ABSTRACT
As the health-care industry emerges into a new era of digital health driven by cloud data storage, distributed computing, and machine learning, health-care data have become a premium commodity with value for private and public entities. Current frameworks of health data collection and distribution, whether from industry, academia, or government institutions, are imperfect and do not allow researchers to leverage the full potential of downstream analytical efforts. In this Health Policy paper, we review the current landscape of commercial health data vendors, with special emphasis on the sources of their data, challenges associated with data reproducibility and generalisability, and ethical considerations for data vending. We argue for sustainable approaches to curating open-source health data to enable global populations to be included in the biomedical research community. However, to fully implement these approaches, key stakeholders should come together to make health-care datasets increasingly accessible, inclusive, and representative, while balancing the privacy and rights of individuals whose data are being collected.
Subject(s)
Algorithms , Biomedical Research , Datasets as Topic , Humans , Privacy , Reproducibility of Results , Datasets as Topic/economics , Datasets as Topic/ethics , Datasets as Topic/trends , Consumer Health Information/economics , Consumer Health Information/ethicsSubject(s)
Data Science/methods , Datasets as Topic/ethics , Industry/ethics , Open Access Publishing/ethics , Social Sciences/methods , Artificial Intelligence , Behavioral Research/economics , Behavioral Research/ethics , Behavioral Research/methods , Data Science/economics , Datasets as Topic/economics , Democracy , Humans , Industry/economics , Information Dissemination/ethics , Information Dissemination/methods , Open Access Publishing/economics , Smartphone , Social Conditions/statistics & numerical data , Social Sciences/economicsSubject(s)
Information Dissemination , Open Access Publishing/economics , Records/economics , Research Support as Topic/economics , Research/economics , Datasets as Topic/economics , Open Access Publishing/standards , Records/standards , Reproducibility of Results , Research/standards , Reward , United KingdomSubject(s)
Datasets as Topic/economics , Datasets as Topic/supply & distribution , Information Dissemination/methods , Investments/economics , Research Design/standards , Research Support as Topic/economics , Research/economics , Artificial Intelligence/economics , Artificial Intelligence/trends , Cloud Computing , Data Analysis , Datasets as Topic/standards , European Union/economics , Guidelines as Topic , Information Dissemination/ethics , Netherlands , Research Design/trends , Research PersonnelSubject(s)
Big Data/supply & distribution , Data Mining/methods , Datasets as Topic/supply & distribution , Information Dissemination/legislation & jurisprudence , Information Dissemination/methods , Open Access Publishing/legislation & jurisprudence , Research , Big Data/economics , Data Mining/trends , Datasets as Topic/economics , Datasets as Topic/legislation & jurisprudence , India , Open Access Publishing/economics , Research Report , Unsupervised Machine Learning/legislation & jurisprudence , Unsupervised Machine Learning/trendsSubject(s)
Datasets as Topic/standards , Earth Sciences/statistics & numerical data , Information Dissemination/methods , Information Storage and Retrieval/methods , Information Storage and Retrieval/standards , Open Access Publishing/standards , Recycling/standards , Datasets as Topic/economics , Datasets as Topic/trends , Earth Sciences/economics , Information Storage and Retrieval/economics , Information Storage and Retrieval/trends , Metadata/standards , Metadata/statistics & numerical data , Meteorology/economics , Meteorology/statistics & numerical data , Open Access Publishing/economics , Open Access Publishing/trends , Recycling/economics , Recycling/methods , Recycling/trends , Reproducibility of ResultsSubject(s)
Authorship , Data Curation/methods , Data Curation/trends , Datasets as Topic/supply & distribution , Information Dissemination/methods , Information Storage and Retrieval/methods , Information Storage and Retrieval/trends , Data Curation/standards , Datasets as Topic/economics , Financing, Organized/statistics & numerical data , Information Storage and Retrieval/standards , Motivation , Pilot Projects , Recycling/statistics & numerical data , RewardSubject(s)
Costs and Cost Analysis , Federal Government , Financing, Government/legislation & jurisprudence , Politics , Research Personnel/economics , Research/economics , Research/legislation & jurisprudence , Datasets as Topic/economics , Financing, Government/economics , Hydrology/education , Ohio , Parks, Recreational/economics , Parks, Recreational/legislation & jurisprudence , Rain , Research Personnel/legislation & jurisprudence , Research Personnel/psychology , Research Support as Topic/economics , Research Support as Topic/legislation & jurisprudence , Time Factors , Uncertainty , United StatesSubject(s)
Data Collection/economics , Earth, Planet , Environmental Monitoring/economics , Federal Government , Remote Sensing Technology/economics , Research/economics , Satellite Imagery/economics , Costs and Cost Analysis , Datasets as Topic/economics , Datasets as Topic/statistics & numerical data , Research Personnel/economics , Satellite Imagery/statistics & numerical data , United States , United States Department of AgricultureSubject(s)
Crowdsourcing , DNA/physiology , Genetic Testing , Genomics/methods , Patient Portals , Commerce/trends , Crowdsourcing/economics , Crowdsourcing/methods , Crowdsourcing/trends , Databases, Genetic/trends , Datasets as Topic/economics , Datasets as Topic/supply & distribution , Genetic Association Studies/economics , Genetic Association Studies/methods , Genetic Association Studies/trends , Genetic Testing/economics , Genetic Testing/methods , Genetic Testing/trends , Genomics/economics , Genomics/trends , High-Throughput Screening Assays/economics , High-Throughput Screening Assays/methods , High-Throughput Screening Assays/trends , Humans , Patient Portals/supply & distribution , Patient Portals/trends , Personal Autonomy , Phenotype , Precision Medicine/economics , Precision Medicine/methods , Precision Medicine/trends , Sequence Analysis, DNA/economics , Sequence Analysis, DNA/methods , Sequence Analysis, DNA/trends , User-Computer InterfaceABSTRACT
The new paradigm of the big data raises many expectations, particularly in the field of health. Curiously, even though medical biology laboratories generate a great amount of data, the opportunities offered by this new field are poorly documented. For better understanding the clinical context of chronical disease follow-up, for leveraging preventive and/or personalized medicine, the contribution of big data analytics seems very promising. It is within this framework that we have explored to use data of a Breton group of laboratories of medical biology to analyze the possible contributions of their exploitation in the improvement of the clinical practices and to anticipate the evolution of pathologies for the benefit of patients. We report here three practical applications derived from routine laboratory data from a period of 5 years (February 2010-August 2015): follow-up of patients treated with AVK according to the recommendations of the High authority of health (HAS), use of the new troponin markers HS and NT-proBNP in cardiology. While the risks and difficulties of using algorithms in the health domain should not be underestimated - quality, accessibility, and protection of personal data in particular - these first results show that use of tools and technologies of the big data repository could provide decisive support for the concept of "evidence based medicine".
Subject(s)
Clinical Laboratory Techniques , Datasets as Topic/statistics & numerical data , High-Throughput Screening Assays/statistics & numerical data , Reagent Kits, Diagnostic , Clinical Laboratory Techniques/economics , Clinical Laboratory Techniques/ethics , Clinical Laboratory Techniques/standards , Clinical Laboratory Techniques/statistics & numerical data , Commerce , Datasets as Topic/economics , Datasets as Topic/ethics , Datasets as Topic/standards , Decision Making , Evidence-Based Medicine , Health Records, Personal/economics , Health Records, Personal/ethics , Health Services Misuse , High-Throughput Screening Assays/economics , High-Throughput Screening Assays/ethics , High-Throughput Screening Assays/standards , Humans , Medical Informatics , Practice Guidelines as Topic , Practice Patterns, Physicians'/trends , Precision Medicine/standards , Precision Medicine/trends , Professional Misconduct , Quality Improvement , Reagent Kits, Diagnostic/economics , Reagent Kits, Diagnostic/ethics , Reagent Kits, Diagnostic/standards , Reagent Kits, Diagnostic/statistics & numerical dataSubject(s)
Biomedical Research/economics , Biomedical Research/methods , Database Management Systems/economics , Databases, Factual/economics , Databases, Factual/statistics & numerical data , National Institutes of Health (U.S.)/economics , Research Support as Topic/methods , Biomedical Research/trends , Databases, Factual/classification , Datasets as Topic/classification , Datasets as Topic/economics , Datasets as Topic/statistics & numerical data , Humans , Models, Economic , Private Sector , United StatesSubject(s)
Biomedical Research/trends , Datasets as Topic/statistics & numerical data , Biomedical Research/economics , Career Mobility , Clinical Trials as Topic , Computational Biology , Confidentiality , Datasets as Topic/economics , Delivery of Health Care/methods , Delivery of Health Care/trends , Electronic Health Records/statistics & numerical data , Electronic Health Records/trends , Humans , Information Storage and Retrieval/economics , Information Storage and Retrieval/trends , Point-of-Care Systems/trends , Research Support as Topic , Reward , Translational Research, Biomedical/trends , WorkforceABSTRACT
BACKGROUND: Over recent years there has been a strong movement towards the improvement of vital statistics and other types of health data that inform evidence-based policies. Collecting such data is not cost free. To date there is no systematic framework to guide investment decisions on methods of data collection for vital statistics or health information in general. We developed a framework to systematically assess the comparative costs and outcomes/benefits of the various data methods for collecting vital statistics. METHODOLOGY: The proposed framework is four-pronged and utilises two major economic approaches to systematically assess the available data collection methods: cost-effectiveness analysis and efficiency analysis. We built a stylised example of a hypothetical low-income country to perform a simulation exercise in order to illustrate an application of the framework. FINDINGS: Using simulated data, the results from the stylised example show that the rankings of the data collection methods are not affected by the use of either cost-effectiveness or efficiency analysis. However, the rankings are affected by how quantities are measured. CONCLUSION: There have been several calls for global improvements in collecting useable data, including vital statistics, from health information systems to inform public health policies. Ours is the first study that proposes a systematic framework to assist countries undertake an economic evaluation of DCMs. Despite numerous challenges, we demonstrate that a systematic assessment of outputs and costs of DCMs is not only necessary, but also feasible. The proposed framework is general enough to be easily extended to other areas of health information.