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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.


Number of Patients Whose EHR Data Have Been Uploaded to the RegistryNotes: The “Goal” line represents the target set as part of the 18-month grant extension, which was 75 percent of ImproveCareNow’s patient population at the time of submission. The “Number Patients” line represents the number of patients whose EHR data have been uploaded.
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f8-egems1168: Number of Patients Whose EHR Data Have Been Uploaded to the RegistryNotes: The “Goal” line represents the target set as part of the 18-month grant extension, which was 75 percent of ImproveCareNow’s patient population at the time of submission. The “Number Patients” line represents the number of patients whose EHR data have been uploaded.

Mentions: The number of centers that are transferring EHR data to the registry is lower than the number that are collecting data in the EHR because of a lag between the form implementation and the extraction process implementation, but we are seeing steady increases. At the end of the initial project in August 2013, only 3 centers were transferring data to the registry. By the end of the grant extension 18 months later, we had seen a seven-fold increase, to 21 centers. Figure 8 represents the progress that the network has made in enabling the transfer of EHR 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)

Number of Patients Whose EHR Data Have Been Uploaded to the RegistryNotes: The “Goal” line represents the target set as part of the 18-month grant extension, which was 75 percent of ImproveCareNow’s patient population at the time of submission. The “Number Patients” line represents the number of patients whose EHR data have been uploaded.
© Copyright Policy
Related In: Results  -  Collection

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

f8-egems1168: Number of Patients Whose EHR Data Have Been Uploaded to the RegistryNotes: The “Goal” line represents the target set as part of the 18-month grant extension, which was 75 percent of ImproveCareNow’s patient population at the time of submission. The “Number Patients” line represents the number of patients whose EHR data have been uploaded.
Mentions: The number of centers that are transferring EHR data to the registry is lower than the number that are collecting data in the EHR because of a lag between the form implementation and the extraction process implementation, but we are seeing steady increases. At the end of the initial project in August 2013, only 3 centers were transferring data to the registry. By the end of the grant extension 18 months later, we had seen a seven-fold increase, to 21 centers. Figure 8 represents the progress that the network has made in enabling the transfer of EHR 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.