Electronic Health Records and Medical Big Data: Law and Policy

This book helps readers gain an in-depth understanding of electronic health record (EHR) systems, medical big data, and the regulations that govern them. It is useful both as a primer for students and as a resource for knowledgeable professionals. The book analyzes the shortcomings and benefits of EHR systems, explores the law\u27s response to the technology’s adoption, highlights gaps in the current legal framework, and develops detailed recommendations for regulatory, policy, and technological improvements. Electronic Health Records and Medical Big Data addresses not only privacy and security concerns, but also other important challenges, such as those related to data quality and data analysis. The book’s many recommendations aim to improve the technology\u27s safety, security, and efficacy for both clinical and secondary (such as research) uses of medical data

Electronic Health Records and Medical Big Data: Law and Policy

This book helps readers gain an in-depth understanding of electronic health record (EHR) systems, medical big data, and the regulations that govern them. It is useful both as a primer for students and as a resource for knowledgeable professionals. The book analyzes the shortcomings and benefits of EHR systems, explores the law\u27s response to the technology’s adoption, highlights gaps in the current legal framework, and develops detailed recommendations for regulatory, policy, and technological improvements. Electronic Health Records and Medical Big Data addresses not only privacy and security concerns, but also other important challenges, such as those related to data quality and data analysis. The book’s many recommendations aim to improve the technology\u27s safety, security, and efficacy for both clinical and secondary (such as research) uses of medical data

a New Scientific Paradigm of Information and Knowledge Development in National Statistical Systems

Ashofteh, A., & Bravo, J. M. (2021). Data Science Training for Official Statistics: a New Scientific Paradigm of Information and Knowledge Development in National Statistical Systems. Statistical Journal of the IAOS, 37(3), 771 – 789. https://doi.org/10.3233/SJI-210841The ability to incorporate new and Big Data sources and to benefit from emerging technologies such as Web Technologies, Remote Data Collection methods, User Experience Platforms, and Trusted Smart Statistics will become increasingly important in producing and disseminating official statistics. The skills and competencies required to automate, analyse, and optimize such complex systems are often not part of the traditional skill set of most National Statistical Offices. The adoption of these technologies requires new knowledge, methodologies and the upgrading of the quality assurance framework, technology, security, privacy, and legal matters. However, there are methodological challenges and discussions among scholars about the diverse methodical confinement and the wide array of skills and competencies considered relevant for those working with big data at NSOs. This paper develops a Data Science Model for Official Statistics (DSMOS), graphically summarizing the role of data science in statistical business processes. The model combines data science, existing scientific paradigms, and trusted smart statistics, and develops around a restricted number of constructs. We considered a combination of statistical engineering, data engineering, data analysis, software engineering and soft skills such as statistical thinking, statistical literacy and specific knowledge of official statistics and dissemination of official statistics products as key requirements of data science in official statistics. We then analyse and discuss the educational requirements of the proposed model, clarifying their contribution, interactions, and current and future importance in official statistics. The DSMOS was validated through a quantitative method, using a survey addressed to experts working at the European statistical systems. The empirical results show that the core competencies considered relevant for the DSMOS include acquisition and processing capabilities related to Statistics, high-frequency data, spatial data, Big Data, and microdata/nano-data, in addition to problem-solving skills, Spatio-temporal modelling, machine learning, programming with R and SAS software, Data visualisation using novel technologies, Data and statistical literacy, Ethics in Official Statistics, New data methodologies, New data quality tools, standards and frameworks for official statistics. Some disadvantages and vulnerabilities are also addressed in the paper.publishersversionpublishe

Big data from electronic health records for early and late translational cardiovascular research: challenges and potential.

AIMS: Cohorts of millions of people's health records, whole genome sequencing, imaging, sensor, societal and publicly available data present a rapidly expanding digital trace of health. We aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research. METHODS AND RESULTS: We sought exemplars based on literature reviews and expertise across the BigData@Heart Consortium. We identified formidable challenges including: data quality, knowing what data exist, the legal and ethical framework for their use, data sharing, building and maintaining public trust, developing standards for defining disease, developing tools for scalable, replicable science and equipping the clinical and scientific work force with new inter-disciplinary skills. Opportunities claimed for big health record data include: richer profiles of health and disease from birth to death and from the molecular to the societal scale; accelerated understanding of disease causation and progression, discovery of new mechanisms and treatment-relevant disease sub-phenotypes, understanding health and diseases in whole populations and whole health systems and returning actionable feedback loops to improve (and potentially disrupt) existing models of research and care, with greater efficiency. In early translational research we identified exemplars including: discovery of fundamental biological processes e.g. linking exome sequences to lifelong electronic health records (EHR) (e.g. human knockout experiments); drug development: genomic approaches to drug target validation; precision medicine: e.g. DNA integrated into hospital EHR for pre-emptive pharmacogenomics. In late translational research we identified exemplars including: learning health systems with outcome trials integrated into clinical care; citizen driven health with 24/7 multi-parameter patient monitoring to improve outcomes and population-based linkages of multiple EHR sources for higher resolution clinical epidemiology and public health. CONCLUSION: High volumes of inherently diverse ('big') EHR data are beginning to disrupt the nature of cardiovascular research and care. Such big data have the potential to improve our understanding of disease causation and classification relevant for early translation and to contribute actionable analytics to improve health and healthcare

Action Research on Development and Application of AIoT Traffic Solution

AIoT solution based on the AI (Artificial Intelligent) and IoT (Internet of Things) is considered state-of-the-art technology and has emerged in various business environments. To enhance intelligent traffic quality, maximize energy saving and reduce carbon emission, this study applied an AIoT technology based on traffic counting modules and people behavior modules as traffic inference systems. Applications of the IoT technology based on WiFi, 3G/4G and NB-IoT (Narrowband IoT) was conducted gradually in key demonstration roads and cities worldwide, and the development and evaluation results were aligned to an action research framework. The five phases in the action research included designing, collecting data, analyzing data, communicating outcome, and acting phases. During the first two phases, problems of functional operations in traffic were verified and designed for network services by ICT (Information and Communication Technology) and IoT technologies to collection traffic big data. In the third phase, stakeholders may use basic statistic or further deep learning methods to solve traffic scheduling, order and road safety issues. During the fourth and fifth phases, the roles and benefits of stakeholders participating in the service models were evaluated, and issues and knowledge of the whole application process were respectively derived and summarized from technological, economic, social and legal perspectives. From an action research approach, AIoT-based intelligent traffic solutions were developed and verified and it enables MOTC (Ministry of Transportation and Communications) and stakeholders to acquire traffic big data for optimizing traffic condition in technology enforcement. With its implementation, it will ultimately be able to go one step closer to smart city vision. The derived service models could provide stakeholders, drivers and citizens more enhanced traffic services and improve policies’ work more efficiency and effectiveness

The ethical and legal landscape of brain data governance

Neuroscience research is producing big brain data which informs both advancements in neuroscience research and drives the development of advanced datasets to provide advanced medical solutions. These brain data are produced under different jurisdictions in different formats and are governed under different regulations. The governance of data has become essential and critical resulting in the development of various governance structures to ensure that the quality, availability, findability, accessibility, usability, and utility of data is maintained. Furthermore, data governance is influenced by various ethical and legal principles. However, it is still not clear what ethical and legal principles should be used as a standard or baseline when managing brain data due to varying practices and evolving concepts. Therefore, this study asks what ethical and legal principles shape the current brain data governance landscape? A systematic scoping review and thematic analysis of articles focused on biomedical, neuro and brain data governance was carried out to identify the ethical and legal principles which shape the current brain data governance landscape. The results revealed that there is currently a large variation of how the principles are presented and discussions around the terms are very multidimensional. Some of the principles are still at their infancy and are barely visible. A range of principles emerged during the thematic analysis providing a potential list of principles which can provide a more comprehensive framework for brain data governance and a conceptual expansion of neuroethics

Achieving the Goals of the Value-Based Purchasing Program: Defining a Standard for External Data Use

In our society, artificial intelligence technology has permeated through every aspect of human life. By the end of 2019, more than 60 million Americans will use some kind of smartwatch, whether a Fitbit or an Apple Watch as a part of their daily routine. Further, over 58% of people currently use a healthcare-related mobile application, such as MyFitnessPal or Nike+ Running. Health and fitness application usage increased by over 330% in the last three years. Unsurprisingly, healthcare-related data is one of the fastest growing and financially valuable data pools in the country, if not worldwide. The volume of data generated is predicted to increase to 2,314 exabytes by 2020. Such data is stored in the Big Data pool with universal interface programming, aimed to enhance interoperability with different health provider systems in the medical community. The absence of a legislative directive, guidance, or a systematic approach to organize vast amounts of continuously incoming data on the front end limits its effectiveness through subsequent utilization. It is more prone to being incomplete, dated, erroneous, or incompatible with the analytic systems into which the data is inputted. This problem is especially pertinent in the context of the Value-Based Purchasing (VBP) Program, where the rate of reimbursement of medical services for Medicare patients depends on production of accurate and reliable data. VBP conditions 90% of its payments on the hospitals’ ability to generate data required by the Program. The interconnectedness between VBP’s premise, “enhanced patient quality and lower medical costs,” and production of accurate data highlights the need for legal action to mitigate risks associated with unstructured healthcare data. Such legal action would at a minimum provide a framework or a set of standards for the kinds of data that may be used by the participating VBP hospitals to ensure accurate reporting and the maximization of healthcare analytics. Finally, hospitals nationwide have an independent incentive for acquiring reliable and relevant data to integrate as part of their network to lower actual hospital costs, as Medicare reimbursement was $53.9 billion short in 2017 among the VBP Program providers

Engaging the public & private sectors in data sharing to improve maternal and newborn health in Uttar Pradesh, India

Background The private for-profit health sector in India delivers around 80% of outpatient treatment and 60% of hospitalisations, and includes more than three quarters of human resources for health. The sector includes solo doctor clinics, small hospitals and big corporate hospital chains, as well as many informal providers. The formal private health sector has grown rapidly without regulatory frameworks and quality assurance. Quality of care is variable and there is lack of adherence to standard treatments, protocols or pricing. Limited information is shared with public health information systems. Aim To develop an engagement strategy with the private for-profit health sector in Uttar Pradesh, India. The broader underlying goal is to develop and pilot a district level Data Informed Platform for Health (DIPH) for improved local health decision-making in maternal and child health including both the public and private health sectors. Methods We reviewed literature, and examined national plans and programme documents to identify lessons from successful public-private engagements for maternal and child health and collate key policies related to the private health sector in India. We sought inputs from 27 national, state and district level stakeholders for developing a strategy to engage with the private sector for a DIPH. Findings In India, public-private partnerships for service delivery and financing represent a key area of engagement with the private sector, especially for maternal and child health. Examples include the Merrygold network, a clinical social franchise, and the Sambhav voucher scheme, in which poor households can exchange vouchers for health services in selected city hospitals in Uttar Pradesh. Engagements related to data recording and reporting from the private health sector have been less successful. There are gaps in reporting even notifiable diseases like Tuberculosis. There is limited data available on the private sector at the national level. Legal provisions can facilitate data exchange and synthesis: a binding legal framework may be available when the Clinical Establishments Act, passed by the Indian Parliament in 2010, is implemented. Proposed engagement strategies Stakeholder consultations suggested that before the Clinical Establishments Act is implemented, the private sector might best be engaged by: 1.Relationship building among key private and public sector stakeholders. 2.Sensitisation of private and public sector groups and individuals with the concept of a DIPH. 3.Inclusion of selected private sector players in the DIPH 4.User-friendly data collection and management. 5.Provision of both financial and non-financial incentives to encourag