Validation of a common data model for active safety surveillance research

Overhage, J. Marc; Ryan, Patrick B.; Reich, Christian; Hartzema, Abraham G.; Stang, Paul

doi:10.1136/amiajnl-2011-000376

Cited by 422 publications

(384 citation statements)

References 6 publications

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“…OHDSI's Common Data Model (18), originally developed as part of the Observational Medical Outcomes Partnership (19), is a deep information model that specifies how to encode and store clinical data at a fine-grained level, ensuring that the same query can be applied consistently to databases around the world. OHDSI has chosen data integration standards that dovetail with those of the United States government and the international community, and it also supplies tools and mapping tables for converting data from other standards.…”

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confidence: 99%

Characterizing treatment pathways at scale using the OHDSI network

Hripcsak

Ryan

Duke

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

280

222

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Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research. Understanding the diversity of populations and the variance in care is one component. In this study, the Observational Health Data Sciences and Informatics (OHDSI) collaboration created an international data network with 11 data sources from four countries, including electronic health records and administrative claims data on 250 million patients. All data were mapped to common data standards, patient privacy was maintained by using a distributed model, and results were aggregated centrally. Treatment pathways were elucidated for type 2 diabetes mellitus, hypertension, and depression. The pathways revealed that the world is moving toward more consistent therapy over time across diseases and across locations, but significant heterogeneity remains among sources, pointing to challenges in generalizing clinical trial results. Diabetes favored a single first-line medication, metformin, to a much greater extent than hypertension or depression. About 10% of diabetes and depression patients and almost 25% of hypertension patients followed a treatment pathway that was unique within the cohort. Aside from factors such as sample size and underlying population (academic medical center versus general population), electronic health records data and administrative claims data revealed similar results. Large-scale international observational research is feasible.observational research | data network | treatment pathways A learning health system (1) must systematically evaluate the effects of medical interventions to enable evidence-based medical decision-making. Randomized clinical trials serve as the cornerstone for causal evidence about medical products (2, 3), but evidence from these trials may be limited by an insufficient number of persons exposed, insufficient length of exposure, and inadequate coverage of the target population, factors that limit external generalizability. Observational studies can contribute to the larger goal of causal inference at three stages: (i) the design of experiments, such as determining what are the current therapies that should be compared with a new therapy; (ii) the direct testing of clinical hypotheses on observational data (4-8) using methods to correct for nonrandom treatment assignment as part of the effect estimation process; and (iii) better understanding of population characteristics to improve the extrapolation of both observational and experimental results to new groups.Without sufficiently broad databases available in the first stage, randomized trials are designed without explicit knowledge of actual disease status and treatment practice. Literature reviews are restricted to the population choices of previous investigations, and pilot studi...

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confidence: 99%

Characterizing treatment pathways at scale using the OHDSI network

Hripcsak

Ryan

Duke

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

280

222

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“…We should agree on a common analytic data model (such as OMOP's common data model) into which data from any EHR can be translated. 10 We must agree on definitions of conditions and outcomes, cognizant of messy EHR data and balancing sensitivity and specificity. For example, there are multiple different published definitions of "diabetes" that variably use recorded diagnoses, test -8-results, and pharmacy data that, when applied to a single EHR, produce different sets of patients.…”

Section: Getting Therementioning

confidence: 99%

The Promise of Information and Communication Technology in Healthcare: Extracting Value From the Chaos

Mamlin

Tierney

2016

The American Journal of the Medical Sciences

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Health care is an information business with expanding use of information and communication technologies (ICTs). Current ICT tools are immature, but a brighter future looms in many areas. We examine six areas of ICT in health care: electronic health records (EHRs), health information exchange (HIE), patient portals, telemedicine, social media, and mobile devices and wearable sensors and monitors. In each of these areas, we examine the current status and future promise, highlighting how each might reach its promise.Steps to better EHRs include a universal programming interface, universal patient identifiers, improved documentation, and improved data analysis. HIEs will require federal subsidies for sustainability and support from EHR vendors, targeting seamless sharing of EHR data. Patient portals must bring patients into the EHR with better design and training, greater provider engagement, and leveraging HIEs. Telemedicine needs sustainable payment models, clear rules of engagement, and quality measures and monitoring. Social media needs consensus on rules of engagement for providers, better data mining tools, and approaches to counter disinformation. Mobile and wearable devices will benefit from a universal programming interface, improved infrastructure, more rigorous research, and integration with EHRs and HIEs.ICT tools are are evolving quickly in health care and require a rational and well-funded national agenda for development, use, and assessment.-3-

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“…[1][2][3][4][5][6] Within these diverse research networks, health care data collected at delivery settings such as hospitals or clinics are extracted, transformed, and loaded (ETL) from their local formats to a common structure and semantic, which is often referred to as the Common Data Model (CDM). [7][8][9] The ETL processes to transform data into a CDM are usually resource-intensive and error-prone due to numerous technical challenges. Further, because these challenges are often underestimated by germane data institutions, there tends to be under-investment in ETL.…”

Section: Introductionmentioning

confidence: 99%

“…In order to address site specific differences, data sharing networks define a CDM that delineates single data structures and values that are allowed for each variable. 8,[10][11][12][13] Data contributors are required to transform their local data into the CDM structures in accordance with the precise definitions provided by the CDM developers. In addition to organizational and regulatory requirements, there are numerous technical processes associated with creating a CDM from an existing clinical system.…”

Section: Introductionmentioning

confidence: 99%