Distributed Health Data Networks

Brown, Jeffrey S.; Holmes, John H.; Shah, Kiran; Hall, Ken; Lazarus, Ross; Platt, Richard

doi:10.1097/mlr.0b013e3181d9919f

Cited by 185 publications

(54 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Brown et al [22] present a "distributed" query system designed to allow data holds to maintain physical control of their data. This system is contrasted with a centralized database, where users submit queries outside of their local firewalls (and also receive results from a remote server).…”

Section: B Related Workmentioning

confidence: 99%

A Quantitative Approach for Evaluating the Utility of a Differentially Private Behavioral Science Dataset

Hill

Hansen

Janssen

et al. 2014

2014 IEEE International Conference on Healthcare Informatics

View full text Add to dashboard Cite

Objective: Social scientists who collect large amounts of medical data value the privacy of their survey participants. As they follow participants through longitudinal studies, they develop unique profiles of these individuals. A growing challenge for these researchers is to maintain the privacy of their study participants, while sharing their data to facilitate research. Differential privacy is a new mechanism which promises improved privacy guarantees for statistical databases. We evaluate the utility of a differentially private dataset. Our results align with the theory of differential privacy and show when the number of records in the database is sufficiently larger than the number of cells covered by a database query, the number of statistical tests with results close to those performed on original data increases.

show abstract

Section: B Related Workmentioning

confidence: 99%

A Quantitative Approach for Evaluating the Utility of a Differentially Private Behavioral Science Dataset

Hill

Hansen

Janssen

et al. 2014

2014 IEEE International Conference on Healthcare Informatics

View full text Add to dashboard Cite

show abstract

“…In most DRNs, data are housed locally and standardized to a common data model (CDM). Analysis programs, or queries, are distributed to the network partners, and results are sent back to the requestor [1011]. …”

Section: Introductionmentioning

confidence: 99%

Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet®)

Qualls¹,

Phillips²,

Hammill³

et al. 2018

eGEMs

Self Cite

View full text Add to dashboard Cite

Introduction:Distributed research networks (DRNs) are critical components of the strategic roadmaps for the National Institutes of Health and the Food and Drug Administration as they work to move toward large-scale systems of evidence generation. The National Patient-Centered Clinical Research Network (PCORnet®) is one of the first DRNs to incorporate electronic health record data from multiple domains on a national scale. Before conducting analyses in a DRN, it is important to assess the quality and characteristics of the data.Methods:PCORnet’s Coordinating Center is responsible for evaluating foundational data quality, or assessing fitness-for-use across a broad research portfolio, through a process called data curation. Data curation involves a set of analytic and querying activities to assess data quality coupled with maintenance of detailed documentation and ongoing communication with network partners. The first cycle of PCORnet data curation focused on six domains in the PCORnet common data model: demographics, diagnoses, encounters, enrollment, procedures, and vitals.Results:The data curation process led to improvements in foundational data quality. Notable improvements included the elimination of data model conformance errors; a decrease in implausible height, weight, and blood pressure values; an increase in the volume of diagnoses and procedures; and more complete data for key analytic variables. Based on the findings of the first cycle, we made modifications to the curation process to increase efficiencies and further reduce variation among data partners.Discussion:The iterative nature of the data curation process allows PCORnet to gradually increase the foundational level of data quality and reduce variability across the network. These activities help increase the transparency and reproducibility of analyses within PCORnet and can serve as a model for other DRNs.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Concerns about data security, patient privacy, unapproved use of data, and disclosure of proprietary information have limited these collaborations [123]. Data organized in a distributed data network (DDN) can mitigate these concerns [123]. Several DDNs already exist and have been used to investigate a wide range of clinical inquires in a distributed matter, including the Centers for Disease Control and Prevention’s Vaccine Safety Datalink [4], the National Institutes of Health (NIH)’s Health Care Systems Research Collaboratory [5], the U.S. Food and Drug Administration (FDA)’s Sentinel System [6], and the Patient-Centered Outcomes Research Institute (PCORI)’s National Patient-Centered Clinical Research Network (PCORnet) [7].…”

Section: Introductionmentioning

confidence: 99%

“…Several DDNs already exist and have been used to investigate a wide range of clinical inquires in a distributed matter, including the Centers for Disease Control and Prevention’s Vaccine Safety Datalink [4], the National Institutes of Health (NIH)’s Health Care Systems Research Collaboratory [5], the U.S. Food and Drug Administration (FDA)’s Sentinel System [6], and the Patient-Centered Outcomes Research Institute (PCORI)’s National Patient-Centered Clinical Research Network (PCORnet) [7]. These networks allow data partners to retain physical control of their data while making multi-database analysis more secure and feasible [123]. …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Query Workflow Design to Perform Automatable Distributed Regression Analysis in Large Distributed Data Networks

Her¹,

Malenfant²,

Malek³

et al. 2018

eGEMs

View full text Add to dashboard Cite

Introduction:Patient privacy and data security concerns often limit the feasibility of pooling patient-level data from multiple sources for analysis. Distributed data networks (DDNs) that employ privacy-protecting analytical methods, such as distributed regression analysis (DRA), can mitigate these concerns. However, DRA is not routinely implemented in large DDNs.Objective:We describe the design and implementation of a process framework and query workflow that allow automatable DRA in real-world DDNs that use PopMedNet™, an open-source distributed networking software platform.Methods:We surveyed and catalogued existing hardware and software configurations at all data partners in the Sentinel System, a PopMedNet-driven DDN. Key guiding principles for the design included minimal disruptions to the current PopMedNet query workflow and minimal modifications to data partners’ hardware configurations and software requirements.Results:We developed and implemented a three-step process framework and PopMedNet query workflow that enables automatable DRA: 1) assembling a de-identified patient-level dataset at each data partner, 2) distributing a DRA package to data partners for local iterative analysis, and 3) iteratively transferring intermediate files between data partners and analysis center. The DRA query workflow is agnostic to statistical software, accommodates different regression models, and allows different levels of user-specified automation.Discussion:The process framework can be generalized to and the query workflow can be adopted by other PopMedNet-based DDNs.Conclusion:DRA has great potential to change the paradigm of data analysis in DDNs. Successful implementation of DRA in Sentinel will facilitate adoption of the analytic approach in other DDNs.

show abstract

Distributed Health Data Networks

Abstract: Distributed networks are capable of addressing nearly all anticipated uses of routinely collected electronic healthcare data. Distributed networks would obviate the need for centralized databases, thus avoiding numerous obstacles.

Cited by 185 publications

References 18 publications

A Quantitative Approach for Evaluating the Utility of a Differentially Private Behavioral Science Dataset

A Quantitative Approach for Evaluating the Utility of a Differentially Private Behavioral Science Dataset

Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet®)

A Query Workflow Design to Perform Automatable Distributed Regression Analysis in Large Distributed Data Networks

Contact Info

Product

Resources

About