Deimplementation of Routine Chest X-rays in Adult Intensive Care Units
BACKGROUND: Choosing Wisely® is a national initiative to deimplement or reduce low-value care. However, there is limited evidence on the effectiveness of strategies to influence ordering patterns.
OBJECTIVE: We aimed to describe the effectiveness of an intervention to reduce daily chest X-ray (CXR) ordering in two intensive care units (ICUs) and evaluate deimplementation strategies.
DESIGN: We conducted a prospective, nonrandomized study with control data from a historical period. Qualitative evaluation was guided by the Consolidated Framework for Implementation Research.
SETTING: The study was performed in the medical intensive care unit (MICU) and cardiovascular intensive care unit (CVICU) of an academic medical center in the United States from October 2015 to June 2016.
PARTICIPANTS: The initiative included the staff of the MICU and CVICU (physicians, surgeons, nurse practitioners, fellows, residents, medical students, and X-ray technologists).
INTERVENTION COMPONENTS: We utilized provider education, peer champions, and weekly data feedback of CXR ordering rates.
MEASUREMENTS: We analyzed the CXR ordering rates and factors facilitating or inhibiting deimplementation.
RESULTS: Segmented linear time-series analysis suggested a small but statistically significant decrease in CXR ordering rates in the CVICU (P < .001) but not in the MICU. Facilitators of deimplementation, which were more prominent in the CVICU, included engagement of peer champions, stable staffing, and regular data feedback. Barriers included the need to establish goal CXR ordering rates, insufficient intervention visibility, and waning investment among medical residents in the MICU due to frequent rotation and competing priorities.
CONCLUSIONS: Intervention modestly reduced CXRs ordered in one of two ICUs evaluated. Understanding why adoption differed between the two units may inform future interventions to deimplement low-value diagnostic tests.
© 2019 Society of Hospital Medicine
In September 2015, CVICU and MICU teams received a didactic session highlighting CW, current CXR ordering rates, and the plan for reducing CXR ordering. On October 5, 2015, teams began receiving weekly e-mails with ordering rates defined as CXRs ordered per patient per day and a brief rationale for reducing unnecessary CXRs. To encourage friendly competition, we provided weekly rates to the MICU teams, allowing for transparent benchmarking against one another. A similar competition strategy was not used in the CVICU due to the lack of multiple teams.
In the CVICU, two ACNPs volunteered as peer champions. These champions coordinated data feedback and advocated for the intervention among their colleagues. In the MICU, three internal medicine residents volunteered as peer champions and fulfilled similar roles.
To facilitate deimplementation, we conducted two Plan-Do-Study-Act (PDSA) cycles, the first from November to mid-December 2015 and the second from mid-December 2015 to mid-January 2016. During these cycles, we tailored our deimplementation strategy based on barriers identified by the peer champions and ICU leaders (described in the Qualitative Results section). Peer champions and the CW Steering Committee generated potential solutions by conversing with stakeholders and using the Expert Recommendations for Implementing Change (ERIC).20 Interventions included disseminating promotional flyers, holding meetings with stakeholders, and providing monthly CXR ordering rates. After the PDSA cycles, we continued reexamining the deimplementation efforts by reviewing ordering rates and soliciting feedback from ICU leaders and peer champions. However, no significant changes to the intervention were made during this time.
Quantitative Evaluation
We extracted data from VUMC’s Enterprise Data Warehouse during the intervention period (October 5, 2015 to May 24, 2016) and a historical control period (October 1, 2014 to October 4, 2015). Within each ICU, descriptive statistics were used to compare patient cohorts in the baseline and intervention periods by age, sex, and race.
The primary outcome was CXRs ordered per patient per day by hospital unit (CVICU or MICU). The baseline period included all data between October 1, 2014 and September 15, 2015. To account for priming of providers from didactic education, we allowed a washout period from September 16, 2015 to October 4, 2015. As a preliminary analysis, we compared CXR rates in the baseline and intervention periods using Wilcoxon rank-sum tests. We then conducted interrupted time-series analyses with segmented linear regression to assess differences in linear trends in CXR rates over the two periods. To account for different staffing models in the MICU, we stratified the impact of the intervention by team—medical resident (physician) or ACNP. R version 3.4.0 was used for statistical analysis.21
Qualitative Evaluation
Our qualitative evaluation consisted of embedded observation and semistructured interviews with stakeholders. The qualitative portion was guided by the Consolidated Framework for Implementation Research (CFIR), a widely used framework for design and evaluation of improvement initiatives that helped us to determine major facilitators and barriers to implementation.22,23
Embedded Observation
From November 2015 to January 2016, we observed morning rounds in the CVICU and MICU one to two times weekly to understand factors facilitating and inhibiting uptake of the intervention. Observations were recorded and organized using a CFIR-based template and directed toward understanding interactions among team members (eg, the decision-making process hierarchy), team workflows and decision-making processes, process of ordering CXRs, and providers’ knowledge and perceptions of the CXR intervention (see Supplemental Material, 3 – CFIR Table).22,23 After rounds, ICU team members were invited to share suggestions for improving the intervention. All observations occurred during and shortly following morning rounds when the vast majority of routine CXRs are ordered; we did not evaluate night or evening workflows. In the spirit of continuous improvement, we evaluated data in real-time.
