Data sharing during COVID-19 pandemic: what to take away

INTRODUCTION

In light of the viral outbreak of SARS-CoV-2 that monopolized the focus of the scientific community and general public alike for the past 6 months, one of the greatest contributors in the battle against this pandemic was the international sharing of information. Whether regarding the viral genome, incubation periods, method of transmission, symptoms, dangerous behaviors, age groups at risk, all information was valuable, all data was shared as soon as possible.

AREAS COVERED

Considering that the most severely impacted group of patients are already suffering from other conditions, accessing the impact that metabolic associated fatty liver disease (MAFLD), obesity, and diabetes has on patients by sharing information between different healthcare facilities is of vital importance. However, the value behind open information sharing would remain significant even without a viral outbreak and should there be a more efficient infrastructure in place, the global exchange of data can become more practical and less arduous.

EXPERT OPINION

Since the sharing of data by individual researchers is often motivated by personal benefits, this observed international collaboration is conditional at best, and the widespread misinformation during this pandemic could be an indication of a certain lack of consensus within the scientific community itself.

Perceived Risks, Benefits, and Interest in Participating in Environmental Health Studies That Share Personal Exposure Data: A U.S. Survey of Prospective Participants

Little is known about the willingness of prospective study participants to share environmental health data. To fill this gap, we conducted a hypothetical vignette survey among 1,575 women who have volunteered to be contacted about breast cancer studies. Eighty-three percent were interested in participating in the environmental studies, with little difference whether data were restricted to the research team, shared with approved researchers, or publicly accessible. However, participants somewhat preferred controlled access for children's data. Respondents were more interested in studies with environmental rather than biological samples and more interested when researchers would return personal results, a practice of increasing importance. They were more reluctant to share location or to participate if studies involved electronic medical records. Many expressed concerns about privacy, particularly security breaches, but reidentification risks were mentioned infrequently, indicating that this topic should be discussed during informed consent.

Advanced Health Information Technologies to Engage Parents, Clinicians, and Community Nutritionists in Coordinating Responsive Parenting Care: Descriptive Case Series of the Women, Infants, and Children Enhancements to Early Healthy Lifestyles for Baby (WEE Baby) Care Randomized Controlled Trial

BACKGROUND

Socioeconomically disadvantaged newborns receive care from primary care providers (PCPs) and Women, Infants, and Children (WIC) nutritionists. However, care is not coordinated between these settings, which can result in conflicting messages. Stakeholders support an integrated approach that coordinates services between settings with care tailored to patient-centered needs.

OBJECTIVE

This analysis describes the usability of advanced health information technologies aiming to engage parents in self-reporting parenting practices, integrate data into electronic health records to inform and facilitate documentation of provided responsive parenting (RP) care, and share data between settings to create opportunities to coordinate care between PCPs and WIC nutritionists.

METHODS

Parents and newborns (dyads) who were eligible for WIC care and received pediatric care in a single health system were recruited and randomized to a RP intervention or control group. For the 6-month intervention, electronic systems were created to facilitate documentation, data sharing, and coordination of provided RP care. Prior to PCP visits, parents were prompted to respond to the Early Healthy Lifestyles (EHL) self-assessment tool to capture current RP practices. Responses were integrated into the electronic health record and shared with WIC. Documentation of RP care and an 80-character, free-text comment were shared between WIC and PCPs. A care coordination opportunity existed when the dyad attended a WIC visit and these data were available from the PCP, and vice versa. Care coordination was demonstrated when WIC or PCPs interacted with data and documented RP care provided at the visit.

RESULTS

Dyads (N=131) attended 459 PCP (3.5, SD 1.0 per dyad) and 296 WIC (2.3, SD 1.0 per dyad) visits. Parents completed the EHL tool prior to 53.2% (244/459) of PCP visits (1.9, SD 1.2 per dyad), PCPs documented provided RP care at 35.3% (162/459) of visits, and data were shared with WIC following 100% (459/459) of PCP visits. A WIC visit followed a PCP visit 50.3% (231/459) of the time; thus, there were 1.8 (SD 0.8 per dyad) PCP to WIC care coordination opportunities. WIC coordinated care by documenting RP care at 66.7% (154/231) of opportunities (1.2, SD 0.9 per dyad). WIC visits were followed by a PCP visit 58.9% (116/197) of the time; thus, there were 0.9 (SD 0.8 per dyad) WIC to PCP care coordination opportunities. PCPs coordinated care by documenting RP care at 44.0% (51/116) of opportunities (0.4, SD 0.6 per dyad).

CONCLUSIONS

Results support the usability of advanced health information technology strategies to collect patient-reported data and share these data between multiple providers. Although PCPs and WIC shared data, WIC nutritionists were more likely to use data and document RP care to coordinate care than PCPs. Variability in timing, sequence, and frequency of visits underscores the need for flexibility in pragmatic studies.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03482908; https://clinicaltrials.gov/ct2/show/NCT03482908.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s12887-018-1263-z.

CNGBdb: China National GeneBank DataBase

China National GeneBank DataBase (CNGBdb) is a data platform aiming to systematically archiving and sharing of multi-omics data in life science. As the service portal of Bio-informatics Data Center of the core structure, namely, "Three Banks and Two Platforms" of China National GeneBank (CNGB), CNGBdb has the advantages of rich sample resources, data resources, cooperation projects, powerful data computation and analysis capabilities. With the advent of high throughput sequencing technologies, research in life science has entered the big data era, which is in the need of closer international cooperation and data sharing. With the development of China's economy and the increase of investment in life science research, we need to establish a national public platform for data archiving and sharing in life science to promote the systematic management, application and industrial utilization. Currently, CNGBdb can provide genomic data archiving, information search engines, data management and data analysis services. The data schema of CNGBdb has covered projects, samples, experiments, runs, assemblies, variations and sequences. Until May 22, 2020, CNGBdb has archived 2176 research projects and more than 2221 TB sequencing data submitted by researchers globally. In the future, CNGBdb will continue to be dedicated to promoting data sharing in life science research and improving the service capability. CNGBdb website is: https://db.cngb.org/.

Expanding and Remixing the Metadata Landscape

Genomic data sharing accelerates research. Data are most valuable when they are accompanied by detailed metadata. To date, metadata are often human-annotated descriptions of samples and their handling. We discuss how machine learning-derived elements complement such descriptions to enhance the research ecosystem around genomic data.

Journal policies and editors' opinions on peer review

Peer review practices differ substantially between journals and disciplines. This study presents the results of a survey of 322 editors of journals in ecology, economics, medicine, physics and psychology. We found that 49% of the journals surveyed checked all manuscripts for plagiarism, that 61% allowed authors to recommend both for and against specific reviewers, and that less than 6% used a form of open peer review. Most journals did not have an official policy on altering reports from reviewers, but 91% of editors identified at least one situation in which it was appropriate for an editor to alter a report. Editors were also asked for their views on five issues related to publication ethics. A majority expressed support for co-reviewing, reviewers requesting access to data, reviewers recommending citations to their work, editors publishing in their own journals, and replication studies. Our results provide a window into what is largely an opaque aspect of the scientific process. We hope the findings will inform the debate about the role and transparency of peer review in scholarly publishing.

Neuronize v2: Bridging the Gap Between Existing Proprietary Tools to Optimize Neuroscientific Workflows

Knowledge about neuron morphology is key to understanding brain structure and function. There are a variety of software tools that are used to segment and trace the neuron morphology. However, these tools usually utilize proprietary formats. This causes interoperability problems since the information extracted with one tool cannot be used in other tools. This article aims to improve neuronal reconstruction workflows by facilitating the interoperability between two of the most commonly used software tools—Neurolucida (NL) and Imaris (Filament Tracer). The new functionality has been included in an existing tool—Neuronize—giving rise to its second version. Neuronize v2 makes it possible to automatically use the data extracted with Imaris Filament Tracer to generate a tracing with dendritic spine information that can be read directly by NL. It also includes some other new features, such as the ability to unify and/or correct inaccurately-formed meshes (i.e., dendritic spines) and to calculate new metrics. This tool greatly facilitates the process of neuronal reconstruction, bridging the gap between existing proprietary tools to optimize neuroscientific workflows.

Improved Criteria for the Classification of Titin Variants in Inherited Skeletal Myopathies

BACKGROUND

Extensive genetic screening results in the identification of thousands of rare variants that are difficult to interpret. Because of its sheer size, rare variants in the titin gene (TTN) are detected frequently in any individual. Unambiguous interpretation of molecular findings is almost impossible in many patients with myopathies or cardiomyopathies.

OBJECTIVE

To refine the current classification framework for TTN-associated skeletal muscle disorders and standardize the interpretation of TTN variants.

METHODS

We used the guidelines issued by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) to re-analyze TTN genetic findings from our patient cohort.

RESULTS

We identified in the classification guidelines three rules that are not applicable to titin-related skeletal muscle disorders; six rules that require disease-/gene-specific adjustments and four rules requiring quantitative thresholds for a proper use. In three cases, the rule strength need to be modified.

CONCLUSIONS

We suggest adjustments are made to the guidelines. We provide frequency thresholds to facilitate filtering of candidate causative variants and guidance for the use and interpretation of functional data and co-segregation evidence. We expect that the variant classification framework for TTN-related skeletal muscle disorders will be further improved along with a better understanding of these diseases.

Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation

BACKGROUND

There has been growing interest in data synthesis for enabling the sharing of data for secondary analysis; however, there is a need for a comprehensive privacy risk model for fully synthetic data: If the generative models have been overfit, then it is possible to identify individuals from synthetic data and learn something new about them.

OBJECTIVE

The purpose of this study is to develop and apply a methodology for evaluating the identity disclosure risks of fully synthetic data.

METHODS

A full risk model is presented, which evaluates both identity disclosure and the ability of an adversary to learn something new if there is a match between a synthetic record and a real person. We term this "meaningful identity disclosure risk." The model is applied on samples from the Washington State Hospital discharge database (2007) and the Canadian COVID-19 cases database. Both of these datasets were synthesized using a sequential decision tree process commonly used to synthesize health and social science data.

RESULTS

The meaningful identity disclosure risk for both of these synthesized samples was below the commonly used 0.09 risk threshold (0.0198 and 0.0086, respectively), and 4 times and 5 times lower than the risk values for the original datasets, respectively.

CONCLUSIONS

We have presented a comprehensive identity disclosure risk model for fully synthetic data. The results for this synthesis method on 2 datasets demonstrate that synthesis can reduce meaningful identity disclosure risks considerably. The risk model can be applied in the future to evaluate the privacy of fully synthetic data.

New frontiers in translational research: Touchscreens, open science, and the mouse translational research accelerator platform

Many neurodegenerative and neuropsychiatric diseases and other brain disorders are accompanied by impairments in high-level cognitive functions including memory, attention, motivation, and decision-making. Despite several decades of extensive research, neuroscience is little closer to discovering new treatments. Key impediments include the absence of validated and robust cognitive assessment tools for facilitating translation from animal models to humans. In this review, we describe a state-of-the-art platform poised to overcome these impediments and improve the success of translational research, the Mouse Translational Research Accelerator Platform (MouseTRAP), which is centered on the touchscreen cognitive testing system for rodents. It integrates touchscreen-based tests of high-level cognitive assessment with state-of-the art neurotechnology to record and manipulate molecular and circuit level activity in vivo in animal models during human-relevant cognitive performance. The platform also is integrated with two Open Science platforms designed to facilitate knowledge and data-sharing practices within the rodent touchscreen community, touchscreencognition.org and mousebytes.ca. Touchscreencognition.org includes the Wall, showcasing touchscreen news and publications, the Forum, for community discussion, and Training, which includes courses, videos, SOPs, and symposia. To get started, interested researchers simply create user accounts. We describe the origins of the touchscreen testing system, the novel lines of research it has facilitated, and its increasingly widespread use in translational research, which is attributable in part to knowledge-sharing efforts over the past decade. We then identify the unique features of MouseTRAP that stand to potentially revolutionize translational research, and describe new initiatives to partner with similar platforms such as McGill's M3 platform (m3platform.org).

Canadian Association of Radiologists White Paper on De-Identification of Medical Imaging: Part 1, General Principles

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 1 of this article will inform CAR members on principles of de-identification, pseudonymization, encryption, direct and indirect identifiers, k-anonymization, risks of reidentification, implementations, data set release models, and validation of AI algorithms, with a view to developing appropriate standards to safeguard patient information effectively.

Canadian Association of Radiologists White Paper on De-identification of Medical Imaging: Part 2, Practical Considerations

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 2 of this article will inform CAR members on the practical aspects of medical imaging de-identification, strengths and limitations of de-identification approaches, list of de-identification software and tools available, and perspectives on future directions.

Linking the Declarations of Helsinki and of Taipei: Critical Challenges of Future-Oriented Research Ethics

Expansion of data-driven research in the 21st century has posed challenges in the evolution of the international agreed framework of research ethics. The World Medical Association (WMA)'s Declaration of Helsinki (DoH) has provided ethical principles for medical research involving humans since 1964, with the last update in 2013. To complement the DoH, WMA issued the Declaration of Taipei (DoT) in 2016 to provide additional principles for health databases and biobanks. However, the ethical principles for secondary use of data or material obtained in research remain unclear. With such a perspective, the Working Group on Ethics (WGE) of the International Federation of Associations of Pharmaceutical Physicians and Pharmaceutical Medicine (IFAPP) suggests a closer scientific linkage in the DoH to the (Declaration of Taipei) DoT focusing specifically on areas that will facilitate data-driven research, and to further strengthen the protection of research participants.

Privacy Perceptions and Concerns in Image-Based Dietary Assessment Systems: Questionnaire-Based Study

BACKGROUND

Complying with individual privacy perceptions is essential when processing personal information for research. Our specific research area is performance development of elite athletes, wherein nutritional aspects are important. Before adopting new automated tools that capture such data, it is crucial to understand and address the privacy concerns of the research subjects that are to be studied. Privacy as contextual integrity emphasizes understanding contextual sensitivity in an information flow. In this study, we explore privacy perceptions in image-based dietary assessments. This research field lacks empirical evidence on what will be considered as privacy violations when exploring trends in long-running studies. Prior studies have only classified images as either private or public depending on their basic content. An assessment and analysis are thus needed to prevent unwanted consequences of privacy breach and other issues perceived as sensitive when designing systems for dietary assessment by using food images.

OBJECTIVE

The aim of this study was to investigate common perceptions of computer systems using food images for dietary assessment. The study delves into perceived risks and data-sharing behaviors.

METHODS

We investigated the privacy perceptions of 105 individuals by using a web-based survey. We analyzed these perceptions along with perceived risks in sharing dietary information with third parties.

RESULTS

We found that understanding the motive behind the use of data increases its chances of sharing with a social group.

CONCLUSIONS

In this study, we highlight various privacy concerns that can be addressed during the design phase. A system design that is compliant with general data protection regulations will increase participants' and stakeholders' trust in an image-based dietary assessment system. Innovative solutions are needed to reduce the intrusiveness of a continuous assessment. Individuals show varying behaviors for sharing metadata, as knowing what the data is being used for, increases the chance of it being shared.

IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution

The novel coronavirus (COVID-19), declared by the World Health Organization (WHO) as a global pandemic, has brought with it changes to the general way of life. Major sectors of the world industry and economy have been affected and the Internet of Things (IoT) management and framework is no exception in this regard. This article provides an up to date survey on how a global pandemic such as COVID-19 has affected the world of IoT technologies. It looks at the contributions that IoT and associated sensor technologies have made towards virus tracing, tracking and spread mitigation. The associated challenges of deployment of sensor hardware in the face of a rapidly spreading pandemic have been looked into as part of this review article. The effects of a global pandemic on the evolution of IoT architectures and management have also been addressed, leading to the likely outcomes on future IoT implementations. In general, this article provides an insight into the advancement of sensor-based E-health towards the management of global pandemics. It also answers the question of how a global virus pandemic has shaped the future of IoT networks.

A deliberative study of public attitudes towards sharing genomic data within NHS genomic medicine services in England

Whole genome (DNA) sequencing is becoming part of routine care healthcare in England. Genomic data are most useful when pooled with other patients' data, meaning that clinicians may need to share data to effectively treat patients. We ran deliberative focus groups to explore views among 44 patients and members of the public about proposals for wider genomic data sharing for clinical care. Participants were briefed about genomic medicine and engaged in group and individual exercises to deliberate on the benefits and risks of using genomic data. Findings showed that participants supported wider sharing of genomic data within health services and naturally linked care and research activities. Nonetheless, they were concerned about managing flows of information to protect patient confidentiality and guard against unauthorised uses, now and over the long-term. Ongoing conversations with the public are needed to determine appropriate uses of genomic data and safeguards to inform service development.

Global Public Perceptions of Genomic Data Sharing: What Shapes the Willingness to Donate DNA and Health Data?

Analyzing genomic data across populations is central to understanding the role of genetic factors in health and disease. Successful data sharing relies on public support, which requires attention to whether people around the world are willing to donate their data that are then subsequently shared with others for research. However, studies of such public perceptions are geographically limited and do not enable comparison. This paper presents results from a very large public survey on attitudes toward genomic data sharing. Data from 36,268 individuals across 22 countries (gathered in 15 languages) are presented. In general, publics across the world do not appear to be aware of, nor familiar with, the concepts of DNA, genetics, and genomics. Willingness to donate one's DNA and health data for research is relatively low, and trust in the process of data's being shared with multiple users (e.g., doctors, researchers, governments) is also low. Participants were most willing to donate DNA or health information for research when the recipient was specified as a medical doctor and least willing to donate when the recipient was a for-profit researcher. Those who were familiar with genetics and who were trusting of the users asking for data were more likely to be willing to donate. However, less than half of participants trusted more than one potential user of data, although this varied across countries. Genetic information was not uniformly seen as different from other forms of health information, but there was an association between seeing genetic information as special in some way compared to other health data and increased willingness to donate. The global perspective provided by our "Your DNA, Your Say" study is valuable for informing the development of international policy and practice for sharing genomic data. It highlights that the research community not only needs to be worthy of trust by the public, but also urgent steps need to be taken to authentically communicate why genomic research is necessary and how data donation, and subsequent sharing, is integral to this.

The Zika Virus Individual Participant Data Consortium: A Global Initiative to Estimate the Effects of Exposure to Zika Virus during Pregnancy on Adverse Fetal, Infant, and Child Health Outcomes

This commentary describes the creation of the Zika Virus Individual Participant Data Consortium, a global collaboration to address outstanding questions in Zika virus (ZIKV) epidemiology through conducting an individual participant data meta-analysis (IPD-MA). The aims of the IPD-MA are to (1) estimate the absolute and relative risks of miscarriage, fetal loss, and short- and long-term sequelae of fetal exposure; (2) identify and quantify the relative importance of different sources of heterogeneity (e.g., immune profiles, concurrent flavivirus infection) for the risk of adverse fetal, infant, and child outcomes among infants exposed to ZIKV in utero; and (3) develop and validate a prognostic model for the early identification of high-risk pregnancies and inform communication between health care providers and their patients and public health interventions (e.g., vector control strategies, antenatal care, and family planning programs). By leveraging data from a diversity of populations across the world, the IPD-MA will provide a more precise estimate of the risk of adverse ZIKV-related outcomes within clinically relevant subgroups and a quantitative assessment of the generalizability of these estimates across populations and settings. The ZIKV IPD Consortium effort is indicative of the growing recognition that data sharing is a central component of global health security and outbreak response.

Ethical, Legal, and Social Issues (ELSI) of Responsible Data Sharing Involving Children in Genomics: A Systematic Literature Review of Reasons

BACKGROUND

Progress in precision medicine relies on the access to, use of, and exchange of genomic and associated clinical data, including from children. The ethical, legal, and social issues (ELSI) of such data access, use, and exchange may be accentuated in the pediatric context due in part to the highly sensitive nature of genomic data, children's consent-related vulnerabilities, and uncertain risks of reidentification. Systematic analyses of the ELSI and scientific reasons for why and how genomic data may be shared responsibly are, however, limited. Methods: We conducted a modified systematic review of reasons according to Sofaer and Strech to examine the ELSI and scientific reasons for "responsible" sharing of children's genomic and associated clinical data. Empirical articles, commentaries, and data-sharing policies indexed in Medline, Scopus, Web of Science, and BIOSIS were included in the analysis if they discussed ELSI and were published between 2003 and 2017 in English. Results: One hundred and fifty-one records met our inclusion criteria. We identified 11 unique reasons and 8 subreasons for why children's genomic data should or should not be shared. Enhancing the prospect of direct and indirect benefits and maximizing the utility of children's data were top reasons why data should be shared. Inadequate data privacy protection was the leading reason why it should not. We furthermore identified 8 reasons and 30 subreasons that support conditional data sharing, in which recontact for the continued use of children's data once they reach the age of majority was the most frequently endorsed condition. Conclusions: The complete list of ELSI reasons and responsible conditions provides an evidentiary basis upon which institutions can develop data-sharing policies. Institutions should encourage the sharing of children's data to advance genomic research, while heeding special reconsent and data protection mechanisms that may help mitigate uncertain longitudinal risks for children and families.

Knowledge and Practice of Patients' Data Sharing and Confidentiality Among Nurses in Jordan

BACKGROUND

The key patient rights entail respecting human decency, receiving healthcare services of high-quality, the right to information, the initial agreement of the patient to medical intervention, respecting privacy and personal life, and sustaining care and treatment. This study aims to survey the knowledge and practice of nurses in various healthcare industries toward sharing and confidentiality of patients' data.

METHODS

A descriptive cross-sectional design was employed through an online survey from May to June 2020. The authors sent a developed tool containing 19 statements reflecting the understanding of nurses' knowledge and practice of privacy and sharing of data required to safeguard patient privacy. A total of 800 nurses agreed to participate in the study out of 1000 nurses.

RESULTS

Roughly, all participants agreed that junior nurses should participate in a data sharing and confidentiality course before engaging in practice. Regarding institution policies for data sharing and protection, many nurses agreed that there are special recommendations and instructions from the institution in which they work to exchange patient information among nurses and the medical staff. The predictors of sharing practices and confidentiality among nurses include age, gender, marriage status, and attending a security course before practice. Young age, female, not attending a data sharing course, and single nurses are less engaging with data sharing and confidentiality of the patients for unauthorized patients.

CONCLUSION

A significant proportion of the staff had appropriate practices that ensured data security. However, practices that ensure patient confidentiality in the aspects of access, sharing, and transferring of patient data need improvement. Training is essential since it will have a beneficial relationship with knowledge, opinions, views, and actions. Thus, planning continuous training on policies and regulations about data safety and privacy may assist in improving healthcare setting practices.

High-quality cardiac surgery through teamwork

The high-quality cardiothoracic surgery program is primed for mindful effective surgery. The challenge lies in attaining mindful skills and efficiency. Herein is one journey toward high departmental quality over two decades.

Assessing the feasibility of introducing an electronic health information system into Tuberculosis clinics and laboratories in Myanmar

Myanmar has launched an advanced tuberculosis examination policy, which involves specimen exchanges among clinics and referral laboratories. However, with the current paper-based operation, it is difficult to trace information accurately. Therefore, since April 2017, we introduced a pilot operation consisting of an electronic health information system (HIS) that uses QR codes for data sharing in the tuberculosis laboratory at seven facilities. This study aimed to assess the feasibility of introducing the electronic HIS into tuberculosis clinics and laboratories based on staff perception, workload and workflow, and data accuracy, and to clarify its advantages and disadvantages. The analysis was descriptive, and it involved a semi-structured interview for the staff, workflow observations to evaluate the workload and describe the change in workflow, and evaluation of the data accuracy by comparing the numbers yielded by the paper-based and HIS-based reports. The HIS was positively accepted as it improved work efficiency, while the operation still depended on paper-based reports. Parallel data registration using both paper-based and HIS-based reports increased the workload. Data discrepancies were found when comparing the paper-based and HIS-based reports, and these discrepancies were not directly attributed to the HIS introduction but individual factors. Crucial facilitating factors of the HIS were its operability and user-friendliness, because it does not require specific training. The additional workload translates into the need for additional human resources, and the parallel data registration remains a challenge. However, we consider that these challenges could be overcome as coverage of the HIS expands.

Profile of Data Sharing in the Clinical Neurosciences

Importance: In today’s climate of high healthcare costs and limited research resources, much attention has been given to inefficiency in research. Open access to research data has been proposed as a way to pool resources and make the most of research funding while also promoting transparency and scientific rigor. 

Objective: The clinical neurosciences stand to benefit greatly from the potential opportunities afforded by open data, and we sought to evaluate the current state of publicly available research findings and data sharing policies within the clinical neurosciences. 

Design: The Clarivate Analytics Web of Science journal citation reports for 2017 were used to sort journals in the category ‘Clinical Neurosciences’ by impact factor. The top 50 journals were selected and reviewed, but data was only collected from journals focused on original research (42/50). For each journal we reviewed the 10 most recent original research articles for 2016, 2017, and 2018 as designated by Scopus. 

Results: A data sharing policy existed for 60% (25/42) of the journals reviewed. Of the articles studied 41% (517/1255) contained source data, and the amount of articles with available source data increased from 2016 to 2018. Of all the articles reviewed, 49.4% (620/1255) were open access. Overall, 6.9% (87/1255) of articles had their source data accessible outside of the manuscript (e.g. registries, databases, etc.) and 8.9% (112/1255) addressed the availability of their source data within the publication itself. The availability of source data outside the manuscript and in-article discussion of source data availability both increased from 2016 to 2018. Only 3.9% (49/1255) of articles reviewed reported negative results for their primary outcome, and 7.6% (95/1255) of the articles could not be defined as primarily reporting positive or negative findings (characterization studies, census reporting, etc.). The distribution of negative versus positive results reported showed no significant trend over the years studied. 

Conclusion and Relevance: Our results demonstrate an opportunity for increased data sharing in neuroscience original research. These findings also suggest a trend towards increased adoption of open data sharing policies among journals and increased availability of unprocessed data in publications. This can increase the quality and speed at which new research is developed in the clinical neurosciences.

ACTION-EHR: Patient-Centric Blockchain-Based Electronic Health Record Data Management for Cancer Care

Background

With increased specialization of health care services and high levels of patient mobility, accessing health care services across multiple hospitals or clinics has become very common for diagnosis and treatment, particularly for patients with chronic diseases such as cancer. With informed knowledge of a patient’s history, physicians can make prompt clinical decisions for smarter, safer, and more efficient care. However, due to the privacy and high sensitivity of electronic health records (EHR), most EHR data sharing still happens through fax or mail due to the lack of systematic infrastructure support for secure, trustable health data sharing, which can also cause major delays in patient care.

Objective

Our goal was to develop a system that will facilitate secure, trustable management, sharing, and aggregation of EHR data. Our patient-centric system allows patients to manage their own health records across multiple hospitals. The system will ensure patient privacy protection and guarantee security with respect to the requirements for health care data management, including the access control policy specified by the patient.

Methods

We propose a permissioned blockchain-based system for EHR data sharing and integration. Each hospital will provide a blockchain node integrated with its own EHR system to form the blockchain network. A web-based interface will be used for patients and doctors to initiate EHR sharing transactions. We take a hybrid data management approach, where only management metadata will be stored on the chain. Actual EHR data, on the other hand, will be encrypted and stored off-chain in Health Insurance Portability and Accountability Act–compliant cloud-based storage. The system uses public key infrastructure–based asymmetric encryption and digital signatures to secure shared EHR data.

Results

In collaboration with Stony Brook University Hospital, we developed ACTION-EHR, a system for patient-centric, blockchain-based EHR data sharing and management for patient care, in particular radiation treatment for cancer. The prototype was built on Hyperledger Fabric, an open-source, permissioned blockchain framework. Data sharing transactions were implemented using chaincode and exposed as representational state transfer application programming interfaces used for the web portal for patients and users. The HL7 Fast Healthcare Interoperability Resources standard was adopted to represent shared EHR data, making it easy to interface with hospital EHR systems and integrate a patient’s EHR data. We tested the system in a distributed environment at Stony Brook University using deidentified patient data.

Conclusions

We studied and developed the critical technology components to enable patient-centric, blockchain-based EHR sharing to support cancer care. The prototype demonstrated the feasibility of our approach as well as some of the major challenges. The next step will be a pilot study with health care providers in both the United States and Switzerland. Our work provides an exemplar testbed to build next-generation EHR sharing infrastructures.