Monica S. Vavilala, Mary A. King, Jen-Ting Yang, Scott L. Erickson, Brianna Mills, Rosemary M. Grant, Carolyn Blayney, Qian Qiu, Randall M. Chesnut, Kenneth M. Jaffe, Bryan J. Weiner, & Brian D. Johnston
University of Washington affiliated authors are displayed in bold.
Published: November 2018
Laura E. Ellington, Elizabeth Jacob‐Files, Rosario Becerra, Gabriela Mallma, José Tantalean da Fieno, and Katie R. Nielsen
University of Washington affiliated authors are displayed in bold.
Published: November 2018
Michael D. Pullmann, Leah Lucid, Julie P. Harrison, Prerna Martin, Esther Deblinger, Katherine S. Benjamin and Shannon Dorsey
University of Washington affiliated authors are displayed in bold.
Published: October 2018
Children infrequently receive evidence-based treatments (EBTs) for mental health problems due to a science-to-practice implementation gap. Workplace-based clinical supervision, in which supervisors provide oversight, feedback, and training on clinical practice, may be a method to support EBT implementation. Our prior research suggests that the intensity of supervisory focus on EBT (i.e., thoroughness of coverage) during workplace-based supervision varies. This study explores predictors of supervisory EBT intensity.
Participants were twenty-eight supervisors and 70 clinician supervisees. They completed a baseline survey, and audio recorded supervision sessions over 1 year. Four hundred and thirty eight recordings were coded for supervision content. We chose to explore predictors of two EBT content elements due to their strong evidence for effectiveness and sufficient variance to permit testing. These included a treatment technique (“exposure”) and a method to structure treatment (“assessment”). We also explored predictors of non-EBT content (“other topics”). Mixed-effects models explored predictors at organizational/supervisor, clinician, and session levels.
Positive implementation climate predicted greater intensity of EBT content coverage for assessment (coefficient = 0.82, p = 0.004) and exposure (coefficient = 0.87, p = 0.001). Intensity of exposure coverage was also predicted by more time spent discussing each case (coefficient = 0.04, p < 0.001). Predictors of greater non-EBT content coverage included longer duration of supervision sessions (coefficient = 0.05, p < 0.001) and lower levels of supervisor EBT knowledge (coefficient = −0.17, p = 0.013). No other supervisor- or clinician-level variables were significant predictors in the mixed effects models.
This was the first study to explore multi-level predictors of objectively coded workplace-based supervision content. Results suggest that organizations that expect, support and reward EBT are more likely to have greater intensity of EBT supervision coverage, which in turn may positively impact clinician EBT fidelity and client outcomes. There was evidence that supervisor knowledge of the EBT contributes to greater coverage, although robust supervisor and clinician factors that drive supervision are yet to be identified. Findings highlight the potential effectiveness of implementation strategies that simultaneously address organizational implementation climate and supervisor practices. More research is needed to identify mechanisms that support integration of EBT into supervision.
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**
Joseph E. Glass, Jennifer F. Bobb, Amy K. Lee, Julie E. Richards, Gwen T. Lapham, Evette Ludman, Carol Achtmeyer, Ryan M. Caldeiro, Rebecca Parrish, Emily C. Williams, Paula Lozano and Katharine A. Bradley
University of Washington affiliated authors are displayed in bold.
Published: August 2018
Read the full text in the open access journal Implementation Science
Experts recommend that alcohol-related care be integrated into primary care (PC) to improve prevention and treatment of unhealthy alcohol use. However, few healthcare systems offer such integrated care. To address this gap, implementation researchers and clinical leaders at Kaiser Permanente Washington (KPWA) partnered to design a high-quality program of evidence-based care for unhealthy alcohol use: the Sustained Patient-centered Alcohol-related Care (SPARC) program. SPARC implements systems of clinical care designed to increase both prevention and treatment of unhealthy alcohol use. This clinical care for unhealthy alcohol use was implemented using three strategies: electronic health record (EHR) decision support, performance monitoring and feedback, and front-line support from external practice coaches with expertise in alcohol-related care (“SPARC implementation intervention” hereafter).
The purpose of this report is to describe the protocol of the SPARC trial, a pragmatic, cluster-randomized, stepped-wedge implementation trial to evaluate whether the SPARC implementation intervention increased alcohol screening and brief alcohol counseling (so-called brief interventions), and diagnosis and treatment of alcohol use disorders (AUDs) in 22 KPWA PC clinics.
The SPARC trial sample includes all adult patients who had a visit to any of the 22 primary care sites in the trial during the study period (January 1, 2015–July 31, 2018). The 22 sites were randomized to implement the SPARC program on different dates (in seven waves, approximately every 4 months). Primary outcomes are the proportion of patients with PC visits who (1) screen positive for unhealthy alcohol use and have documented brief interventions and (2) have a newly recognized AUD and subsequently initiate and engage in alcohol-related care. Main analyses compare the rates of these primary outcomes in the pre- and post-implementation periods, following recommended approaches for analyzing stepped-wedge trials. Qualitative analyses assess barriers and facilitators to implementation and required adaptations of implementation strategies.
The SPARC trial is the first study to our knowledge to use an experimental design to test whether practice coaches with expertise in alcohol-related care, along with EHR clinical decision support and performance monitoring and feedback to sites, increase both preventive care—alcohol screening and brief intervention—as well as diagnosis and treatment of AUDs.
The trial is registered at ClinicalTrials.Gov: NCT02675777. Registered February 5, 2016, https://clinicaltrials.gov/ct2/show/NCT02675777.
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**
Jill Locke, Wendy Shih, Christina D Kang-Yi, Julie Caramanico, Travis Shingledecker, Jordan Gibson, Lindsay Frederick and David S Mandell
University of Washington affiliated authors are displayed in bold.
Published: July 2018
Leah Lucid, Rosemary Meza, Michael D. Pullmann, Nathaniel Jungbluth, Esther Deblinger, & Shannon Dorsey
University of Washington affiliated authors are displayed in bold.
Published: July 2018
Christopher G. Kemp; Bryan J. Weiner; Kenneth H. Sherr; Linda E. Kupfer; Peter K. Cherutich; David Wilson; Elvin H. Geng; Judith N. Wasserheit
University of Washington affiliated authors are displayed in bold.
Published: July 2018
Katrina J. Stime, Nigel Garrett, Yukteshwar Sookrajh, Jienchi Dorward, Ntuthu Dlamini, Ayo Olowolagba, Monisha
Sharma, Ruanne V. Barnabas and Paul K. Drain
University of Washington affiliated authors are displayed in bold.
Published: May 2018
Many clinics in Southern Africa have long waiting times. The implementation of point-of-care (POC) tests to accelerate diagnosis and improve clinical management in resource-limited settings may improve or worsen clinic flow and waiting times. The objective of this study was to describe clinic flow with special emphasis on the impact of POC testing at a large
urban public healthcare clinic in Durban, South Africa.
We used time and motion methods to directly observe patients and practitioners. We created patient flow maps and recorded individual patient waiting and consultation times for patients seeking STI, TB, or HIV care. We conducted semi-structured interviews with 20 clinic staff to ascertain staff opinions on clinic flow and POC test implementation.
Among 121 observed patients, the total number of queues ranged from 4 to 7 and total visit times ranged from 0:14 (hours:minutes) to 7:38. Patients waited a mean of 2:05 for standard-of-care STI management, and approximately 4:56 for STI POC diagnostic testing. Stable HIV patients who collected antiretroviral therapy refills waited a mean of 2:42 in the standard queue and 2:26 in the fast-track queue. A rapid TB test on a small sample of patients with the Xpert MTB/RIF assay and treatment initiation took a mean of 6:56, and 40% of patients presenting with TB-related symptoms were asked to return for an additional clinic visit to obtain test results. For all groups, the mean clinical assessment time with a nurse
or physician was 7 to 9 min, which accounted for 2 to 6% of total visit time. Staff identified poor clinic flow and personnel shortages as areas of concern that may pose challenges to expanding POC tests in the current clinic environment.
This busy urban clinic had multiple patient queues, long clinical visits, and short clinical encounters. Although POC testing ensured patients received a diagnosis sooner, it more than doubled the time STI patients spent at the clinic and did not result in same-day diagnosis for all patients screened for TB. Further research on implementing POC testing efficiently into care pathways is required to make these promising assays a success.
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**
Christian D. Helfrich, Marlana J. Kohn, Austin Stapleton, Claire L. Allen, Kristen Elizabeth Hammerback, K. C. Gary Chan, Amanda T. Parrish, Daron E. Ryan, Bryan J. Weiner, Jeffrey R. Harris, & Peggy A. Hannon
University of Washington affiliated authors are displayed in bold.
Published: April 2018
Organizational readiness to change may be a key determinant of implementation success and a mediator of the effectiveness of implementation interventions. If organizational readiness can be reliably and validly assessed at the outset of a change initiative, it could be used to assess the effectiveness of implementation-support activities by measuring changes in readiness factors over time.
We analyzed two waves of readiness-to-change survey data collected as part of a three-arm, randomized controlled trial to implement evidence-based health promotion practices in small worksites in low-wage industries. We measured five readiness factors: context (favorable broader conditions); change valence (valuing health promotion); information assessment (demands and resources to implement health promotion); change commitment (an intention to implement health promotion); and change efficacy (a belief in shared ability to implement health promotion). We expected commitment and efficacy to increase at intervention sites along with their self-reported effort to implement health promotion practices, termed wellness-program effort. We compared means between baseline and 15 months, and between intervention and control sites. We used linear regression to test whether intervention and control sites differed in their change-readiness scores over time.
Only context and change commitment met reliability thresholds. Change commitment declined significantly for both control (−0.39) and interventions sites (−0.29) from baseline to 15 months, while context did not change for either. Only wellness program effort at 15 months, but not at baseline, differed significantly between control and intervention sites (1.20 controls, 2.02 intervention). Regression analyses resulted in two significant differences between intervention and control sites in changes from baseline to 15 months: (1) intervention sites exhibited significantly smaller change in context scores relative to control sites over time and (2) intervention sites exhibited significantly higher changes in wellness program effort relative to control sites.
Contrary to our hypothesis, change commitment declined significantly at both Healthlinks and control sites, even as wellness-program effort increased significantly at HealthLinks sites. Regression to the mean may explain the decline in change commitment. Future research needs to assess whether baseline commitment is an independent predictor of wellness-program effort or an effect modifier of the HealthLinks intervention.
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**
Katie R. Nielsen, Rosario Becerra, Gabriela Mallma, & José Tantaleán da Fieno
University of Washington affiliated authors are displayed in bold.
Published: April 2018
Acute lower respiratory infections are the leading cause of death outside the neonatal period for children less than 5 years of age. Widespread availability of invasive and non-invasive mechanical ventilation in resource-rich settings has reduced mortality rates; however, these technologies are not always available in many low- and middle-income countries due to the high cost and trained personnel required to implement and sustain their use. High flow nasal cannula (HFNC) is a form of non-invasive respiratory support with growing evidence for use in pediatric respiratory failure. Its simple interface makes utilization in resource-limited settings appealing, although widespread implementation in these settings lags behind resource-rich settings. Implementation science is an emerging field dedicated to closing the know-do gap by incorporating evidence-based interventions into routine care, and its principles have guided the scaling up of many global health interventions. In 2016, we introduced HFNC use for respiratory failure in a pediatric intensive care unit in Lima, Peru using implementation science methodology. Here, we review our experience in the context of the principles of implementation science to serve as a guide for others considering HFNC implementation in resource-limited settings.
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**