Talk to the Veterans Crisis Line now
U.S. flag
An official website of the United States government

Health Services Research & Development

Go to the ORD website
Go to the QUERI website

HSR&D Citation Abstract

Search | Search by Center | Search by Source | Keywords in Title

Application of a nonrandomized stepped wedge design to evaluate an evidence-based quality improvement intervention: a proof of concept using simulated data on patient-centered medical homes.

Huynh AK, Lee ML, Farmer MM, Rubenstein LV. Application of a nonrandomized stepped wedge design to evaluate an evidence-based quality improvement intervention: a proof of concept using simulated data on patient-centered medical homes. BMC medical research methodology. 2016 Oct 21; 16(1):143.

Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects.

If you have VA-Intranet access, click here for more information vaww.hsrd.research.va.gov/dimensions/

VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address.
   Search Dimensions for VA for this citation
* Don't have VA-internal network access or a VA email address? Try searching the free-to-the-public version of Dimensions



Abstract:

BACKGROUND: Stepped wedge designs have gained recognition as a method for rigorously assessing implementation of evidence-based quality improvement interventions (QIIs) across multiple healthcare sites. In theory, this design uses random assignment of sites to successive QII implementation start dates based on a timeline determined by evaluators. However, in practice, QII timing is often controlled more by site readiness. We propose an alternate version of the stepped wedge design that does not assume the randomized timing of implementation while retaining the method's analytic advantages and applying to a broader set of evaluations. To test the feasibility of a nonrandomized stepped wedge design, we developed simulated data on patient care experiences and on QII implementation that had the structures and features of the expected data from a planned QII. We then applied the design in anticipation of performing an actual QII evaluation. METHODS: We used simulated data on 108,000 patients to model nonrandomized stepped wedge results from QII implementation across nine primary care sites over 12 quarters. The outcome we simulated was change in a single self-administered question on access to care used by Veterans Health Administration (VA), based in the United States, as part of its quarterly patient ratings of quality of care. Our main predictors were QII exposure and time. Based on study hypotheses, we assigned values of 4 to 11 % for improvement in access when sites were first exposed to implementation and 1 to 3 % improvement in each ensuing time period thereafter when sites continued with implementation. We included site-level (practice size) and respondent-level (gender, race/ethnicity) characteristics that might account for nonrandomized timing in site implementation of the QII. We analyzed the resulting data as a repeated cross-sectional model using HLM 7 with a three-level hierarchical data structure and an ordinal outcome. Levels in the data structure included patient ratings, timing of adoption of the QII, and primary care site. RESULTS: We were able to demonstrate a statistically significant improvement in adoption of the QII, as postulated in our simulation. The linear time trend while sites were in the control state was not significant, also as expected in the real life scenario of the example QII. CONCLUSIONS: We concluded that the nonrandomized stepped wedge design was feasible within the parameters of our planned QII with its data structure and content. Our statistical approach may be applicable to similar evaluations.





Questions about the HSR&D website? Email the Web Team.

Any health information on this website is strictly for informational purposes and is not intended as medical advice. It should not be used to diagnose or treat any condition.