Genetic data and electronic health records: a discussion of ethical, logistical and technological considerations

Addressing the ethical and societal challenges posed by genome-wide association studies of behavioral and brain-related traits

Genome-wide association studies have led to the identification of robust statistical associations of genetic variants with numerous brain-related traits, including neurological and psychiatric conditions, and psychological and behavioral measures. These results may provide insight into the biology underlying these traits and may facilitate clinically useful predictions. However, these results also carry the risk of harm, including possible negative effects of inaccurate predictions, violations of privacy, stigma and genomic discrimination, raising serious ethical and legal implications. Here, we discuss ethical concerns surrounding the results of genome-wide association studies for individuals, society and researchers. Given the success of genome-wide association studies and the increasing availability of nonclinical genomic prediction technologies, better laws and guidelines are urgently needed to regulate the storage, processing and responsible use of genetic data. Also, researchers should be aware of possible misuse of their results, and we provide guidance to help avoid such negative impacts on individuals and society.

A Study on Epidemic Information Screening, Prevention and Control of Public Opinion Based on Health and Medical Big Data: A Case Study of COVID-19

The outbreak of the coronavirus disease 2019 (COVID-19) represents an alert for epidemic prevention and control in public health. Offline anti-epidemic work is the main battlefield of epidemic prevention and control. However, online epidemic information prevention and control cannot be ignored. The aim of this study was to identify reliable information sources and false epidemic information, as well as early warnings of public opinion about epidemic information that may affect social stability and endanger the people's lives and property. Based on the analysis of health and medical big data, epidemic information screening and public opinion prevention and control research were decomposed into two modules. Eight characteristics were extracted from the four levels of coarse granularity, fine granularity, emotional tendency, and publisher behavior, and another regulatory feature was added, to build a false epidemic information identification model. Five early warning indicators of public opinion were selected from the macro level and the micro level to construct the early warning model of public opinion about epidemic information. Finally, an empirical analysis on COVID-19 information was conducted using big data analysis technology.

Integration of Artificial Intelligence and Blockchain Technology in Healthcare and Agriculture

Over the last decade, the healthcare sector has accelerated its digitization and electronic health records (EHRs). As information technology progresses, the notion of intelligent health also gathers popularity. By combining technologies such as the internet of things (IoT) and artificial intelligence (AI), innovative healthcare modifies and enhances traditional medical systems in terms of efficiency, service, and personalization. On the other side, intelligent healthcare systems are incredibly vulnerable to data breaches and other malicious assaults. Recently, blockchain technology has emerged as a potentially transformative option for enhancing data management, access control, and integrity inside healthcare systems. Integrating these advanced approaches in agriculture is critical for managing food supply chains, drug supply chains, quality maintenance, and intelligent prediction. This study reviews the literature, formulates a research topic, and analyzes the applicability of blockchain to the agriculture/food industry and healthcare, with a particular emphasis on AI and IoT. This article summarizes research on the newest blockchain solutions paired with AI technologies for strengthening and inventing new technological standards for the healthcare ecosystems and food industry.

Integration of Biobanks in National eHealth Ecosystems Facilitating Long-Term Longitudinal Clinical-Omics Studies and Citizens' Engagement in Research Through eHealthBioR

Biobanks have long existed to support research activities with BBMRI-ERIC formed as a European research infrastructure supporting the coordination for biobanking with 20 country members and one international organization. Although the benefits of biobanks to the research community are well-established, the direct benefit to citizens is limited to the generic benefit of promoting future research. Furthermore, the advent of General Data Protection Regulation (GDPR) legislation raised a series of challenges for scientific research especially related to biobanking associate activities and longitudinal research studies. Electronic health record (EHR) registries have long existed in healthcare providers. In some countries, even at the national level, these record the state of the health of citizens through time for the purposes of healthcare and data portability between different providers. The potential of EHRs in research is great and has been demonstrated in many projects that have transformed EHR data into retrospective medical history information on participating subjects directly from their physician's collected records; many key challenges, however, remain. In this paper, we present a citizen-centric framework called eHealthBioR, which would enable biobanks to link to EHR systems, thus enabling not just retrospective but also lifelong prospective longitudinal studies of participating citizens. It will also ensure strict adherence to legal and ethical requirements, enabling greater control that encourages participation. Citizens would benefit from the real and direct control of their data and samples, utilizing technology, to empower them to make informed decisions about providing consent and practicing their rights related to the use of their data, as well as by having access to knowledge and data generated from samples they provided to biobanks. This is expected to motivate patient engagement in future research and even leads to participatory design methodologies with citizen/patient-centric designed studies. The development of platforms based on the eHealthBioR framework would need to overcome significant challenges. However, it would shift the burden of addressing these to experts in the field while providing solutions enabling in the long term the lower monetary and time cost of longitudinal studies coupled with the option of lifelong monitoring through EHRs.

The anatomy of electronic patient record ethics: a framework to guide design, development, implementation, and use

BACKGROUND

This manuscript presents a framework to guide the identification and assessment of ethical opportunities and challenges associated with electronic patient records (EPR). The framework is intended to support designers, software engineers, health service managers, and end-users to realise a responsible, robust and reliable EPR-enabled healthcare system that delivers safe, quality assured, value conscious care.

METHODS

Development of the EPR applied ethics framework was preceded by a scoping review which mapped the literature related to the ethics of EPR technology. The underlying assumption behind the framework presented in this manuscript is that ethical values can inform all stages of the EPR-lifecycle from design, through development, implementation, and practical application.

RESULTS

The framework is divided into two parts: context and core functions. The first part 'context' entails clarifying: the purpose(s) within which the EPR exists or will exist; the interested parties and their relationships; and the regulatory, codes of professional conduct and organisational policy frame of reference. Understanding the context is required before addressing the second part of the framework which focuses on EPR 'core functions' of data collection, data access, and digitally-enabled healthcare.

CONCLUSIONS

The primary objective of the EPR Applied Ethics Framework is to help identify and create value and benefits rather than to merely prevent risks. It should therefore be used to steer an EPR project to success rather than be seen as a set of inhibitory rules. The framework is adaptable to a wide range of EPR categories and can cater for new and evolving EPR-enabled healthcare priorities. It is therefore an iterative tool that should be revisited as new EPR-related state-of-affairs, capabilities or activities emerge.

Examination and diagnosis of electronic patient records and their associated ethics: a scoping literature review

Background

Electronic patient record (EPR) technology is a key enabler for improvements to healthcare service and management. To ensure these improvements and the means to achieve them are socially and ethically desirable, careful consideration of the ethical implications of EPRs is indicated. The purpose of this scoping review was to map the literature related to the ethics of EPR technology. The literature review was conducted to catalogue the prevalent ethical terms, to describe the associated ethical challenges and opportunities, and to identify the actors involved. By doing so, it aimed to support the future development of ethics guidance in the EPR domain.

Methods

To identify journal articles debating the ethics of EPRs, Scopus, Web of Science, and PubMed academic databases were queried and yielded 123 eligible articles. The following inclusion criteria were applied: articles need to be in the English language; present normative arguments and not solely empirical research; include an abstract for software analysis; and discuss EPR technology.

Results

The medical specialty, type of information captured and stored in EPRs, their use and functionality varied widely across the included articles. Ethical terms extracted were categorised into clusters ‘privacy’, ‘autonomy’, ‘risk/benefit’, ‘human relationships’, and ‘responsibility’. The literature shows that EPR-related ethical concerns can have both positive and negative implications, and that a wide variety of actors with rights and/or responsibilities regarding the safe and ethical adoption of the technology are involved.

Conclusions

While there is considerable consensus in the literature regarding EPR-related ethical principles, some of the associated challenges and opportunities remain underdiscussed. For example, much of the debate is presented in a manner more in keeping with a traditional model of healthcare and fails to take account of the multidimensional ensemble of factors at play in the EPR era and the consequent need to redefine/modify ethical norms to align with a digitally-enabled health service. Similarly, the academic discussion focuses predominantly on bioethical values. However, approaches from digital ethics may also be helpful to identify and deliberate about current and emerging EPR-related ethical concerns.

Comprehensive Rare Disease Care model for screening and diagnosis of rare genetic diseases - an experience of private medical college and hospital, South India

Rare diseases (RD) of genetic origin are raising public health concern contributing to a massive economic burden in India. Establishing Specialty Centers to bridge the RD community with apex centers is felt as a need in developing countries. Hence a Comprehensive Rare Disease Care (CRDC) model was set up at the department of pediatrics under Center for Human Genomics and Counseling at a medical college hospital in South India. The patients suspected to have genetic disease were evaluated as per the work flow of the designed model. The utilization statistics depict the outcome of this model. In the face of limited resources, it was possible to establish a functional RD unit with meticulous planning, supportive administration and trained interdisciplinary staff. A scalable prototype that could be replicated in other Medical colleges and Hospitals of India is described.

Utilization of health information technology among cancer genetic counselors

Background

Health information technology (IT) is becoming increasingly utilized by cancer genetic counselors (CGCs). We sought to understand the current engagement, satisfaction, and opportunities to adopt new health IT tools among CGCs.

Methods

We conducted a mixed‐mode survey among 128 board‐certified CGCs using both closed‐ and open‐ended questions. We then evaluated the utilization and satisfaction among 10 types of health IT tools, including the following: cancer screening tool, family health history (FHx) collection tools, electronic health records (EHRs), telegenetics software, pedigree drawing software, genetic risk assessment tools, gene test panel ordering tools, electronic patient education tools, patient communication tools, and family communication tools.

Results

Seven of 10 health IT tools were used by a minority of CGCs. The vast majority of respondents reported using EHRs (95.2%) and genetic risk assessment tools (88.6%). Genetic test panel ordering software had the highest satisfaction rate (very satisfied and satisfied) at 80.0%, followed by genetic risk assessment tools (77.1%). EHRs had the highest dissatisfaction rate among CGCs at 18.3%. Dissatisfaction with a health IT tool was associated with desire to change: EHRs (p < .001), cancer screening tools (p = .010), genetic risk assessment tools (p = .024), and family history collection tools (p = .026). We found that nearly half of CGCs were considering adopting or changing their FHx tool (49.2%), cancer screening tool (44.9%), and pedigree drawing tool (41.8%).

Conclusion

Overall, CGCs reported high levels of satisfaction among commonly used health IT tools. Tools that enable the collection of FHx, cancer screening tools, and pedigree drawing software represent the greatest opportunities for research and development.

Patient perspectives on variant reclassification after cancer susceptibility testing

BACKGROUND

Little is known about the impact of reclassification on patients' perception of medical uncertainty or trust in genetics-based clinical care.

METHODS

Semistructured telephone interviews were conducted with 20 patients who had received a reclassified genetic test result related to hereditary cancer. All participants had undergone genetic counseling and testing for cancer susceptibility at Vanderbilt-Ingram Cancer Center Hereditary Cancer Clinic within the last six years.

RESULTS

Most of the participants did not express distress related to the variant reclassification and only a minority expressed a decrease in trust in medical genetics. However, recall of the new interpretation was limited, even though all participants were recontacted by letter, phone, or clinic visit.

CONCLUSION

Reclassification of genetic tests is an important issue in modern healthcare because changes in interpretation have the potential to alter previously recommended management. Participants in this study did not express strong feelings of mistrust or doubt about their genetic evaluation. However, there was a low level of comprehension and information retention related to the updated report. Future research can build on this study to improve communication with patients about their reclassified results.

Confidential genetic testing and electronic health records: A survey of current practices among Huntington disease testing centers

Background

Clinical care teams providing presymptomatic genetic testing often employ advanced confidentiality practices for documentation and result storage. However, patient requests for increased confidentiality may be in conflict with the legal obligations of medical providers to document patient care activities in the electronic health record (EHR). Huntington disease presents a representative case study for investigating the ways centers currently balance the requirements of EHRs with the privacy demands of patients seeking presymptomatic genetic testing.

Methods

We surveyed 23 HD centers (53% response rate) regarding their use of the EHR for presymptomatic HD testing.

Results

Our survey revealed that clinical care teams and laboratories have each developed their own practices, which are cumbersome and often include EHR avoidance. We found that a majority of HD care teams record appointments in the EHR (91%), often using vague notes. Approximately half of the care teams (52%) keep presymptomatic results of out of the EHR.

Conclusion

As genetic knowledge grows, linking more genes to late‐onset conditions, institutions will benefit from having professional recommendations to guide development of policies for EHR documentation of presymptomatic genetic results. Policies must be sensitive to the ethical differences and patient demands for presymptomatic genetic testing compared to those undergoing confirmatory genetic testing.

Integrating Genetic Data into Electronic Health Records: Medical Geneticists' Perspectives

OBJECTIVES

Genetic disorders are the main causes of many other diseases. Integrating genetic data into Electronic Health Records (EHRs) can facilitate the management of genetic information and care of patients in clinical practices. The aim of this study was to identify the main requirements for integrating genetic data into the EHR system from the medical geneticists' perspectives.

METHODS

The research was completed in 2018 and consisted of two phases. In the first phase, the main requirements for integrating genetic data into the EHR system were identified by reviewing the literature. In the second phase, a 5-point Likert scale questionnaire was developed based on the literature review and the results derived from the first phase. Then, the Delphi method was applied to reach a consensus about the integration requirements.

RESULTS

The findings of the first phase showed that data elements, including patients' and healthcare providers' personal data, clinical and genetic data, technical infrastructure, security issues and functional requirements, should be taken into account before data integration. In the second phase, a consensus was reached for most of the items (mean ≥3.75). The items with a mean value of less than 2.5 did not achieve a consensus and were removed from the final list.

CONCLUSIONS

The integration of genetic data into the EHRs can provide a ground for increasing accuracy and precision in the diagnosis and treatment of genetic disorders. Such integration requires adequate investments to identify users' requirements as well as technical and non-technical issues.

Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application

There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine.

Multiple Stakeholder Views on Data Sharing in a Biobank in an Integrated Healthcare Delivery System: Implications for Biobank Governance

BACKGROUND

Beginning in 2005, researchers at Kaiser Permanente Northern California (KPNC) Division of Research developed the Research Program on Genes, Environment, and Health (RPGEH), a research resource of linked biospecimens, health surveys, and electronic health records on more than 200,000 adult KPNC members. This study examined multiple stakeholders' values and preferences regarding protection of participants' privacy and wide sharing of participant data by RPGEH.

METHODS

We conducted 45 semi-structured interviews in person or via phone and two focus groups with seven stakeholder groups, including RPGEH participants and decliners who are KPNC members, KPNC research scientists, external scientists, leadership, Human Subjects Research Protection Program staff, and RPGEH Community Advisory Panel members.

RESULTS

Three major themes emerged related to: (1) perceived individual and social harms associated with data sharing; (2) concerns to address when governing access to RPGEH data; and (3) impact of a blurred boundary between research and clinical care in the context of biobanking.

CONCLUSIONS

The study results were considered in the development of RPGEH data governance and motivated the inclusion of KPNC Community Advisory Panel members and ELSI experts on committees that evaluate data access proposals. Our findings can help inform other biobanks going through similar processes developing data sharing and access policies.

Standards and legacies: Pragmatic constraints on a uniform gene nomenclature

Over the past half-century, there have been concerted efforts to standardize how clinicians and medical researchers refer to genetic material. However, practical and historical impediments thwart this goal. In the current paper I argue that the ontological status of a genetic mutation cannot be cleanly separated from its pragmatic role in therapy. Attempts at standardization fail due to the non-standardized ends to which genetic information is employed, along with historical inertia and unregulated local innovation. These factors prevent rationalistic attempts to 'modernize' what is otherwise trumpeted as the most modern of the medical sciences.

The Spectrum of Genetic Testing

OBJECTIVE

To introduce genetic testing as it relates to oncology and nursing.

DATA SOURCES

Peer-reviewed journals, government web sites and resources, published recommendations, and professional experience as a genetic counselor.

CONCLUSION

Genetic testing is a major component of oncology health care and with the continued expansion of the application of genetic testing, many patients will have genetic testing throughout their cancer journey.

IMPLICATIONS FOR NURSING PRACTICE

To provide supportive care for patients with cancer or at risk for cancer, oncology nurses need to appreciate the many and varied genetic testing platforms and testing strategies. Oncology nurses can be a resource for patients and family members regarding testing options, insurance coverage, and understanding medical management decisions.

Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning

The increasing amount of scientific literature in biological and biomedical science research has created a challenge in continuous and reliable curation of the latest knowledge discovered, and automatic biomedical text-mining has been one of the answers to this challenge. In this paper, we aim to further improve the reliability of biomedical text-mining by training the system to directly simulate the human behaviors such as querying the PubMed, selecting articles from queried results, and reading selected articles for knowledge. We take advantage of the efficiency of biomedical text-mining, the exibility of deep reinforcement learning, and the massive amount of knowledge collected in UMLS into an integrative artificial intelligent reader that can automatically identify the authentic articles and effectively acquire the knowledge conveyed in the articles. We construct a system, whose current primary task is to build the genetic association database between genes and complex traits of human. Our contributions in this paper are three-fold: 1) We propose to improve the reliability of text-mining by building a system that can directly simulate the behavior of a researcher, and we develop corresponding methods, such as Bi-directional LSTM for text mining and Deep Q-Network for organizing behaviors. 2) We demonstrate the effectiveness of our system with an example in constructing a genetic association database. 3) We release our implementation as a generic framework for researchers in the community to conveniently construct other databases.

Operationalizing the Reciprocal Engagement Model of Genetic Counseling Practice: a Framework for the Scalable Delivery of Genomic Counseling and Testing

With the advent of widespread genomic testing for diagnostic indications and disease risk assessment, there is increased need to optimize genetic counseling services to support the scalable delivery of precision medicine. Here, we describe how we operationalized the reciprocal engagement model of genetic counseling practice to develop a framework of counseling components and strategies for the delivery of genomic results. This framework was constructed based upon qualitative research with patients receiving genomic counseling following online receipt of potentially actionable complex disease and pharmacogenomics reports. Consultation with a transdisciplinary group of investigators, including practicing genetic counselors, was sought to ensure broad scope and applicability of these strategies for use with any large-scale genomic testing effort. We preserve the provision of pre-test education and informed consent as established in Mendelian/single-gene disease genetic counseling practice. Following receipt of genomic results, patients are afforded the opportunity to tailor the counseling agenda by selecting the specific test results they wish to discuss, specifying questions for discussion, and indicating their preference for counseling modality. The genetic counselor uses these patient preferences to set the genomic counseling session and to personalize result communication and risk reduction recommendations. Tailored visual aids and result summary reports divide areas of risk (genetic variant, family history, lifestyle) for each disease to facilitate discussion of multiple disease risks. Post-counseling, session summary reports are actively routed to both the patient and their physician team to encourage review and follow-up. Given the breadth of genomic information potentially resulting from genomic testing, this framework is put forth as a starting point to meet the need for scalable genetic counseling services in the delivery of precision medicine.

Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record

OBJECTIVE

Clinical informatics researchers depend on the availability of high-quality data from the electronic health record (EHR) to design and implement new methods and systems for clinical practice and research. However, these data are frequently unavailable or present in a format that requires substantial revision. This article reports the results of a review of informatics literature published from 2010 to 2016 that addresses these issues by identifying categories of data content that might be included or revised in the EHR.

MATERIALS AND METHODS

We used an iterative review process on 1,215 biomedical informatics research articles. We placed them into generic categories, reviewed and refined the categories, and then assigned additional articles, for a total of three iterations.

RESULTS

Our process identified eight categories of data content issues: Adverse Events, Clinician Cognitive Processes, Data Standards Creation and Data Communication, Genomics, Medication List Data Capture, Patient Preferences, Patient-reported Data, and Phenotyping.

DISCUSSION

These categories summarize discussions in biomedical informatics literature that concern data content issues restricting clinical informatics research. These barriers to research result from data that are either absent from the EHR or are inadequate (e.g., in narrative text form) for the downstream applications of the data. In light of these categories, we discuss changes to EHR data storage that should be considered in the redesign of EHRs, to promote continued innovation in clinical informatics.

CONCLUSION

Based on published literature of clinical informaticians' reuse of EHR data, we characterize eight types of data content that, if included in the next generation of EHRs, would find immediate application in advanced informatics tools and techniques.

Consanguineous marriages in the genetic counseling centers of Isfahan and the ethical issues of clinical consultations

Consanguineous marriage, which is common in many regions in the world, has absorbed much attention as a causative factor in raising the incidence of genetic diseases. The adverse effects may be attributed to the expression of the genes received from common ancestors and mortality and morbidity of the offspring. Iran has a high rate of consanguineous marriages. In recent years genetic counseling has come to be considered in health care services. This cross-sectional study was conducted in order to determine the prevalence and types of consanguineous marriages in the genetic clinics in Isfahan. We aimed to define the different types of marriages, specific categories of genetic disorders associated with consanguineous marriages, and mode of inheritance in the family tree. We also narratively reviewed the ethical aspects of the issue. The data were collected using a simple questionnaire. A total number of 1535 couples from urban and rural areas formed the study population. The marriages were classified according to the degree of the relationship between couples, including: double cousin, first cousin, first cousin once removed, second cousin and beyond second cousin. The SPSS software version 16 was used for data analysis. Data obtained through genetic counseling offered during a 5-year period revealed that 74.3% had consanguineous relationships, 62.3% were first cousins, 1% were double cousins and 7.8% were second cousins. In addition, 76% of the couples had at least one genetic disease in their family tree. Related ethical issues were also considered in this study, including autonomy and informed decision making, benefit and harm assessment, confidentiality, ethics in research, justice in access to counseling services, financial problems ethics, and the intellectual property of scientific success.

Ethics and Epistemology in Big Data Research

Biomedical innovation and translation are increasingly emphasizing research using "big data." The hope is that big data methods will both speed up research and make its results more applicable to "real-world" patients and health services. While big data research has been embraced by scientists, politicians, industry, and the public, numerous ethical, organizational, and technical/methodological concerns have also been raised. With respect to technical and methodological concerns, there is a view that these will be resolved through sophisticated information technologies, predictive algorithms, and data analysis techniques. While such advances will likely go some way towards resolving technical and methodological issues, we believe that the epistemological issues raised by big data research have important ethical implications and raise questions about the very possibility of big data research achieving its goals.

Assessing the readiness of precision medicine interoperabilty: An exploratory study of the National Institutes of Health genetic testing registry

BACKGROUND

  Precision medicine involves three major innovations currently taking place in healthcare:  electronic health records, genomics, and big data.  A major challenge for healthcare providers, however, is understanding the readiness for practical application of initiatives like precision medicine.

OBJECTIVE

  To better understand the current state and challenges of precision medicine interoperability using a national genetic testing registry as a starting point, placed in the context of established interoperability formats.

METHODS

  We performed an exploratory analysis of the National Institutes of Health Genetic Testing Registry.  Relevant standards included Health Level Seven International Version 3 Implementation Guide for Family History, the Human Genome Organization Gene Nomenclature Committee (HGNC) database, and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT).  We analyzed the distribution of genetic testing laboratories, genetic test characteristics, and standardized genome/clinical code mappings, stratified by laboratory setting.

RESULTS

There were a total of 25472 genetic tests from 240 laboratories testing for approximately 3632 distinct genes.  Most tests focused on diagnosis, mutation confirmation, and/or risk assessment of germline mutations that could be passed to offspring.  Genes were successfully mapped to all HGNC identifiers, but less than half of tests mapped to SNOMED CT codes, highlighting significant gaps when linking genetic tests to standardized clinical codes that explain the medical motivations behind test ordering.  Conclusion:  While precision medicine could potentially transform healthcare, successful practical and clinical application will first require the comprehensive and responsible adoption of interoperable standards, terminologies, and formats across all aspects of the precision medicine pipeline.

Security controls in an integrated Biobank to protect privacy in data sharing: rationale and study design

Background

With the goal of realizing genome-based personalized healthcare, we have developed a biobank that integrates personal health, genome, and omics data along with biospecimens donated by volunteers of 150,000. Such a large-scale of data integration involves obvious risks of privacy violation. The research use of personal genome and health information is a topic of global discussion with regard to the protection of privacy while promoting scientific advancement. The present paper reports on our plans, current attempts, and accomplishments in addressing security problems involved in data sharing to ensure donor privacy while promoting scientific advancement.

Methods

Biospecimens and data have been collected in prospective cohort studies with the comprehensive agreement. The sample size of 150,000 participants was required for multiple researches including genome-wide screening of gene by environment interactions, haplotype phasing, and parametric linkage analysis.

Results

We established the TohokuMedicalMegabank (TMM) data sharing policy: a privacy protection rule that requires physical, personnel, and technological safeguards against privacy violation regarding the use and sharing of data. The proposed policy refers to that of NCBI and that of the Sanger Institute. The proposed policy classifies shared data according to the strength of re-identification risks. Local committees organized by TMM evaluate re-identification risk and assign a security category to a dataset. Every dataset is stored in an assigned segment of a supercomputer in accordance with its security category. A security manager should be designated to handle all security problems at individual data use locations. The proposed policy requires closed networks and IP-VPN remote connections.

Conclusion

The mission of the biobank is to distribute biological resources most productively. This mission motivated us to collect biospecimens and health data and simultaneously analyze genome/omics data in-house. The biobank also has the mission of improving the quality and quantity of the contents of the biobank. This motivated us to request users to share the results of their research as feedback to the biobank. The TMM data sharing policy has tackled every security problem originating with the missions. We believe our current implementation to be the best way to protect privacy in data sharing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-017-0494-5) contains supplementary material, which is available to authorized users.

A 'joint venture' model of recontacting in clinical genomics: challenges for responsible implementation

Advances in genomics often lead healthcare professionals (HCPs) to learn new information, e.g., about reinterpreted variants that could have clinical significance for patients seen previously. A question arises of whether HCPs should recontact these former patients. We present some findings interrogating the views of patients (or parents of patients) with a rare or undiagnosed condition about how such recontacting might be organised ethically and practically. Forty-one interviews were analysed thematically. Participants suggested a 'joint venture' model in which efforts to recontact are shared with HCPs. Some proposed an ICT-approach involving an electronic health record that automatically alerts them to potentially relevant updates. The need for rigorous privacy controls and transparency about who could access their data was emphasised. Importantly, these findings highlight that the lack of clarity about recontacting is a symptom of a wider problem: the lack of necessary infrastructure to pool genomic data responsibly, to aggregate it with other health data, and to enable patients/parents to receive updates. We hope that our findings will instigate a debate about the way responsibilities for recontacting under any joint venture model could be allocated, as well as the limitations and normative implications of using ICT as a solution to this intractable problem. As a first step to delineating responsibilities in the clinical setting, we suggest HCPs should routinely discuss recontacting with patients/parents, including the new information that should trigger a HCP to initiate recontact, as part of the consent process for genetic testing.

A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries

In the past decade, the volume of "omics" data generated by the different high-throughput technologies has expanded exponentially. The managing, storing, and analyzing of this big data have been a great challenge for the researchers, especially when moving towards the goal of generating testable data-driven hypotheses, which has been the promise of the high-throughput experimental techniques. Different bioinformatics approaches have been developed to streamline the downstream analyzes by providing independent information to interpret and provide biological inference. Text mining (also known as literature mining) is one of the commonly used approaches for automated generation of biological knowledge from the huge number of published articles. In this review paper, we discuss the recent advancement in approaches that integrate results from omics data and information generated from text mining approaches to uncover novel biomedical information.

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.

Developing knowledge resources to support precision medicine: principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC)

To move beyond a select few genes/drugs, the successful adoption of pharmacogenomics into routine clinical care requires a curated and machine-readable database of pharmacogenomic knowledge suitable for use in an electronic health record (EHR) with clinical decision support (CDS). Recognizing that EHR vendors do not yet provide a standard set of CDS functions for pharmacogenetics, the Clinical Pharmacogenetics Implementation Consortium (CPIC) Informatics Working Group is developing and systematically incorporating a set of EHR-agnostic implementation resources into all CPIC guidelines. These resources illustrate how to integrate pharmacogenomic test results in clinical information systems with CDS to facilitate the use of patient genomic data at the point of care. Based on our collective experience creating existing CPIC resources and implementing pharmacogenomics at our practice sites, we outline principles to define the key features of future knowledge bases and discuss the importance of these knowledge resources for pharmacogenomics and ultimately precision medicine.

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health

The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.

Electronic health records: current and future use

This paper provides an overview of the current state of the electronic medical record, including benefits and shortcomings, and presents key factors likely to drive development in the next decade and beyond. The current electronic medical record to a large extent represents a digital version of the traditional paper legal record, owned and maintained by the practitioner. The future electronic health record is expected to be a shared tool, engaging patients in decision making, wellness and disease management and providing data for individual decision support, population management and analytics. Many drivers will determine this path, including payment model reform, proliferation of mobile platforms, telemedicine, genomics and individualized medicine and advances in 'big data' technologies.

Comparison of consumers' views on electronic data sharing for healthcare and research

New models of healthcare delivery such as accountable care organizations and patient-centered medical homes seek to improve quality, access, and cost. They rely on a robust, secure technology infrastructure provided by health information exchanges (HIEs) and distributed research networks and the willingness of patients to share their data. There are few large, in-depth studies of US consumers' views on privacy, security, and consent in electronic data sharing for healthcare and research together.

OBJECTIVE

This paper addresses this gap, reporting on a survey which asks about California consumers' views of data sharing for healthcare and research together.

MATERIALS AND METHODS

The survey conducted was a representative, random-digit dial telephone survey of 800 Californians, performed in Spanish and English.

RESULTS

There is a great deal of concern that HIEs will worsen privacy (40.3%) and security (42.5%). Consumers are in favor of electronic data sharing but elements of transparency are important: individual control, who has access, and the purpose for use of data. Respondents were more likely to agree to share deidentified information for research than to share identified information for healthcare (76.2% vs 57.3%, p < .001).

DISCUSSION

While consumers show willingness to share health information electronically, they value individual control and privacy. Responsiveness to these needs, rather than mere reliance on Health Insurance Portability and Accountability Act (HIPAA), may improve support of data networks.

CONCLUSION

Responsiveness to the public's concerns regarding their health information is a pre-requisite for patient-centeredness. This is one of the first in-depth studies of attitudes about electronic data sharing that compares attitudes of the same individual towards healthcare and research.

Secure Genomic Computation through Site-Wise Encryption

Commercial clouds provide on-demand IT services for big-data analysis, which have become an attractive option for users who have no access to comparable infrastructure. However, utilizing these services for human genome analysis is highly risky, as human genomic data contains identifiable information of human individuals and their disease susceptibility. Therefore, currently, no computation on personal human genomic data is conducted on public clouds. To address this issue, here we present a site-wise encryption approach to encrypt whole human genome sequences, which can be subject to secure searching of genomic signatures on public clouds. We implemented this method within the Hadoop framework, and tested it on the case of searching disease markers retrieved from the ClinVar database against patients' genomic sequences. The secure search runs only one order of magnitude slower than the simple search without encryption, indicating our method is ready to be used for secure genomic computation on public clouds.

Supporting interoperability of genetic data with LOINC

Electronic reporting of genetic testing results is increasing, but they are often represented in diverse formats and naming conventions. Logical Observation Identifiers Names and Codes (LOINC) is a vocabulary standard that provides universal identifiers for laboratory tests and clinical observations. In genetics, LOINC provides codes to improve interoperability in the midst of reporting style transition, including codes for cytogenetic or mutation analysis tests, specific chromosomal alteration or mutation testing, and fully structured discrete genetic test reporting. LOINC terms follow the recommendations and nomenclature of other standards such as the Human Genome Organization Gene Nomenclature Committee's terminology for gene names. In addition to the narrative text they report now, we recommend that laboratories always report as discrete variables chromosome analysis results, genetic variation(s) found, and genetic variation(s) tested for. By adopting and implementing data standards like LOINC, information systems can help care providers and researchers unlock the potential of genetic information for delivering more personalized care.

Automatic Placement of Genomic Research Results in Medical Records: Do Researchers Have a Duty? Should Participants Have a Choice?

In genomics research, it is becoming common practice to return individualized primary and incidental findings to participants and several ongoing major studies have begun to automatically transfer these results to a participant's clinical medical record. This paper explores who should decide whether to place genomic research findings into a clinical medical record. Should participants make this decision, or does a researcher's duty to place this information in a medical record override the participant's autonomy? We argue that there are no clear ethical, legal, professional, or regulatory duties that mandate placement without the consent of the participant. We conclude that informing participants of results, together with a clear explanation, relevant recommendations and referral sources, and the option to consent to placement in the medical records will best discharge researchers' ethical and legal duties towards participants.

A community assessment of privacy preserving techniques for human genomes

To answer the need for the rigorous protection of biomedical data, we organized the Critical Assessment of Data Privacy and Protection initiative as a community effort to evaluate privacy-preserving dissemination techniques for biomedical data. We focused on the challenge of sharing aggregate human genomic data (e.g., allele frequencies) in a way that preserves the privacy of the data donors, without undermining the utility of genome-wide association studies (GWAS) or impeding their dissemination. Specifically, we designed two problems for disseminating the raw data and the analysis outcome, respectively, based on publicly available data from HapMap and from the Personal Genome Project. A total of six teams participated in the challenges. The final results were presented at a workshop of the iDASH (integrating Data for Analysis, 'anonymization,' and SHaring) National Center for Biomedical Computing. We report the results of the challenge and our findings about the current genome privacy protection techniques.

Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives. Contribution of the IMIA Social Media Working Group

OBJECTIVES

As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges.

METHODS

A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale.

RESULTS

Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts: The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to "small data" would also be useful.

Genetics and health communication: a primer

The progress of genetic knowledge has been swift and steadfast. As we move forward in the genomic era, post Human Genome Project, and continue to explore how one's genes interact with one's environment, it becomes increasingly important for all audiences to have a firm grasp of the vocabulary used in this health context. This primer is intended to be used as a reference and to introduce and/or make more clear concepts related to genetics to increase understanding.

Deciphering next-generation pharmacogenomics: an information technology perspective

In the post-genomic era, the rapid evolution of high-throughput genotyping technologies and the increased pace of production of genetic research data are continually prompting the development of appropriate informatics tools, systems and databases as we attempt to cope with the flood of incoming genetic information. Alongside new technologies that serve to enhance data connectivity, emerging information systems should contribute to the creation of a powerful knowledge environment for genotype-to-phenotype information in the context of translational medicine. In the area of pharmacogenomics and personalized medicine, it has become evident that database applications providing important information on the occurrence and consequences of gene variants involved in pharmacokinetics, pharmacodynamics, drug efficacy and drug toxicity will become an integral tool for researchers and medical practitioners alike. At the same time, two fundamental issues are inextricably linked to current developments, namely data sharing and data protection. Here, we discuss high-throughput and next-generation sequencing technology and its impact on pharmacogenomics research. In addition, we present advances and challenges in the field of pharmacogenomics information systems which have in turn triggered the development of an integrated electronic 'pharmacogenomics assistant'. The system is designed to provide personalized drug recommendations based on linked genotype-to-phenotype pharmacogenomics data, as well as to support biomedical researchers in the identification of pharmacogenomics-related gene variants. The provisioned services are tuned in the framework of a single-access pharmacogenomics portal.

Translational informatics in personalized medicine: an update for 2014

Many things have changed but much has remained the same as we have seen a dramatic increase in the generation of genetics, genomics and a variety of clinical data leading to increased data density and continued challenges in organizing and managing that data in pursuit of personalized medicine. Simultaneously, we have seen an increase in commercial and open-source solutions, and marked movement toward open sharing of tools and data in public–private partnerships, yet still few examples of traditional companion diagnostics for personalized medicine products. Most encouraging are examples of focused public and private efforts that have resulted in knowledge leading to critical assessment of existing therapies and the development of new therapies. These examples lay highly emulatable informatics foundations for rapid advances in personalized medicine.