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Updated: Apr 30, 2026
06:28
E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
Published on: August 1, 2019
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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.
Summary
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.
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.
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