Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre, Stéphane M.; Lovis, Christian; Bürkle, Thomas; Tognola, Gabriella; Budrionis, Andrius; Lehmann, Christoph U.

doi:10.15265/iy-2017-007

Cited by 155 publications

(110 citation statements)

References 156 publications

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“…SRA and GEO are truly useful projects, enabling sharing and exchange of 'omic' data, which is important for data reuse [17]. An example demonstrates reuse of large amounts of data (over 5000 gene expression data-sets from GEO), where the different studies were integrated and revealed the overall structure of gene expression space across different tissues, which could not have been observed using any of the contributing studies individually [18].…”

Section: Discussionmentioning

confidence: 99%

SpiderSeqR: an R package for crawling the web of high-throughput multi-omic data repositories for data-sets and annotation

Sozanska

Fletcher

Bihary

et al. 2020

Preprint

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More than three decades ago, the microarray revolution brought about high-throughput data generation capability to biology and medicine. Subsequently, the emergence of massively parallel sequencing technologies led to many big-data initiatives such as the human genome project and the encyclopedia of DNA elements (ENCODE) project. These, in combination with cheaper, faster massively parallel DNA sequencing capabilities, have democratised multi-omic (genomic, transcriptomic, translatomic and epigenomic) data generation leading to a data deluge in bio-medicine. While some of these data-sets are trapped in inaccessible silos, the vast majority of these data-sets are stored in public data resources and controlled access data repositories, enabling their wider use (or misuse). Currently, most peer reviewed publications require the deposition of the data-set associated with a study under consideration in one of these public data repositories. However, clunky and difficult to use interfaces, subpar or incomplete annotation prevent discovering, searching and filtering of these multi-omic data and hinder their re-purposing in other use cases. In addition, the proliferation of multitude of different data repositories, with partially redundant storage of similar data are yet another obstacle to their continued usefulness. Similarly, interfaces where annotation is spread across multiple web pages, use of accession identifiers with ambiguous and multiple interpretations and lack of good curation make these data-sets difficult to use. We have produced SpiderSeqR, an R package, whose main features include the integration between NCBI GEO and SRA databases, enabling an integrated unified search of SRA and GEO data-sets and associated annotations, conversion between database accessions, as well as convenient filtering of results and saving past queries for future use. All of the above features aim to promote data reuse to facilitate making new discoveries and maximising the potential of existing data-sets.

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Section: Discussionmentioning

confidence: 99%

SpiderSeqR: an R package for crawling the web of high-throughput multi-omic data repositories for data-sets and annotation

Sozanska

Fletcher

Bihary

et al. 2020

Preprint

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“…S.M. Meystre, C. Lovis, T. Bürkle, et al [15]; Y. Wang, L. Wang, M. Rastegar-Mojarad, et al [16] showed another fundamental feature of the EHR data extraction. This internal structural complexity determines the relationship between survey data and medical prescriptions (procedures, medications), taking into account the chronology of events, etc.…”

Section: Big Biomedical Data Mining: Related Workmentioning

confidence: 99%

Infrastructure of the Electronic Health Record Data Management for Digital Patient Phenotype Creating

Zakharov¹,

Потапов²,

Zakharova³

et al. 2019

Proceedings of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019)

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improving the health care system requires the effective use of digitized biomedical data already accumulated and constantly updated due to the widespread introduction of applied information systems. One of the priorities here is the creation of a patient's digital phenotype based on data from an electronic health record (EHR). To solve this problem, it is necessary to rely on an ecosystem that provides secure storage, processing and analysis of large volumes of heterogeneous information (tables, images, texts in natural language). The main goal of the article is to study the possibilities of Big Data methods and technologies for the reuse of digitized biomedical data. We have considered designing and implementing of the EHR data management infrastructure that provides tools for digital patient phenotype creating. We designed the data lake prototype "5P Medicine-Big Data" based on the original "Big Data to Smart Data" (BD2SD) multi-layer approach. We have developed an information repository based on the generalized NoSQL approach and the "document repository" model, as well as the system of services. These services provide solutions for the secure data transfer from medical information systems, data storage, validation of information, preliminary data analysis and visualization, data extraction from unstructured documents, and sampling for the machine and deep learning methods. We offered methods for analyzing a significant amount of EHR based on machine learning and Big Data technologies. We applied these methods to extract valid information from unstructured EHR data (first of all, patient examination protocols) and identify characteristic patient categories. We constructed digital patient phenotype, represented by exactly those features extracted from the EHR data that are key in the context of a specific medical problem. Digital phenotypes were formed based on more than 30,000 medical texts for about 2,000 patients. We have shown the effectiveness of the proposed approach by examining the problem of identifying patterns in the patient's visits to a doctor before and after the cardiovascular diseases (angina pectoris, myocardial infarction, and ischemic heart disease) appeared in the electronic health record.

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“…High potential is expected from the reuse of such structured clinical data for secondary research purposes, for example when applied in epidemiological and health economic research, comparative effectiveness research, or health care quality improvement (2)(3)(4). The establishment of learning health care systems (LHCS) and the development of precision medicine are further examples of promising developments (5,6).…”

Section: Introductionmentioning

confidence: 99%

Implementation of data access and use procedures in clinical data warehouses. A systematic review of literature and publicly available policies

Pavlenko

Strech

Langhof

2020

Preprint

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Background The promises of improved health care and health research through data-intensive applications rely on a growing amount of health data. At the core of large-scale data integration efforts, clinical data warehouses (CDW) are also responsible of data governance, managing data access and (re)use. As the complexity of the data flow increases, greater transparency and standardization of criteria and procedures is required in order to maintain objective oversight and control. This study assessed the spectrum of data access and use criteria and procedures in clinical data warehouses governance internationally. Methods We performed a systematic review of (a) the published scientific literature on CDW and (b) publicly available information on CDW data access, e.g., data access policies. A qualitative thematic analysis was applied to all included literature and policies. Results Twenty-three scientific publications and one policy document were included in the final analysis. The qualitative analysis led to a final set of three main thematic categories: (1) requirements, including recipient requirements, reuse requirements, and formal requirements; (2) structures and processes, including review bodies and review values; and (3) access, including access limitations. Conclusions The description of data access and use governance in the scientific literature is characterized by a high level of heterogeneity and ambiguity. In practice, this might limit the effective data sharing needed to fulfil the high expectations of data-intensive approaches in medical research and health care. The lack of publicly available information on access policies conflicts with ethical requirements linked to principles of transparency and accountability. CDW should publicly disclose by whom and under which conditions data can be accessed, and provide designated governance structures and policies to increase transparency on data access. The results of this review may contribute to the development of practice-oriented minimal standards for the governance of data access, which could also result in a stronger harmonization, efficiency, and effectiveness of CDW.

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Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Cited by 155 publications

References 156 publications

SpiderSeqR: an R package for crawling the web of high-throughput multi-omic data repositories for data-sets and annotation

SpiderSeqR: an R package for crawling the web of high-throughput multi-omic data repositories for data-sets and annotation

Infrastructure of the Electronic Health Record Data Management for Digital Patient Phenotype Creating

Implementation of data access and use procedures in clinical data warehouses. A systematic review of literature and publicly available policies

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