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

Utilizing RxNorm to Support Practical Computing Applications: Capturing Medication History in Live Electronic Health Records

RxNorm was utilized as the basis for direct-capture of medication history data in a live EHR system deployed in a large, multi-state outpatient behavioral healthcare provider in the United States serving over 75,000 distinct patients each year across 130 clinical locations. This tool incorporated auto-complete search functionality for medications and proper dosage identification assistance. The overarching goal was to understand if and how standardized terminologies like RxNorm can be used to support practical computing applications in live EHR systems. We describe the stages of implementation, approaches used to adapt RxNorm's data structure for the intended EHR application, and the challenges faced. We evaluate the implementation using a four-factor framework addressing flexibility, speed, data integrity, and medication coverage. RxNorm proved to be functional for the intended application, given appropriate adaptations to address high-speed input/output (I/O) requirements of a live EHR and the flexibility required for data entry in multiple potential clinical scenarios. Future research around search optimization for medication entry, user profiling, and linking RxNorm to drug classification schemes holds great potential for improving the user experience and utility of medication data in EHRs.Comment: Appendix (including SQL/DDL Code) available by author request. Keywords: RxNorm; Electronic Health Record; Medication History; Interoperability; Unified Medical Language System; Search Optimizatio

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

Patient Recruitment into a Multicenter Randomized Clinical Trial for Kidney Disease: Report of the Focal Segmental Glomerulosclerosis Clinical Trial (FSGS CT)

We describe the experience of the focal segmental glomerulosclerosis clinical trial (FSGS CT) in the identification and recruitment of participants into the study. This National Institutes of Health funded study, a multicenter, open‐label, randomized comparison of cyclosporine versus oral dexamethasone pulses plus mycophenolate mofetil, experienced difficulty and delays meeting enrollment goals. These problems occurred despite the support of patient advocacy groups and aggressive recruitment strategies. Multiple barriers were identified including: (1) inaccurate estimates of the number of potential incident FSGS patients at participating centers; (2) delays in securing one of the test agents; (3) prolonged time between IRB approval and execution of a subcontract (mean 7.5 ± 0.8 months); (4) prolonged time between IRB approval and enrollment of the first patient at participating sites (mean 19.6 ± 1.4 months); and (5) reorganization of clinical coordinating core infrastructure to align resources with enrollment. A Web‐based anonymous survey of site investigators revealed site‐related barriers to patient recruitment. The value of a variety of recruitment tools was of marginal utility in facilitating patient enrollment. We conclude that improvements in the logistics of study approval and regulatory start‐up and testing of promising novel agents are important factors in promoting enrollment into randomized clinical trials in nephrology. Clin Trans Sci 2013; Volume 6: 13–20Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96741/1/cts12003.pd

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

Background High-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. Methods A 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. Results We present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing. Conclusions There 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

Hadron therapy information sharing prototype

The European PARTNER project developed a prototypical system for sharing hadron therapy data. This system allows doctors and patients to record and report treatment-related events during and after hadron therapy. It presents doctors and statisticians with an integrated view of adverse events across institutions, using open-source components for data federation, semantics, and analysis. There is a particular emphasis upon semantic consistency, achieved through intelligent, annotated form designs. The system as presented is ready for use in a clinical setting, and amenable to further customization. The essential contribution of the work reported here lies in the novel data integration and reporting methods, as well as the approach to software sustainability achieved through the use of community-supported open-source components

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