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The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time
Published on: February 19, 2021
The science of quality improvement implementation: developing capacity to make a difference
Jeffrey A Alexander1, Larry R Hearld
1Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, MI 48109-2029, USA. jalexand@sph.umich.edu
Background:
Quality improvement (QI) holds promise to improve quality of care; however, organizations often struggle with its implementation. It has been recommended that practitioners, managers, and researchers attempt to increase systematic understanding of the structure, practices, and context of organizations that facilitate or impede the implementation of QI innovations.
Objectives:
To critically review the empirical research on QI implementation in health care organizations.
Research Design:
A literature review of 107 studies that examined the implementation of QI innovations in health care organizations. Studies were classified into 4 groups based on the types of predictors that were assumed to affect implementation (content of QI innovation, organizational processes, internal context, and external context).
Results:
Internal context and organizational processes were the most frequently studied categories. External context and organizational process categories exhibited the highest rate of positive effects on QI implementation.
Conclusions:
The review revealed several important gaps in the QI implementation literature. Studies often lacked clear conceptual frameworks to guide the research, which may hinder efforts to compare relationships across studies. Studies also tended to adopt designs that were narrowly focused on independent effects of predictors and did not include holistic frameworks to capture interactions among the many factors involved in implementation. Other design limitations included the use of cross-sectional designs, single-source data collection, and potential selection bias among study participants.
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