Learning to Share Health Care Data: A Brief Timeline of Influential Common Data
            Models and Distributed Health Data Networks in U.S. Health Care Research

Weeks, John; Pardee, Roy

doi:10.5334/egems.279

Cited by 58 publications

(45 citation statements)

References 9 publications

(7 reference statements)

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“…The VDW is one of the most influential common data models and one of the most distributed health data networks in the United States for health care research among millions of patients for more than 20 years, and it preceded and served as a model for the US Food and Drug Administration's Sentinel and PCORnet data models. 36 But even with the VDW's depth and breadth of data capture, our results indicate that currently health systems in the CRN are unable to efficiently capture molecular testing use or results at the population level for the majority of cancer types. On a patient-by-patient basis, the scanned image of the test can be opened and read and is thus usable for clinical care.…”

Section: Discussionmentioning

confidence: 80%

Clinical Molecular Marker Testing Data Capture to Promote Precision Medicine Research Within the Cancer Research Network

Burnett‐Hartman

Udaltsova

Kushi

et al. 2019

JCO Clinical Cancer Informatics

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PURPOSE To evaluate health care systems for the availability of population-level data on the frequency of use and results of clinical molecular marker tests to inform precision cancer care. METHODS We assessed cancer-related molecular marker test data availability across 12 US health care systems in the Cancer Research Network. Overall, these systems provide care to a diverse population of more than 12 million people in the United States. We performed qualitative analyses of test data availability for five blood-based protein, nine germline, and 14 tissue-based tumor marker tests in each health care system’s electronic health record and tumor registry using key informants, test code lists, and manual review of data types and output. We then performed quantitative analyses to estimate the proportion of patients with cancer with test utilization data and results for specific molecular marker tests. RESULTS Health systems were able to systematically capture population-level data on all five blood protein markers, six of 14 tissue-based tumor markers, and none of the nine germline markers. Successful, systematic data capture was achievable for tests with electronic data feeds for test results (blood protein markers) or through prior manual abstraction by tumor registrars (select tumor-based markers). For test results stored in scanned image files (particularly germline and tumor marker tests), information on which test was performed and test results was not readily accessible in an electronic format. CONCLUSION Even in health care systems with sophisticated electronic health records, there were few codified data elements available for evaluating precision cancer medicine test use and results at the population level. Health care organizations should establish standards for electronic reporting of precision medicine tests to expedite cancer research and facilitate the implementation of precision medicine approaches.

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Section: Discussionmentioning

confidence: 80%

Clinical Molecular Marker Testing Data Capture to Promote Precision Medicine Research Within the Cancer Research Network

Burnett‐Hartman

Udaltsova

Kushi

et al. 2019

JCO Clinical Cancer Informatics

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“…Over the last few years, several common data models (CDMs) have been implemented in the health care field. [1][2][3][4][5][6][7] These data models are designed to integrate data into a common structure, even when data have been collected through distinct and heterogeneous systems. They enable data exchange, sharing, and storage.…”

Section: Background and Significancementioning

confidence: 99%

Transforming French Electronic Health Records into the Observational Medical Outcome Partnership's Common Data Model: A Feasibility Study

et al. 2020

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Background Common data models (CDMs) enable data to be standardized, and facilitate data exchange, sharing, and storage, particularly when the data have been collected via distinct, heterogeneous systems. Moreover, CDMs provide tools for data quality assessment, integration into models, visualization, and analysis. The observational medical outcome partnership (OMOP) provides a CDM for organizing and standardizing databases. Common data models not only facilitate data integration but also (and especially for the OMOP model) extends the range of available statistical analyses. Objective This study aimed to evaluate the feasibility of implementing French national electronic health records in the OMOP CDM. Methods The OMOP's specifications were used to audit the source data, specify the transformation into the OMOP CDM, implement an extract–transform–load process to feed data from the French health care system into the OMOP CDM, and evaluate the final database. Results Seventeen vocabularies corresponding to the French context were added to the OMOP CDM's concepts. Three French terminologies were automatically mapped to standardized vocabularies. We loaded nine tables from the OMOP CDM's “standardized clinical data” section, and three tables from the “standardized health system data” section. Outpatient and inpatient data from 38,730 individuals were integrated. The median (interquartile range) number of outpatient and inpatient stays per patient was 160 (19–364). Conclusion Our results demonstrated that data from the French national health care system can be integrated into the OMOP CDM. One of the main challenges was the use of international OMOP concepts to annotate data recorded in a French context. The use of local terminologies was an obstacle to conceptual mapping; with the exception of an adaptation of the International Classification of Diseases 10th Revision, the French health care system does not use international terminologies. It would be interesting to extend our present findings to the 65 million people registered in the French health care system.

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“…Since the results from the siMICE method are the same as the results from the standard MICE using pooled data, the latter is omitted from Fig. 1, and the results from the siMICE method are (1) n (2) n (3) n (4) n (5) n (6) n (7) n (8) n (9) n (10) N is the total number of samples. See Table 1 for local sample sizes.…”

Section: Resultsmentioning

confidence: 99%

“…To address these challenges, distributed health data networks (DHDNs) that can store and analyze EHRs data from multiple sites without sharing individual-level data have drawn increasing interests in recent years 7 , 8 . Examples of DHDNs 9 , include the vaccine safety datalink, the health care systems research network, the sentinel initiative, and most recently the patient-centered SCAlable national network for effectiveness research (pSCANNER) 10 that is part of PCORnet. To enhance scalability and privacy protection in distributed analysis, the PopMedNet platform 11 , 12 has been developed to provide software enabled governance over shared data.…”

Section: Introductionmentioning

confidence: 99%

Multiple imputation for analysis of incomplete data in distributed health data networks

et al. 2020

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Distributed health data networks (DHDNs) leverage data from multiple sources or sites such as electronic health records (EHRs) from multiple healthcare systems and have drawn increasing interests in recent years, as they do not require sharing of subject-level data and hence lower the hurdles for collaboration between institutions considerably. However, DHDNs face a number of challenges in data analysis, particularly in the presence of missing data. The current state-of-the-art methods for handling incomplete data require pooling data into a central repository before analysis, which is not feasible in DHDNs. In this paper, we address the missing data problem in distributed environments such as DHDNs that has not been investigated previously. We develop communication-efficient distributed multiple imputation methods for incomplete data that are horizontally partitioned. Since subject-level data are not shared or transferred outside of each site in the proposed methods, they enhance protection of patient privacy and have the potential to strengthen public trust in analysis of sensitive health data. We investigate, through extensive simulation studies, the performance of these methods. Our methods are applied to the analysis of an acute stroke dataset collected from multiple hospitals, mimicking a DHDN where health data are horizontally partitioned across hospitals and subject-level data cannot be shared or sent to a central data repository.

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Learning to Share Health Care Data: A Brief Timeline of Influential Common Data Models and Distributed Health Data Networks in U.S. Health Care Research

Cited by 58 publications

References 9 publications

Clinical Molecular Marker Testing Data Capture to Promote Precision Medicine Research Within the Cancer Research Network

Clinical Molecular Marker Testing Data Capture to Promote Precision Medicine Research Within the Cancer Research Network

Transforming French Electronic Health Records into the Observational Medical Outcome Partnership's Common Data Model: A Feasibility Study

Multiple imputation for analysis of incomplete data in distributed health data networks

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