NATHALIE HUGUET
Oregon Health & Science University

There is strong evidence that different implementation support strategies (e.g., facilitation, audit and feedback, performance benchmarking) can help clinical practices with adoption and maintenance of evidence-based guidelines. There are, however, costs to both providing and receiving implementation support. While evidence demonstrates the effectiveness of various implementation strategies, relatively little is known about which practices will benefit most from a particular implementation strategy, how much assistance a practice might need, or if practices could improve on their own. New methods are needed to predict which practices may implement targeted changes with less support and which will need more. The objective of this study is to develop and validate predictive models that estimate the likelihood of adoption of an electronic health record (EHR)-related tool.

EHR data from 351 community health centers (CHCs) from the OCHIN Network in which an insurance support information technology (IT) tool was implemented 05/01/2018. The insurance support tool was designed for clinic eligibility specialists to document health insurance assistance provided and assist with HRSA required reporting.

We used LASSO penalized logistic regression to develop and validate models predicting adoption and sustainability of the tool. Predictive performance was assessed using ROC curve (AUC). Adoption is defined as any instance of tool use within the 12-month follow-up period (up to 6/30/2019). Sustainability is defined as at least one tool use in the last four months of the follow-up period. Variables included in the models were geographic variables, number and type of departments/clinics, patient panel, patient panel demographic characteristics, type and number of encounters, payer distribution, provider type, number of encounters with eligibility specialists.

About 42% of CHCs adopted the tool and 25% demonstrated sustained use. Models for adoption (AUC= 0.784; 95%CI: 0.710 – 0.858) and sustainability (AUC=0.829; 95%CI: 0.746 – 0.912) show high classification accuracy. Out of the 25 variables entered in the model, three predicted adoption (years in EHR, total number of visits, and percent of visits that were ambulatory) and one (total number of visits) predicted sustainability.

EHR data can be used to predict EHR tool use. This work is the next step toward advancing the science of ‘precision implementation’ and how to efficiently tailor and deploy implementation support strategies for IT innovations.

JOHN WATSON, MD
University of Colorado Anschutz Medical Campus, Children’s Hospital Colorado

While asthma controllers (inhaled corticosteroids and leukotriene inhibitors) have been shown to reduce exacerbations in children 2-5 years old with asthma or recurrent wheeze, deciding when to prescribe controllers in children <2 years old remains challenging given the substantial clinical overlap between asthma and lower respiratory tract infections (LRTIs). Our objective was to assess the association between time on controller medications and emergency department (ED) and inpatient (IP) visits for LRTI or asthma in children <2 years old.

Children <2 years old with at least one prior LRTI (bronchiolitis and pneumonia) were identified in the 2009-2017 Colorado All Payer Claims Database. Those with complex chronic conditions, a diagnosis of asthma prior to first LRTI, or controller use prior to first LRTI were excluded.

Retrospective cohort study using administrative claims data. The primary exposure variable was a time-dependent indicator for presence of a prescription for controller medication. The primary outcome was count of ED/IP visits for any diagnosis of LRTI or ED/IP visit for a primary diagnosis of asthma after the first LRTI (using ICD9/10 codes). A Poisson regression model accounting for correlation within subjects was used. Adjusted models included baseline covariates for gender, insurance type, prematurity, family history of asthma claim, and time-dependent covariates of prior wheeze claim, atopy claim, number of LRTI visits, LRTI hospitalizations, prior subspecialty claim (Allergy/Immunology or Pulmonology), and outpatient beta agonist prescriptions.

We identified 40,473 children meeting inclusion criteria, ultimately constituting 547,082 person-months. A larger percentage of person-time on controller compared to off controller was seen with older age, male gender, Medicaid insurance, family history of asthma claim, prior atopy claim, prior wheeze claim, more prior LRTI visits, and prior outpatient beta agonist prescription. Controller medication use was not significantly associated with a reduction in ED/IP visits for LRTI or asthma in the adjusted model (RR 0.77; 95% CI: 0.57, 1.05).

In children under 2 with LRTI, controllers are more often prescribed in those who have more risk factors for future asthma. However, we found that time on controllers did not statistically reduce ED/IP visits for related respiratory diagnoses in this age group, potentially indicating an area for increased prescription stewardship.

BRYAN R. GARNER, PhD
RTI International

The “research-to-practice gap” is a major public health problem that dissemination and implementation research seeks to address. According to the theory of implementation effectiveness, implementation effectiveness (i.e., the consistency and quality of implementation) is a function of two key constructs. The first is implementation climate, which is defined as the extent to which implementation of an innovation is expected, supported, and rewarded. The second is the fit between the innovation and the values of the targeted practice setting. Two widely used measures for assessing implementation climate include the 6-item measure developed by Jacobs, Weiner, and Bunger (2014) and the 18-item measure developed by Ehrhart, Aarons, and Farahnak (2014). In contrast, there is not a widely used measure to assess the fit between the innovation developed as part of research and the practice setting of interest. This presentation/poster introduces the 6-item innovation-practice fit developed and used as part of the Substance-Treatment-Strategies for HIV care (STS4HIV) Project. Setting/Population: Although developed for assessing the fit between evidence-based treatment interventions for substance use disorders (i.e., the innovation) and HIV service organizations (i.e., the practice setting), this measure may be adapted for any innovation-practice combination. Methods: In May 2020, 253 HIV service organizations from across the United States were invited to participate in a study focused on identify the most promising evidence-based substance use disorder treatments for integration within HIV service organizations. Nine evidence-based treatments for substance use disorders were assessed. Using a standardized format, an infographic and video was developed for each evidence-based treatment. For each evidence-based treatment, the HIV service organization’s respondent was first shown the video and infographic and then asked to rate (0=not at all; 1=to a minor extent; 2=to a moderate extent; 3=to a major extent) the extent to which the evidenced-based treatment was fundable, implementable, retainable, sustainable, scalable, and timely for their HIV service organization. Innovation-practice fit was calculated for each evidence-based treatment by taking the sum of these six items (possible range is 0-18).

Of 253 HIV service organizations invited to participate, 203 (80%) completed participation. The average innovation-practice score for motivational interviewing (11.42; SD=0.29) was significantly (p<.05) higher than the other eight evidence-based treatments and was the only one rated higher than the measure’s midpoint. The average innovation-practice fit score for cognitive behavioral therapy was at the midpoint (9.5; SD=.31). The average innovation-practice fit score was below the midpoint for the other six evidence-based treatments assessed.

A new measure for assessing innovation-practice fit has been developed.