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Updated: May 20, 2026

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time
Published on: February 19, 2021
Measurement of a model of implementation for health care: toward a testable theory
Joan M Cook1, Casey O'Donnell, Stephanie Dinnen
1Department of Psychiatry, Yale School of Medicine, 950 Campbell Avenue, NEPEC/182, West Haven, CT 06516, USA. Joan.Cook@yale.edu
Background:
Greenhalgh et al. used a considerable evidence-base to develop a comprehensive model of implementation of innovations in healthcare organizations [1]. However, these authors did not fully operationalize their model, making it difficult to test formally. The present paper represents a first step in operationalizing Greenhalgh et al.'s model by providing background, rationale, working definitions, and measurement of key constructs.
Methods:
A systematic review of the literature was conducted for key words representing 53 separate sub-constructs from six of the model's broad constructs. Using an iterative process, we reviewed existing measures and utilized or adapted items. Where no one measure was deemed appropriate, we developed other items to measure the constructs through consensus.
Results:
The review and iterative process of team consensus identified three types of data that can been used to operationalize the constructs in the model: survey items, interview questions, and administrative data. Specific examples of each of these are reported.
Conclusion:
Despite limitations, the mixed-methods approach to measurement using the survey, interview measure, and administrative data can facilitate research on implementation by providing investigators with a measurement tool that captures most of the constructs identified by the Greenhalgh model. These measures are currently being used to collect data concerning the implementation of two evidence-based psychotherapies disseminated nationally within Department of Veterans Affairs. Testing of psychometric properties and subsequent refinement should enhance the utility of the measures.
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