A PROCESS FOR ACHIEVING COMPARABLE DATA FROM HETEROGENEOUS DATABASES

The current state of health and biomedicine includes an enormity of heterogeneous data ‘silos’, collected for different purposes and represented differently, that are presently impossible to share or analyze in toto. The greatest challenge for large-scale and meaningful analyses of health-related data is to achieve a uniform data representation for data extracted from heterogeneous source representations. Based upon an analysis and categorization of heterogeneities, a process for achieving comparable data content by using a uniform terminological representation is developed. This process addresses the types of representational heterogeneities that commonly arise in healthcare data integration problems. Specifically, this process uses a reference terminology, and associated maps to transform heterogeneous data to a standard representation for comparability and secondary use. The capture of quality and precision of the “maps” between local terms and reference terminology concepts enhances the meaning of the aggregated data, empowering end users with better-informed queries for subsequent analyses. A data integration case study in the domain of pediatric asthma illustrates the development and use of a reference terminology for creating comparable data from heterogeneous source representations. The contribution of this research is a generalized process for the integration of data from heterogeneous source representations, and this process can be applied and extended to other problems where heterogeneous data needs to be merged

Metadata Standards for Computable Biomedical Knowledge (CBK)

Purpose: Platforms for computable biomedical knowledge are rapidly emerging to accelerate the application of biomedical knowledge into practice. At an inaugural Mobilizing Computable Biomedical Knowledge (MCBK) working meeting held in Ann Arbor, MI on October 18 & 19, 2017, the group took important early steps to: Engage critical dialogue on how to effectively develop and govern platforms for machine-executable biomedical knowledge to improve health and to build a pre-competitive computable biomedical knowledge community. This conference was significant for advancing work in areas that require computable knowledge to translate biomedical insights for better health: Learning Health Systems, Open Biomedical Science, and Clinical Decision Support. Participants explored what will be required to shape and sustain a community focused on making computable biomedical knowledge FAIR: Findable, Accessible, Interoperable and Reusable. Participants discussed biomedical computable knowledge in the context of four, overarching themes, one of which included Metadata Standards for Computable Biomedical Knowledge.https://deepblue.lib.umich.edu/bitstream/2027.42/140743/1/Metadata Standards for Computable Biomedical Knowledge (CBK).pdfDescription of Metadata Standards for Computable Biomedical Knowledge (CBK).pdf : Briefing Pape

Reimagining the research-practice relationship: policy recommendations for informatics-enabled evidence-generation across the US health system

Abstract. The widespread adoption and use of electronic health records and their use to enable learning health systems (LHS) holds great promise to accelerate both evidence-generating medicine (EGM) and evidence-based medicine (EBM), thereby enabling a LHS. In 2016, AMIA convened its 10th annual Policy Invitational to discuss issues key to facilitating the EGM-EBM paradigm at points-of-care (nodes), across organizations (networks), and to ensure viability of this model at scale (sustainability). In this article, we synthesize discussions from the conference and supplements those deliberations with relevant context to inform ongoing policy development. Specifically, we explore and suggest public policies needed to facilitate EGM-EBM activities on a national scale, particularly those policies that can enable and improve clinical and health services research at the point-of-care, accelerate biomedical discovery, and facilitate translation of findings to improve the health of individuals and population

Summary of second annual MCBK public meeting: Mobilizing Computable Biomedical Knowledge—A movement to accelerate translation of knowledge into action

The volume of biomedical knowledge is growing exponentially and much of this knowledge is represented in computer executable formats, such as models, algorithms and programmatic code. There is a growing need to apply this knowledge to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations do not yet have the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are not sufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Second Annual MCBK public meeting, which was held at the National Institutes of Health on July 18‐19, 2019 and brought together over 150 participants from various domains to frame and address important dimensions for mobilizing CBK.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/1/lrh2-sup-0001-supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/2/lrh210222.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/3/lrh210222_am.pd

Desiderata for the development of next-generation electronic health record phenotype libraries

BackgroundHigh-quality phenotype definitions are desirable to enable the extraction of patient cohorts from large electronic health record repositories and are characterized by properties such as portability, reproducibility, and validity. Phenotype libraries, where definitions are stored, have the potential to contribute significantly to the quality of the definitions they host. In this work, we present a set of desiderata for the design of a next-generation phenotype library that is able to ensure the quality of hosted definitions by combining the functionality currently offered by disparate tooling.MethodsA group of researchers examined work to date on phenotype models, implementation, and validation, as well as contemporary phenotype libraries developed as a part of their own phenomics communities. Existing phenotype frameworks were also examined. This work was translated and refined by all the authors into a set of best practices.ResultsWe present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing.ConclusionsThere are a number of choices to be made when constructing phenotype libraries. Our considerations distil the best practices in the field and include pointers towards their further development to support portable, reproducible, and clinically valid phenotype design. The provision of high-quality phenotype definitions enables electronic health record data to be more effectively used in medical domains

The Rare Diseases Clinical Research Network Contact Registry Update: Features and Functionality

The Rare Diseases Clinical Research Network (RDCRN) Contact Registry has grown in size and scope since it was first reported in this journal in 2007. In this paper, we reflect on our seven years\u27 experience developing and expanding the RDCRN Contact Registry to include many more rare diseases. We present the functional and data requirements that motivated this registry, and the new features and policies that have been developed since. Given the high costs and long-term commitment required to build patient registries, the RDCRN Contact Registry experience represents a reasonable approach for identifying and cultivating potential research populations, with minimal resources and patient burden. The basic model of a patient-reported registry has not changed since our 2007 report, but the number of diseases has grown from 42 to 201, and the types of information that are exchanged with participants has expanded. A patient-directed information-sharing feature has been added to reduce barriers to communication between investigators and patients affected by rare and genetic diseases. As specific data and research needs arise, the Contact Registry can be leveraged to access needed data or to solicit patients for particular research opportunities. This multiple-disease registry is scalable, expandable, and standards-driven, and has become a model for clinical and translational research across rare and many other diseases