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The Greater Plains Collaborative: a PCORnet Clinical Research Data Network.

Lemuel R Waitman1, Lauren S Aaronson2, Prakash M Nadkarni3

  • 1Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA.

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Summary
This summary is machine-generated.

The Greater Plains Collaborative (GPC) created a large health network across seven states. This network focuses on building patient cohorts for breast cancer and amyotrophic lateral sclerosis research.

Keywords:
CTSAClinical ResearchComparative EffectivenessData WarehousePCORIPatient Centered

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Area of Science:

  • Translational Science and Clinical Research Network Development

Background:

  • The Greater Plains Collaborative (GPC) leverages existing Clinical and Translational Science Award (CTSA) infrastructure.
  • It comprises 10 major medical centers across seven states, covering a vast geographical area and diverse population.
  • Community input guided the selection of research focus areas.

Purpose of the Study:

  • To establish a robust, multi-institutional research network for clinical and translational studies.
  • To initiate cohort building activities for high-prevalence and rare diseases.
  • To focus on breast cancer and amyotrophic lateral sclerosis (ALS).

Main Methods:

  • Integration of research programs and informatics infrastructure from CTSA initiatives.
  • Formation of a collaborative network spanning 10 medical centers.
  • Utilizing community engagement to prioritize research areas.

Main Results:

  • Established a collaborative network covering 10 million people across seven states.
  • Selected breast cancer (high-prevalence) and amyotrophic lateral sclerosis (rare disease) as initial cohort focus areas.
  • Demonstrated the capacity to repurpose existing infrastructure for new research initiatives.

Conclusions:

  • The GPC successfully established a large-scale, multi-state research network.
  • The network is poised to facilitate cohort development for significant health conditions.
  • This model effectively repurposes translational science infrastructure for broader research impact.