The Findability, Accessibility, Interoperability, and Reusability (FAIR) Guiding Principles provide guidelines to make data findable, accessible, interoperable and reusable to the research community. Federated learning, standards and ontologies are useful to improve robustness of artificial intelligence algorithms working on big data and to increase trust in these algorithms. When dealing with big data, the univariate statistical approach changes to multivariate statistical methods significantly shifting the potential of big data. Combining multiple omics gives previously unsuspected information and provides understanding of scientific questions, an approach which is also called the systems biology approach. Big data and artificial intelligence also offer opportunities for laboratories and the In Vitro Diagnostic industry to optimize the productivity of the laboratory, the quality of laboratory results and ultimately patient outcomes, through tools such as predictive maintenance and "moving average" based on the aggregate of patient results.

Returning study results to research participants: Data access, format, and sharing preferences

INTRODUCTION

Research participants have a growing expectation for transparency with their collected information; however, there is little guidance on participant preferences for receiving health information and how researchers should return this information to participants.

METHODS

We conducted a cross-sectional online survey with a representative sample of 502 participants in the United States. Participants were asked about their preferences for receiving, sharing, and the formatting of health information collected for research purposes.

RESULTS

Most participants wanted their health information returned (84 %) to use it for their own knowledge and to manage their own health. Email was the most preferred format for receiving health data (67 %), followed by online website (44 %), and/or paper copy (32 %). Data format preferences varied by age, education, financial resources, subjective numeracy, and health literacy. Around one third of Generation Z (25 %), Millennials (30 %), and Generation X (29 %) participants preferred to receive their health information with a mobile app. In contrast, very few Baby Boomers (12 %) and none from the Silent Generation preferred the mobile app format. Having a paper copy of the data was preferred by 38 % of participants without a college degree compared to those with a college degree. Preferences were highest for sharing all health information with doctors and nurses (77 %), and some information with friends and family (66 %).

CONCLUSION

Study findings support returning research information to participants in multiple formats, including email, online websites, and paper copy. Preferences for whom to share information with varied by stakeholders and by sociodemographic characteristics. Researchers should offer multiple formats to participants and tailor data sharing options to participants' preferences. Future research should further explore combinations of individual characteristics that may further influence data sharing and format preferences.

Empowering quality data - the gordian knot of bringing real innovation into healthcare system

OBJECTIVES

The introduction of Personalised Medicine (PM) into healthcare systems could benefit from a clearer understanding of the distinct national and regional frameworks around the world. Recent engagement by international regulators on maximising the use of real-world evidence (RWE) has highlighted the scope for improving the exploitation of the treasure-trove of health data that is currently largely neglected in many countries. The European Alliance for Personalised Medicine (EAPM) led an international study aimed at identifying the current status of conditions.

METHODS

A literature review examined how far such frameworks exist, with a view to identifying conducive factors - and crucial gaps. This extensive review of key factors across 22 countries and 5 regions revealed a wide variety of attitudes, approaches, provisions and conditions, and permitted the construction of a comprehensive overview of the current status of PM. Based on seven key pillars identified from the literature review and expert panels, the data was quantified, and on the basis of further analysis, an index was developed to allow comparison country by country and region by region.

RESULTS

The results show that United States of America is leading according to overall outcome whereas Kenya scored the least in the overall outcome.

CONCLUSIONS

Still, common approaches exist that could help accelerate take-up of opportunities even in the less prosperous parts of the world.

EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation

BACKGROUND

Modern biomedical research is data-driven and relies heavily on the re-use and sharing of data. Biomedical data, however, is subject to strict data protection requirements. Due to the complexity of the data required and the scale of data use, obtaining informed consent is often infeasible. Other methods, such as anonymization or federation, in turn have their own limitations. Secure multi-party computation (SMPC) is a cryptographic technology for distributed calculations, which brings formally provable security and privacy guarantees and can be used to implement a wide-range of analytical approaches. As a relatively new technology, SMPC is still rarely used in real-world biomedical data sharing activities due to several barriers, including its technical complexity and lack of usability.

RESULTS

To overcome these barriers, we have developed the tool EasySMPC, which is implemented in Java as a cross-platform, stand-alone desktop application provided as open-source software. The tool makes use of the SMPC method Arithmetic Secret Sharing, which allows to securely sum up pre-defined sets of variables among different parties in two rounds of communication (input sharing and output reconstruction) and integrates this method into a graphical user interface. No additional software services need to be set up or configured, as EasySMPC uses the most widespread digital communication channel available: e-mails. No cryptographic keys need to be exchanged between the parties and e-mails are exchanged automatically by the software. To demonstrate the practicability of our solution, we evaluated its performance in a wide range of data sharing scenarios. The results of our evaluation show that our approach is scalable (summing up 10,000 variables between 20 parties takes less than 300 s) and that the number of participants is the essential factor.

CONCLUSIONS

We have developed an easy-to-use "no-code solution" for performing secure joint calculations on biomedical data using SMPC protocols, which is suitable for use by scientists without IT expertise and which has no special infrastructure requirements. We believe that innovative approaches to data sharing with SMPC are needed to foster the translation of complex protocols into practice.

Literature analysis of artificial intelligence in biomedicine

Artificial intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning (ML), deep learning (DL) and neural networks (NNs). AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of 'big data' and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-3 of diseases studied using AI; China, the United States, South Korea, the United Kingdom and Canada are publishing the most articles in AI research; Stanford University is the world's leading university in AI research; and convolutional NNs are by far the most popular DL algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

Privacy-by-Design Environments for Large-Scale Health Research and Federated Learning from Data

This article offers a brief overview of 'privacy-by-design (or data-protection-by-design) research environments', namely Trusted Research Environments (TREs, most commonly used in the United Kingdom) and Personal Health Trains (PHTs, most commonly used in mainland Europe). These secure environments are designed to enable the safe analysis of multiple, linked (and often big) data sources, including sensitive personal data and data owned by, and distributed across, different institutions. They take data protection and privacy requirements into account from the very start (conception phase, during system design) rather than as an afterthought or 'patch' implemented at a later stage on top of an existing environment. TREs and PHTs are becoming increasingly important for conducting large-scale privacy-preserving health research and for enabling federated learning and discoveries from big healthcare datasets. The paper also presents select examples of successful TRE and PHT implementations and of large-scale studies that used them.

Quality of Multicenter Studies Using MRI Radiomics for Diagnosing Clinically Significant Prostate Cancer: A Systematic Review

Background: Reproducibility and generalization are major challenges for clinically significant prostate cancer modeling using MRI radiomics. Multicenter data seem indispensable to deal with these challenges, but the quality of such studies is currently unknown. The aim of this study was to systematically review the quality of multicenter studies on MRI radiomics for diagnosing clinically significant PCa. Methods: This systematic review followed the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Multicenter studies investigating the value of MRI radiomics for the diagnosis of clinically significant prostate cancer were included. Quality was assessed using the checklist for artificial intelligence in medical imaging (CLAIM) and the radiomics quality score (RQS). CLAIM consisted of 42 equally important items referencing different elements of good practice AI in medical imaging. RQS consisted of 36 points awarded over 16 items related to good practice radiomics. Final CLAIM and RQS scores were percentage-based, allowing for a total quality score consisting of the average of CLAIM and RQS. Results: Four studies were included. The average total CLAIM score was 74.6% and the average RQS was 52.8%. The corresponding average total quality score (CLAIM + RQS) was 63.7%. Conclusions: A very small number of multicenter radiomics PCa classification studies have been performed with the existing studies being of bad or average quality. Good multicenter studies might increase by encouraging preferably prospective data sharing and paying extra care to documentation in regards to reproducibility and clinical utility.

Deep Learning Applications for Acute Stroke Management

Brain imaging is essential to the clinical care of patients with stroke, a leading cause of disability and death worldwide. Whereas advanced neuroimaging techniques offer opportunities for aiding acute stroke management, several factors, including time delays, inter-clinician variability, and lack of systemic conglomeration of clinical information, hinder their maximal utility. Recent advances in deep machine learning (DL) offer new strategies for harnessing computational medical image analysis to inform decision making in acute stroke. We examine the current state of the field for DL models in stroke triage. First, we provide a brief, clinical practice-focused primer on DL. Next, we examine real-world examples of DL applications in pixel-wise labeling, volumetric lesion segmentation, stroke detection, and prediction of tissue fate postintervention. We evaluate recent deployments of deep neural networks and their ability to automatically select relevant clinical features for acute decision making, reduce inter-rater variability, and boost reliability in rapid neuroimaging assessments, and integrate neuroimaging with electronic medical record (EMR) data in order to support clinicians in routine and triage stroke management. Ultimately, we aim to provide a framework for critically evaluating existing automated approaches, thus equipping clinicians with the ability to understand and potentially apply DL approaches in order to address challenges in clinical practice. ANN NEUROL 2022.

AI-Based Predictive Modelling of the Onset and Progression of Dementia

Dementia, the most severe expression of cognitive impairment, is among the main causes of disability in older adults and currently affects over 55 million individuals. Dementia prevention is a global public health priority, and recent studies have shown that dementia risk can be reduced through non-pharmacological interventions targeting different lifestyle areas. The FINnish GERiatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) has shown a positive effect on cognition in older adults at risk of dementia through a 2-year multidomain intervention targeting lifestyle and vascular risk factors. The LETHE project builds on these findings and will provide a digital-enabled FINGER intervention model for delaying or preventing the onset of cognitive decline. An individualised ICT-based multidomain, preventive lifestyle intervention program will be implemented utilising behaviour and intervention data through passive and active data collection. Artificial intelligence and machine learning methods will be used for data-driven risk factor prediction models. An initial model based on large multinational datasets will be validated and integrated into an 18-month trial integrating digital biomarkers to further improve the model. Furthermore, the LETHE project will investigate the concept of federated learning to, on the one hand, protect the privacy of the health and behaviour data and, on the other hand, to provide the opportunity to enhance the data model easily by integrating additional clinical centres.

Perceptions and Opinions Towards Data-Sharing: A Survey of Addiction Journal Editorial Board Members

Background

We surveyed addiction journal editorial board members to better understand their opinions towards data-sharing.

Methods

Survey items consisted of Likert-type (e.g., one to five scale), multiple-choice, and free-response questions. Journal websites were searched for names and email addresses. Emails were distributed using SurveyMonkey. Descriptive statistics were used to characterize the responses.

Results

We received 178 responses (of 1039; 17.1%). Of these, 174 individuals agreed to participate in our study (97.8%). Most respondents did not know whether their journal had a data-sharing policy. Board members "somewhat agree" that addiction journals should recommend but not require data-sharing for submitted manuscripts [M=4.09 (SD=0.06); 95% CI: 3.97-4.22]. Items with the highest perceived benefit ratings were "secondary data use (e.g., meta-analysis)" [M=3.44 (SD=0.06); 95% CI: 3.31-3.56] and "increased transparency" [M=3.29 (SD=0.07); 95% CI: 3.14-3.43]. Items perceived to be the greatest barrier to data-sharing included "lack of metadata standards" [M=3.21 (SD=0.08); 95% CI: 3.06-3.36], "no incentive" [M=3.43 (SD=0.07); 95% CI: 3.30-3.57], "inadequate resources" [M=3.53 (SD=0.05); 95% CI: 3.42-3.63], and "protection of privacy"[M=3.22 (SD=0.07); 95% CI: 3.07-3.36].

Conclusion

Our results suggest addiction journal editorial board members believe data-sharing has a level of importance within the research community. However, most board members are unaware of their journals' data-sharing policies, and most data-sharing should be recommended but not required. Future efforts aimed at better understanding common reservations and benefits towards data-sharing, as well as avenues to optimize data-sharing while minimizing potential risks, are warranted.

Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls

Background: Machine learning (ML) is a key component of artificial intelligence (AI). The terms machine learning, artificial intelligence, and deep learning are erroneously used interchangeably as they appear as monolithic nebulous entities. This technology offers immense possibilities and opportunities to advance diagnostics in the field of medicine and dentistry. This necessitates a deep understanding of AI and its essential components, such as machine learning (ML), artificial neural networks (ANN), and deep learning (DP). Aim: This review aims to enlighten clinicians regarding AI and its applications in the diagnosis of oral diseases, along with the prospects and challenges involved. Review results: AI has been used in the diagnosis of various oral diseases, such as dental caries, maxillary sinus diseases, periodontal diseases, salivary gland diseases, TMJ disorders, and oral cancer through clinical data and diagnostic images. Larger data sets would enable AI to predict the occurrence of precancerous conditions. They can aid in population-wide surveillance and decide on referrals to specialists. AI can efficiently detect microfeatures beyond the human eye and augment its predictive power in critical diagnosis. Conclusion: Although studies have recognized the benefit of AI, the use of artificial intelligence and machine learning has not been integrated into routine dentistry. AI is still in the research phase. The coming decade will see immense changes in diagnosis and healthcare built on the back of this research. Clinical significance: This paper reviews the various applications of AI in dentistry and illuminates the shortcomings faced while dealing with AI research and suggests ways to tackle them. Overcoming these pitfalls will aid in integrating AI seamlessly into dentistry.

Jordanian views regarding sharing of medical data for research: A cross-sectional study during COVID-19 pandemic

PURPOSE

In the current study, the views of Jordanian regarding sharing medical reports for research purposes were investigated during the COVID-19 pandemic. In addition, motivators and barriers regarding sharing of medical records were examined.

METHODS

This observational survey-based cross-sectional study was conducted using an electronic questionnaire during the COVID-19 pandemic (second half of 2020). The questionnaire link was disseminated through two social media platforms (WhatsApp and Facebook), targeting Jordanian adults (age >18 years).

RESULTS

In this study, 1,194 participants agreed to complete the study survey. Results showed that 58.3% of them (n = 696) reported to be willing to share their medical data. while 17.6% of the participants (n = 210) showed hesitancy to share their medical information. The most important motivators as perceived by the study participants were helping other patients who have similar health conditions (n = 995, 83.3%). Moreover, fearing from stigma (n = 753, 63.1%), and the lack of confidence in data security and privacy (n = 728, 61.0%) were among the main barriers preventing participants from sharing their information. Finally, results showed that participants with higher educational level (bachelor or higher) (OR = 0.299, P<0.001), or those living in center of Jordan (OR = 0.270, P<0.001) showed a lower tendency to share their medical data. While participants those who have shared data before showed a higher tendency to share their medical data (OR = 2.524, P<0.001).

CONCLUSION

In this study, many of the participants had a positive attitude towards sharing biomedical data for scientific research during the COVID-19 pandemic, many had doubts in the control over their data. Thus, policymakers and data users should address the concerns and values of patients and understand their preferences in favor of an ethically scrupulous use of data in research.

The Risk Status of Waiting Areas for Airborne Infection Control in Delhi Hospitals

Background

Hospital waiting areas are overlooked from the airborne infection control viewpoint as they are not classified as critical for infection control. This is the area where undiagnosed and potentially infected patients gather with susceptible and vulnerable patients, and there is no mechanism to segregate the two, especially when the potentially infected visitors/patients themselves are unaware of the infection or may be asymptomatic. It is important to know whether hospitals in Delhi, a populated, low-resource setting having community transmission/occurrence of airborne diseases such as tuberculosis, consider waiting areas as critical. Hence, this study aims to determine whether hospitals in Delhi consider waiting areas as critical areas from the airborne infection control viewpoint.

Methodology

The Right to Information Act, 2005, was used to request information from 11 hospitals included in this study.

Results

After compiling the results, it was found that five out of the 11 hospitals did not consider waiting areas as critical from the infection spread point of view. Two of the 11 hospitals acknowledged the criticality of waiting areas but did not include the same in the list of critical areas. Only three out of the 11 considered waiting areas as critical and included these in the list of critical areas in a hospital.

Conclusions

This study provided evidence that most hospitals in Delhi do not include waiting areas in the list of critical areas in a hospital.

Data Science in Healthcare: COVID-19 and Beyond

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning (ML), neural networks (NN) and artificial intelligence (AI), to create value, based on extracting knowledge and insights from available 'big' data [...].

Medical-Blocks: A Platform for Exploration, Management, Analysis, and Sharing of Data in Biomedical Research

BACKGROUND

Biomedical research requires healthcare institutions to provide sensitive clinical data to leverage data science and artificial intelligence technologies. However, providing healthcare data to researchers simple and secure, proves to be challenging for healthcare institutions.

OBJECTIVE

We describe and introduce Medical-Blocks, a platform for data exploration, data management, data analysis, and data sharing in biomedical research.

METHODS

The specification requirements for Medical-Blocks included: i) Connection to data sources of healthcare institutions with an interface for data exploration, ii) management of data in an internal file storage system, iii) data analysis through visualization and classification of data, and iv) data sharing via a file hosting service for collaboration. Medical-Blocks should be simple to use via a web-based user interface and extensible with new functionalities by a modular design via microservices ("blocks"). The scalability of the platform should be ensured by containerization. Security and legal regulations were considered during the development.

RESULTS

Medical-Blocks is a web application that runs in the cloud or as a local instance at a healthcare institution. Local instances of Medical-Blocks access data sources such as electronic health records and picture archiving and communications system (PACS) at healthcare institutions. Researchers and clinicians can explore, manage, and analyze the available data through Medical-Blocks. The data analysis involves classification of data for metadata extraction and the formation of cohorts. In collaborations, metadata (e.g., number of patients per cohort) and/or the data itself can be shared through Medical-Blocks locally or via a cloud instance to other researchers and clinicians.

CONCLUSIONS

Medical-Blocks facilitates biomedical research by providing a centralized platform to interact with medical data in collaborative research projects. The access to and management of medical data is simplified. Data can be swiftly analyzed to form cohorts for research and be shared among researchers. The modularity of Medical-Blocks makes the platform feasible for biomedical research where heterogenous medical data is needed.

CLINICALTRIAL

Haben Patient*innen die moralische Pflicht, ihre klinischen Daten für Forschung bereitzustellen? Eine kritische Prüfung möglicher Gründe

Die Sekundärnutzung klinischer Daten für Forschungs- und Lernaktivitäten hat das Potenzial, medizinisches Wissen und klinische Versorgung erheblich zu verbessern. Zur Realisierung dieses Potenzials bedarf es einer ethischen und rechtlichen Grundlage für die Datennutzung, vorzugsweise in Form der Einwilligung von Patient*innen. Damit stellt sich die grundsätzliche Frage: Haben Patient*innen eine moralische Pflicht, ihre klinischen Daten für Forschungs- und Lernaktivitäten zur Verfügung zu stellen?

Auf Basis eines ethischen Ansatzes, der als „sorgender Liberalismus“ bezeichnet werden kann, werden folgende Argumente zur Begründung einer Pflicht von Patient*innen zur Bereitstellung ihrer klinischen Daten für Forschungs- und Lernaktivitäten auf Plausibilität und moralisches Gewicht untersucht: die allgemeine Hilfspflicht; Solidarität; die Pflicht zu gemeinwohlförderlichem Handeln; das Trittbrettfahrerargument; transgenerationale Gerechtigkeit; das Prinzip des Zurückgebens; das Prinzip des Nicht-Schädigens; die Forschungsfreiheit und der Wert der Wissenschaft.

Die allgemeine Hilfspflicht und die Pflicht zu gemeinwohlförderlichem Handeln sind gewichtige Gründe für eine moralische Pflicht von Patient*innen zur Bereitstellung ihrer klinischen Daten für Forschungs- und Lernaktivitäten. Das Argument der transgenerationalen Gerechtigkeit und das Prinzip des Zurückgebens sind ethisch schwache Gründe für eine solche Pflicht, können jedoch eine motivationale Rolle spielen. Die anderen Gründe sind nicht geeignet, eine Pflicht zu begründen. Das Ergebnis ist in mehrfacher Hinsicht relevant: für Patient*innen, die um die Einwilligung in die Sekundärnutzung ihrer klinischen Daten gebeten werden; für die ethische Diskussion der Frage, ob und inwieweit Abstriche von der klassischen spezifischen Einwilligung unter bestimmten Bedingungen ethisch akzeptabel sind; für die rechtwissenschaftliche Diskussion der Bedingungen für eine juristisch verhältnismäßige Sekundärnutzung klinischer Daten für Forschungs- und Lernaktivitäten.

Streamlining statistical reproducibility: NHLBI ORCHID clinical trial results reproduction

Reproducibility in medical research has been a long-standing issue. More recently, the COVID-19 pandemic has publicly underlined this fact as the retraction of several studies reached out to general media audiences. A significant number of these retractions occurred after in-depth scrutiny of the methodology and results by the scientific community. Consequently, these retractions have undermined confidence in the peer-review process, which is not considered sufficiently reliable to generate trust in the published results. This partly stems from opacity in published results, the practical implementation of the statistical analysis often remaining undisclosed. We present a workflow that uses a combination of informatics tools to foster statistical reproducibility: an open-source programming language, Jupyter Notebook, cloud-based data repository, and an application programming interface can streamline an analysis and help to kick-start new analyses. We illustrate this principle by (1) reproducing the results of the ORCHID clinical trial, which evaluated the efficacy of hydroxychloroquine in COVID-19 patients, and (2) expanding on the analyses conducted in the original trial by investigating the association of premedication with biological laboratory results. Such workflows will be encouraged for future publications from National Heart, Lung, and Blood Institute-funded studies.

The COVID-19 pandemic has seen several articles published in high-profile journals being retracted. These retractions undermined even more confidence in the peer-review process, which is not considered sufficiently reliable to generate trust in the published results. A significant number of these retractions occurred after in-depth scrutiny of the methodology and results by the scientific community. This partly stems from opacity in published results, the practical implementation of the statistical analysis often remaining undisclosed. This article presents a simple workflow that leverages a combination of preexisting and newly developed biomedical informatics tools to promote transparent statistical analysis in biomedical research, which relies on the National Heart, Lung, and Blood Institute (NHLBI) BioData Catalyst platform. By streamlining access to data and analysis source code, it eases results reproduction and accelerates supplemental analyses. Such workflows will be encouraged for future publications from NHLBI-funded studies. We illustrate it by reproducing the results of the ORCHID clinical trial, which evaluated the efficacy of hydroxychloroquine in COVID-19 patients.

Collecting and sharing self-generated health and lifestyle data: Understanding barriers for people living with long-term health conditions - a survey study

Background

The growing popularity of collecting self-generated health and lifestyle data presents a valuable opportunity to develop our understanding of long-term health conditions and improve care. Barriers remain to the effective sharing of health and lifestyle data by those living with long-term health conditions which include beliefs around concepts of Trust, Identity, Privacy and Security, experiences of stigma, perceptions of risk and information sensitivity.

Method

We surveyed 250 UK adults who reported living with a range of long-term health conditions. We recorded data to assess self-reported behaviours, experiences, attitudes and motivations relevant to sharing self-generated health and lifestyle data. We also asked participants about their beliefs about Trust, Identity, Privacy and Security, stigma, and perceptions of risk and information sensitivity regarding their health and lifestyle data.

Results

Three-quarters of our sample reported recording information about their health and lifestyle on a daily basis. However, two-thirds reported never or rarely sharing this information with others. Trust, Identity, Privacy and Security concerns were considered to be ‘very important’ by those with long-term health conditions when deciding whether or not to share self-generated health and lifestyle data with others, with security concerns considered most important. Of those living with a long-term health condition, 58% reported experiencing stigma associated with their condition. The greatest perceived risk from sharing with others was the potential for future harm to their social relationships.

Conclusions

Our findings suggest that, in order for health professionals and researchers to benefit from the increased prevalence of self-generated health and lifestyle data, more can be done to address security concerns and to understand perceived risks associated with data sharing. Digital platforms aimed at facilitating the sharing of self-generated health and lifestyle data may look to highlight security features, enable users to control the sharing of certain information types, and emphasise the practical benefits to users of sharing health and lifestyle data with others.

Understanding the Barriers and Facilitators to Sharing Patient-Generated Health Data Using Digital Technology for People Living With Long-Term Health Conditions: A Narrative Review

Using digital technology to share patient-generated health data has the potential to improve the self-management of multiple long-term health conditions. Sharing these data can allow patients to receive additional support from healthcare professionals and peer communities, as well as enhance their understanding of their own health. A deeper understanding of the concerns raised by those living with long-term health conditions when considering whether to share health data via digital technology may help to facilitate effective data sharing practices in the future. The aim of this review is to identify whether trust, identity, privacy and security concerns present barriers to the successful sharing of patient-generated data using digital technology by those living with long-term health conditions. We also address the impact of stigma on concerns surrounding sharing health data with others. Searches of CINAHL, PsychInfo and Web of Knowledge were conducted in December 2019 and again in October 2020 producing 2,581 results. An iterative review process resulted in a final dataset of 23 peer-reviewed articles. A thorough analysis of the selected articles found that issues surrounding trust, identity, privacy and security clearly present barriers to the sharing of patient-generated data across multiple sharing contexts. The presence of enacted stigma also acts as a barrier to sharing across multiple settings. We found that the majority of literature focuses on clinical settings with relatively little attention being given to sharing with third parties. Finally, we suggest the need for more solution-based research to overcome the discussed barriers to sharing.

Health professionals' attitudes toward religiosity and spirituality: a NERSH Data Pool based on 23 surveys from six continents

Background

In order to facilitate better international and cross-cultural comparisons of health professionals (HPs) attitudes towards Religiosity and/or Spirituality (R/S) using individual participant data meta-analysis we updated the NERSH Data Pool.

Methods

We performed both a network search, a citation search and systematic literature searches to find new surveys.

Results

We found six new surveys (N=1,068), and the complete data pool ended up comprising 7,323 observations, including 4,070 females and 3,253 males. Most physicians (83%, N=3,700) believed that R/S had “some” influence on their patients’ health (CI95%) (81.8%–84.2%). Similarly, nurses (94%, N=1,020) shared such a belief (92.5%–95.5%). Across all samples 649 (16%; 14.9%–17.1%) physicians reported to have undergone formal R/S-training, compared with nurses where this was 264 (23%; 20.6%–25.4%).

Conclusions

Preliminary analysis indicates that HPs believe R/S to be important for patient health but lack formal R/S-training. Findings are discussed. We find the data pool suitable as a base for future cross-cultural comparisons using individual participant data meta-analysis.

An active human role is essential in big data-led decisions and data-intensive science

Big data is transforming many sectors, with far-reaching consequences to how decisions are made and how knowledge is produced and shared. In the current move toward more data-led decisions and data-intensive science, we aim here to examine three issues that are changing the way data are read and used. First, there is a shift toward paradigms that involve a large amount of data. In such paradigms, the creation of complex data-led models becomes tractable and appealing to generate predictions and explanations. This necessitates for instance a rethinking of Occam's razor principle in the context of knowledge discovery. Second, there is a growing erosion of the human role in decision making and knowledge discovery processes. Human users' involvement is decreasing at an alarming rate, with no say on how to read, process, and summarize data. This makes legal responsibility and accountability hard to define. Third, thanks to its increasing popularity, big data is gaining a seductive allure, where volume and complexity of big data can de facto confer more persuasion and significance to knowledge or decisions that result from big-data-based processes. These issues call for an active human role by creating opportunities to incorporate, in the most unbiased way, human expertise and prior knowledge in decision making and knowledge production. This also requires putting in place robust monitoring and appraisal mechanisms to ensure that relevant data is answering the right questions. As the proliferation of data continues to grow, we need to rethink the way we interact with data to serve human needs.

Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption

The assessment of health and disease requires a set of criteria to define health status and progression. These health measures are referred to as "endpoints." A "digital endpoint" is defined by its use of sensor-generated data often collected outside of a clinical setting such as in a patient's free-living environment. Applicable sensors exist in an array of devices and can be applied in a diverse set of contexts. For example, a smartphone's microphone might be used to diagnose or predict mild cognitive impairment due to Alzheimer's disease or a wrist-worn activity monitor (such as those found in smartwatches) may be used to measure a drug's effect on the nocturnal activity of patients with sickle cell disease. Digital endpoints are generating considerable excitement because they permit a more authentic assessment of the patient's experience, reveal formerly untold realities of disease burden, and can cut drug discovery costs in half. However, before these benefits can be realized, effort must be applied not only to the technical creation of digital endpoints but also to the environment that allows for their development and application. The future of digital endpoints rests on meaningful interdisciplinary collaboration, sufficient evidence that digital endpoints can realize their promise, and the development of an ecosystem in which the vast quantities of data that digital endpoints generate can be analyzed. The fundamental nature of health care is changing. With coronavirus disease 2019 serving as a catalyst, there has been a rapid expansion of home care models, telehealth, and remote patient monitoring. The increasing adoption of these health-care innovations will expedite the requirement for a digital characterization of clinical status as current assessment tools often rely upon direct interaction with patients and thus are not fit for purpose to be administered remotely. With the ubiquity of relatively inexpensive sensors, digital endpoints are positioned to drive this consequential change. It is therefore not surprising that regulators, physicians, researchers, and consultants have each offered their assessment of these novel tools. However, as we further describe later, the broad adoption of digital endpoints will require a cooperative effort. In this article, we present an analysis of the current state of digital endpoints. We also attempt to unify the perspectives of the parties involved in the development and deployment of these tools. We conclude with an interdependent list of challenges that must be collaboratively addressed before these endpoints are widely adopted.

Individual notions of fair data sharing from the perspectives of Swiss stakeholders

Background

The meaningful sharing of health data between different stakeholders is central to the advancement of science and to improve care offered to individual patients. However, it is important that the interests of individual stakeholders involved in this data sharing ecosystem are taken into account to ensure fair data sharing practices. In this regard, this qualitative study investigates such practices from the perspectives of a subset of relevant Swiss expert stakeholders, using a distributive justice lens.

Methods

Using purposive and snowball sampling methodologies, 48 expert stakeholders from the Swiss healthcare and research domains were recruited for semi-structured interviews. After the experts had consented, the interviews were audio-recorded and transcribed verbatim, but omitting identifying information to ensure confidentiality and anonymity. A thematic analysis using a deductive approach was conducted to identify fair data sharing practices for secondary research purposes. Themes and subthemes were then identified and developed during the analysis.

Results

Three distributive justice themes were identified in the data sharing negotiation processes, and these are: (i) effort, which was subcategorized into two subthemes (i.e. a claim to data reciprocity and other reciprocal advantages, and a claim to transparency on data re-use), (ii) compensation, which was subcategorized into two subthemes (i.e. a claim to an academic compensation and a claim to a financial compensation), and lastly, (iii) contribution, i.e. the significance of data contributions should be matched with a corresponding reward.

Conclusions

This qualitative study provides insights, which could inform policy-making on claims and incentives that encourage Swiss expert stakeholders to share their datasets. Importantly, several claims have been identified and justified under the basis of distributive justice principles, whilst some are more debatable and likely insufficient in justifying data sharing activities. Nonetheless, these claims should be taken seriously and discussed more broadly. Indeed, promoting health research while ensuring that healthcare systems guarantee better services, it is paramount to ensure that solutions developed are sustainable, provide fair criteria for academic careers and promote the sharing of high quality data to advance science.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06906-2.

Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

BACKGROUND

Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches.

METHODS

The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes.

RESULTS

Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility.

CONCLUSIONS

There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.

Longitudinal Health Studies: Secondary Uses Serving the Future

Our research compares the ethical and institutional conditions that govern the sharing and secondary use of longitudinal population health data from multiple cohorts. The data use and data sharing conditions applicable to 27 population health cohorts were assessed. This assessment was performed in consulting the informed consent materials and institutional policies applicable to the use of data. Descriptions drawn from the research ethics consent materials were refined through dialog with the institutional staff responsible for overseeing access to data, where possible. Our results demonstrate that data of longitudinal population health cohorts assessed can generally be shared and used for secondary purposes. However, the purposes of secondary use and the preconditions applicable thereto are highly variable. Heterogeneous use conditions can also impede the storage of legacy research data and the pooling thereof for the purpose of common reuse.

Developing a Transnational Health Record Framework with Level-Specific Interoperability Guidelines Based on a Related Literature Review

With the advent of digital healthcare without borders, enormous amounts of health information are captured and computerized. As healthcare quality largely depends on the reliability of given health information, personal health records should be accessible according to patients’ mobility, even as they travel or migrate to other countries. However, since all the health information is scattered in multiple places, it is an onerous task to carry it whenever people move to other countries. To effectively and efficiently utilize health information, interoperability, which is the ability of various healthcare information technologies to exchange, to interpret, and to use data, is needed. Hence, building a robust transnational health information infrastructure with clear interoperability guidelines considering heterogeneous aspects is necessary. For this purpose, this study proposes a Transnational Health Record framework, which enables access to personal health records anywhere. We review related literature and define level-specific interoperability guidelines, business processes, and requirements for the Transnational Health Record system framework.