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A Digital Architecture for a Network-Based Learning Health System: Integrating Chronic Care Management, Quality Improvement, and Research.

Marsolo K, Margolis PA, Forrest CB, Colletti RB, Hutton JJ - EGEMS (Wash DC) (2015)

Bottom Line: Additional standards are needed in order for this vision to be achieved, however.We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build.We have also highlighted opportunities where sponsors could help accelerate progress.

View Article: PubMed Central - PubMed

Affiliation: Cincinnati Children's Hospital Medical Center.

ABSTRACT

Introduction: We collaborated with the ImproveCareNow Network to create a proof-of-concept architecture for a network-based Learning Health System. This collaboration involved transitioning an existing registry to one that is linked to the electronic health record (EHR), enabling a "data in once" strategy. We sought to automate a series of reports that support care improvement while also demonstrating the use of observational registry data for comparative effectiveness research.

Description of architecture: We worked with three leading EHR vendors to create EHR-based data collection forms. We automated many of ImproveCareNow's analytic reports and developed an application for storing protected health information and tracking patient consent. Finally, we deployed a cohort identification tool to support feasibility studies and hypothesis generation. There is ongoing uptake of the system. To date, 31 centers have adopted the EHR-based forms and 21 centers are uploading data to the registry. Usage of the automated reports remains high and investigators have used the cohort identification tools to respond to several clinical trial requests.

Suggestions for future use: The current process for creating EHR-based data collection forms requires groups to work individually with each vendor. A vendor-agnostic model would allow for more rapid uptake. We believe that interfacing network-based registries with the EHR would allow them to serve as a source of decision support. Additional standards are needed in order for this vision to be achieved, however.

Conclusions: We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build. We have also highlighted opportunities where sponsors could help accelerate progress.

No MeSH data available.


Related in: MedlinePlus

Example i2b2 Interface Used for Cohort IdentificationNote: This query is intended to find all males with a current diagnosis of Crohn’s disease who have ever been on a biologic.
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f7-egems1168: Example i2b2 Interface Used for Cohort IdentificationNote: This query is intended to find all males with a current diagnosis of Crohn’s disease who have ever been on a biologic.

Mentions: One of the key elements in positioning ImproveCareNow as a Learning Health System was to increase the network’s capacity to engage in research, particularly as it relates to clinical trials. To support these endeavors, we integrated cohort identification tools with the registry database, allowing users to quickly generate hypotheses and determine study feasibility. The registry supports two levels of cohort identification. Using a custom version of the i2b2 workbench36,37 (Figure 7), care centers can run queries against their own patient population, generating aggregate results with the ability to drill down into patient level data. We have also created a virtual Shared Health Research Information Network (SHRINE) among the i2b2 databases hosted at the data coordinating center,36–39 which allows authorized users to generate aggregate numbers for the network as a whole through a custom version of the SHRINE workbench, which is very similar to the i2b2 workbench, but lacks the ability to drill down into patient-level data.


A Digital Architecture for a Network-Based Learning Health System: Integrating Chronic Care Management, Quality Improvement, and Research.

Marsolo K, Margolis PA, Forrest CB, Colletti RB, Hutton JJ - EGEMS (Wash DC) (2015)

Example i2b2 Interface Used for Cohort IdentificationNote: This query is intended to find all males with a current diagnosis of Crohn’s disease who have ever been on a biologic.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4562738&req=5

f7-egems1168: Example i2b2 Interface Used for Cohort IdentificationNote: This query is intended to find all males with a current diagnosis of Crohn’s disease who have ever been on a biologic.
Mentions: One of the key elements in positioning ImproveCareNow as a Learning Health System was to increase the network’s capacity to engage in research, particularly as it relates to clinical trials. To support these endeavors, we integrated cohort identification tools with the registry database, allowing users to quickly generate hypotheses and determine study feasibility. The registry supports two levels of cohort identification. Using a custom version of the i2b2 workbench36,37 (Figure 7), care centers can run queries against their own patient population, generating aggregate results with the ability to drill down into patient level data. We have also created a virtual Shared Health Research Information Network (SHRINE) among the i2b2 databases hosted at the data coordinating center,36–39 which allows authorized users to generate aggregate numbers for the network as a whole through a custom version of the SHRINE workbench, which is very similar to the i2b2 workbench, but lacks the ability to drill down into patient-level data.

Bottom Line: Additional standards are needed in order for this vision to be achieved, however.We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build.We have also highlighted opportunities where sponsors could help accelerate progress.

View Article: PubMed Central - PubMed

Affiliation: Cincinnati Children's Hospital Medical Center.

ABSTRACT

Introduction: We collaborated with the ImproveCareNow Network to create a proof-of-concept architecture for a network-based Learning Health System. This collaboration involved transitioning an existing registry to one that is linked to the electronic health record (EHR), enabling a "data in once" strategy. We sought to automate a series of reports that support care improvement while also demonstrating the use of observational registry data for comparative effectiveness research.

Description of architecture: We worked with three leading EHR vendors to create EHR-based data collection forms. We automated many of ImproveCareNow's analytic reports and developed an application for storing protected health information and tracking patient consent. Finally, we deployed a cohort identification tool to support feasibility studies and hypothesis generation. There is ongoing uptake of the system. To date, 31 centers have adopted the EHR-based forms and 21 centers are uploading data to the registry. Usage of the automated reports remains high and investigators have used the cohort identification tools to respond to several clinical trial requests.

Suggestions for future use: The current process for creating EHR-based data collection forms requires groups to work individually with each vendor. A vendor-agnostic model would allow for more rapid uptake. We believe that interfacing network-based registries with the EHR would allow them to serve as a source of decision support. Additional standards are needed in order for this vision to be achieved, however.

Conclusions: We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build. We have also highlighted opportunities where sponsors could help accelerate progress.

No MeSH data available.


Related in: MedlinePlus