# Implementing Precision Medicine: Determinants of Adoption in Community Oncology

> **NIH NIH P20** · UNIVERSITY OF KANSAS MEDICAL CENTER · 2020 · $226,132

## Abstract

ABSTRACT 
Precision medicine has enormous potential to change cancer outcomes for >500,000 Americans annually by 
targeting the genetic mutations of their tumors with FDA-approved drugs known to more effectively treat their 
disease. Thus, accelerating the use of cancer genomics is a national priority with combined public and private 
investment topping $8 billion a year. Despite high significance and investment, uptake of precision medicine in 
clinical practice is low. Tumor genome sequencing is not widely used and treatments based on molecular profiling 
are infrequently implemented. Implementation science is an emerging field which offers a theoretically-informed, 
evidence-based approach to accelerate the translation of evidence into practice, but has yet to be applied to 
precision medicine and lacks tools to rapidly diagnose organizational challenges to innovation adoption. Using 
this approach, we have identified a number of critical gaps in current research on the barriers to precision 
medicine adoption. Focusing on the needs of community oncologists, who deliver the majority of cancer care in 
the US, we will: 
1) Survey oncologists to identify precision medicine adopters, assess community oncologists' motivations for 
 innovation adoption, and evaluate the degree to which precision medicine aligns with community practice. 
2) Conduct linked, semi-structured qualitative interviews of physicians, staff and administrators involved in 
 precision medicine implementation. Using the Theoretical Domains Framework, we will identify constructs 
 key to implementation success and further describe the strength, frequency, and type of implementation 
 strategies used by successful organizations. 
3) Assess the feasibility of using natural language processing to more rapidly diagnose adoption and 
 implementation barriers. We will apply an ontology of barriers and facilitators to data collected in Aim 2 and 
 three extant qualitative datasets exploring innovation adoption. We will develop and train an automated 
 feature extraction system to code data sets and compare congruence of results from human coding and 
 artificial intelligence. 
This work is expected to advance precision medicine, implementation science and cancer outcomes. Aim 1 will 
allow the first estimate of precision medicine adoption in community oncology. Barriers to precision medicine 
implementation identified in Aims 1 and 2 will be mapped to strategies known to be effective in addressing them, 
enabling a randomized trial of the comparative effectiveness of precision medicine implementation strategies. 
Ontologies and machine learning developed in Aim 3 will contribute to a larger machine learning effort launched 
this year to expedite the selection of effective, tailored implementation strategies. Ultimately, this work is 
expected to expedite society's return on investment in the precision medicine initiative and contribute to cancer 
patients' longer survival and enhanced ...

## Key facts

- **NIH application ID:** 9869928
- **Project number:** 5P20GM130423-02
- **Recipient organization:** UNIVERSITY OF KANSAS MEDICAL CENTER
- **Principal Investigator:** Shellie Dawn Ellis
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $226,132
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9869928

## Citation

> US National Institutes of Health, RePORTER application 9869928, Implementing Precision Medicine: Determinants of Adoption in Community Oncology (5P20GM130423-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9869928. Licensed CC0.

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