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Precision Community Research Study

Precision Community research study by Shellie D. Ellis, M.A., Ph.D., Junior Faculty, Kansas Institute for Precision Medicine COBRE.

Precision medicine has enormous potential to change cancer outcomes, especially for rural patients. More than a third of the 1.5 million Americans diagnosed with cancer each year have genetic mutations in their tumors that could be targeted with an FDA-approved drug to more effectively treat their disease. Accelerating the use of cancer genomics is a national priority with public and private investment topping $8 billion a year. Despite high significance and investment, use of targeted cancer therapy in clinical practice is disappointing. Tumor testing is not widely used, so improved treatments based on molecular and genomic profiling are not offered to patients.

Precision Community is a research study that uses an implementation science approach. The goal is to identify strategies that accelerate the use of evidence-based targeted cancer therapy.

Precision Community is designed to accomplish the following aims:

precision community graphic

  1. Talk to community oncologists to learn about their use of targeted therapy and identify barriers to precision medicine adoption.
    We will use institutional tumor registry data and genomic test information in patients' medical record to see how often oncologists are using targeted cancer treatment guidelines. We also will look at provider and practice characteristics associated with using guidelines for targeted therapy.

  2. Isolate barriers to successful implementation of targeted cancer therapy in community oncology practice.
    We will interview physicians, staff and administrators involved in targeted cancer therapy implementation. We will identify key factors that influence implementation.

  3. Test the feasibility of using natural language processing to diagnose barriers and facilitators to innovation adoption.
    We will combine data collected in the Precision Community study with three extant qualitative datasets exploring cancer specialists' innovation adoption to test the feasibility of developing and training a machine-learning algorithm capable of annotating similar interviews, through a natural language processing approach. This could substantially accelerate the process of identifying barriers and facilitators to innovation adoption at other sites.

Community oncologists can learn more about joining the Precision Community by emailing

S Ellis
Shellie D. Ellis, M.A., Ph.D.

Assistant Professor, Department of Population Health, University of Kansas School of Medicine
Junior Faculty, Kansas Institute for Precision Medicine COBRE
NCI/Academy Health Visiting Scholar
National Cancer Institute