Domain

Governance

Type

Conceptual or Process Model/Framework

Theme

population

Start Date

7-6-2014 1:15 PM

End Date

7-6-2014 2:45 PM

Structured Abstract

Background:

The emergence of big data in healthcare is inevitable. The growing demand for big data has been empowered by major improvements in data sharing, steady rise of Electronic Health Record adoption, and supporting federal policies and incentives. The appetite for big data has already reached community-wide population health initiatives partly due to the recent roll out of Accountable Care Organizations (ACO). These policies and technological advancements have created a unique ‘landscape’ to draw upon big data for a ‘greener’ population health delivery system.

Study Overview:

This presentation reviews a list of initiatives that JHMI has established to propel the use of big data in improving the population health of their patient community. This list is then mapped against potential sources of Big Data in population health management to identify gaps and opportunities that are applicable to other academic medical centers as well. Furthermore, a conceptual framework is proposed and then contextualized for JHMI to identify stages involved in translating Big Data into effective population healthcare policies.

Findings:

After applying the proposed conceptual big data population health framework at JHMI, the following items were identified as critical to overcome the barriers in translating big data into community-wide outcomes: (a) increasing the coordination of activities among all centers and institutes through a ‘Population Health Big Data Committee’ operating under the ACO management; (b) aligning incentives for participating units, centers and institutions to form a big data eco-system; and, (c) promote the culture of sharing in which population-level clinical data can be accessed and studied by researchers for the purpose of quality improvement, while research findings can be swiftly translated into population solutions and deployed back into operations.

Policy Implications:

The federal government has supported the big data movement in various domains through a number of policies. Probably the most impactful federal policies and legislations that have spurred the use of big data in community-wide population-health are initiatives that have no direct aim at big data (such as ACA and the HITECH Act through Meaningful Use and HIE initiatives). Federal government can empower the use of big data for improved population health by: (a) Funding dedicated projects and/or open calls for translational population health research using big data; (2) Expanding the current funding opportunities such as CMMI to include the use of big data for community-wide population health; (3) Establish an ‘Office of Big Data’ with various committees, including population health, at ONC to coordinate big data efforts in healthcare; (4) Incorporating community-wide population health measures in the future stages of Meaningful Use objectives and also upcoming EHR certification criteria.

States have less control on the overall big data policies; however, healthcare delivery and payment models can change the landscape of big data in population health. For example, in Maryland, the All-Payer model and state-wide PCMH have empowered providers to share data to improve population health. Results of such big data initiatives will eventually change the outcomes of population health research in each State which in turn can potentially lead into changes in local healthcare policy, statues and regulations.

Acknowledgements

N/A

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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Jun 7th, 1:15 PM Jun 7th, 2:45 PM

A Conceptual Framework for Big Data in Community-wide Population Healthcare Delivery and Research

Background:

The emergence of big data in healthcare is inevitable. The growing demand for big data has been empowered by major improvements in data sharing, steady rise of Electronic Health Record adoption, and supporting federal policies and incentives. The appetite for big data has already reached community-wide population health initiatives partly due to the recent roll out of Accountable Care Organizations (ACO). These policies and technological advancements have created a unique ‘landscape’ to draw upon big data for a ‘greener’ population health delivery system.

Study Overview:

This presentation reviews a list of initiatives that JHMI has established to propel the use of big data in improving the population health of their patient community. This list is then mapped against potential sources of Big Data in population health management to identify gaps and opportunities that are applicable to other academic medical centers as well. Furthermore, a conceptual framework is proposed and then contextualized for JHMI to identify stages involved in translating Big Data into effective population healthcare policies.

Findings:

After applying the proposed conceptual big data population health framework at JHMI, the following items were identified as critical to overcome the barriers in translating big data into community-wide outcomes: (a) increasing the coordination of activities among all centers and institutes through a ‘Population Health Big Data Committee’ operating under the ACO management; (b) aligning incentives for participating units, centers and institutions to form a big data eco-system; and, (c) promote the culture of sharing in which population-level clinical data can be accessed and studied by researchers for the purpose of quality improvement, while research findings can be swiftly translated into population solutions and deployed back into operations.

Policy Implications:

The federal government has supported the big data movement in various domains through a number of policies. Probably the most impactful federal policies and legislations that have spurred the use of big data in community-wide population-health are initiatives that have no direct aim at big data (such as ACA and the HITECH Act through Meaningful Use and HIE initiatives). Federal government can empower the use of big data for improved population health by: (a) Funding dedicated projects and/or open calls for translational population health research using big data; (2) Expanding the current funding opportunities such as CMMI to include the use of big data for community-wide population health; (3) Establish an ‘Office of Big Data’ with various committees, including population health, at ONC to coordinate big data efforts in healthcare; (4) Incorporating community-wide population health measures in the future stages of Meaningful Use objectives and also upcoming EHR certification criteria.

States have less control on the overall big data policies; however, healthcare delivery and payment models can change the landscape of big data in population health. For example, in Maryland, the All-Payer model and state-wide PCMH have empowered providers to share data to improve population health. Results of such big data initiatives will eventually change the outcomes of population health research in each State which in turn can potentially lead into changes in local healthcare policy, statues and regulations.