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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.


Initial Version of the Pre-Visit Planning ReportNote: Diagnosis and phenotype data are provided in the top section, followed with selected values from previous visits. Lab results that are out-of-date based on the patient’s treatment regimen are labeled in pink. The patient’s risk stratification score is listed in the next section, followed by information on any medications or treatments they are taking. If a medication was not dosed according to the specified guideline, a note would be listed in the “Attention Needed” column (bottom right).
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f4-egems1168: Initial Version of the Pre-Visit Planning ReportNote: Diagnosis and phenotype data are provided in the top section, followed with selected values from previous visits. Lab results that are out-of-date based on the patient’s treatment regimen are labeled in pink. The patient’s risk stratification score is listed in the next section, followed by information on any medications or treatments they are taking. If a medication was not dosed according to the specified guideline, a note would be listed in the “Attention Needed” column (bottom right).

Mentions: The network employs a number of reports to support its chronic care management, QI, and research activities. These reports are described in Table 4. They are generated using Microsoft SQL Server Reporting Services (SSRS), an enterprise-level reporting tool. The look and feel of the QI and data quality reports reflect past experience and best practices in process improvement. The content and format of the care management reports (population management and pre-visit planning) were modeled on existing paper and Microsoft Excel-based reports. Examples of the reports are shown in Figures 3 and 4 below.


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)

Initial Version of the Pre-Visit Planning ReportNote: Diagnosis and phenotype data are provided in the top section, followed with selected values from previous visits. Lab results that are out-of-date based on the patient’s treatment regimen are labeled in pink. The patient’s risk stratification score is listed in the next section, followed by information on any medications or treatments they are taking. If a medication was not dosed according to the specified guideline, a note would be listed in the “Attention Needed” column (bottom right).
© Copyright Policy
Related In: Results  -  Collection

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

f4-egems1168: Initial Version of the Pre-Visit Planning ReportNote: Diagnosis and phenotype data are provided in the top section, followed with selected values from previous visits. Lab results that are out-of-date based on the patient’s treatment regimen are labeled in pink. The patient’s risk stratification score is listed in the next section, followed by information on any medications or treatments they are taking. If a medication was not dosed according to the specified guideline, a note would be listed in the “Attention Needed” column (bottom right).
Mentions: The network employs a number of reports to support its chronic care management, QI, and research activities. These reports are described in Table 4. They are generated using Microsoft SQL Server Reporting Services (SSRS), an enterprise-level reporting tool. The look and feel of the QI and data quality reports reflect past experience and best practices in process improvement. The content and format of the care management reports (population management and pre-visit planning) were modeled on existing paper and Microsoft Excel-based reports. Examples of the reports are shown in Figures 3 and 4 below.

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.