scholarly journals Tracking the Randomized Rollout of a Veterans Affairs Opioid Risk Management Tool: A Multi-method Implementation Evaluation Using the Consolidated Framework for Implementation Research

Author(s):  
Sharon McCarthy ◽  
Matthew Chinman ◽  
Shari Rogal ◽  
Gloria Klima ◽  
Leslie Hausmann ◽  
...  

Abstract BackgroundThe Veterans Health Administration (VHA) developed the Stratification Tool for Opioid Risk Mitigation (STORM) dashboard to assist VHA clinicians in identifying Veterans at risk for adverse opioid overdose or suicide-related events. In 2018, a national policy was implemented requiring providers at all VHA facilities to complete case reviews of Veterans identified by STORM as very high risk for adverse events. Nationally, facilities were randomized by the type of oversight required when sufficient case reviews were not completed and also by the timing of an increase in the number of required case reviews. As part of a comprehensive assessment of this policy intervention, we aimed to 1) identify barriers and facilitators to implementing case reviews as required in the policy; 2) assess variation across the four arms of the study; and 3) evaluate associations between facility characteristics and implementation barriers and facilitators.MethodsUsing the Consolidated Framework for Implementation Research (CFIR), we developed a semi-structured interview guide to examine barriers to and facilitators of implementing the STORM policy. Staff from 40 purposefully selected facilities who were involved in implementation were invited to participate in telephone interviews. Interview transcripts were coded and then organized into memos, which were numerically rated using the -2 to +2 CFIR rating system for each construct. Descriptive statistics were used to evaluate the mean ratings on each CFIR construct, the associations between ratings and study arm, and three facility characteristics (size, rurality, and level of academic detailing) associated with CFIR ratings. We used the mean CFIR rating for each site to determine which constructs differed between the sites with highest and lowest overall CFIR scores, and these constructs were described in detail. ResultsInterviews with 78 staff at 39 VHA facilities identified a slightly positive (+0.2) overall mean CFIR rating. CFIR ratings were not significantly different between the four study arms, nor associated with facility characteristics. Overall, two important barriers to implementation were CFIR constructs Access to knowledge and information and Evaluating and reflecting. Having time to complete the reviews was a pervasive barrier. Sites with higher overall CFIR scores showed three important facilitators: Leadership engagement, Engaging, and Implementation climate. ConclusionAlthough there was variability in implementation barriers and facilitators across facilities, these were unrelated to study arms and facility characteristics. Leadership, resources, and overall implementation climate were the strongest facilitators of policy implementation.

2021 ◽  
Vol 4 (Supplement_1) ◽  
pp. 41-42
Author(s):  
E Johnson ◽  
M Carbonneau ◽  
D Campbell-Scherer ◽  
P Tandon ◽  
A Hyde

Abstract Background Cirrhosis is the leading cause of mortality and morbidity in individuals with gastrointestinal disease. Multiple care gaps exist for hospitalized patients with cirrhosis, resulting in high rates of re-hospitalization (e.g. 44% at 90 days in Alberta). The Cirrhosis Care Alberta (CCAB) is a 4-year multi-component pragmatic trial with an aim to reduce acute-care utilization by implementing an electronic order set and supporting education across eight hospital sites in Alberta. Aims As part of the pre-implementation evaluation, this qualitative study analyzed data from provider focus groups to identify barriers and facilitators to implementation. Methods We conducted focus groups at eight hospital sites with a total of 54 healthcare providers (3–12 per site). A semi-structured interview guide based upon constructs of the Consolidated Framework for Implementation Research (CFIR) and Normalization Process Theory (NPT) frameworks was used to guide the focus groups. Focus groups were recorded and transcribed verbatim. Data was analyzed thematically and inductively. Results Five major themes emerged across all eight sites: (i) understanding past implementation experiences, (ii) resource challenges, (iii) competing priorities among healthcare providers, (iv) system challenges, and (v) urban versus rural differences. Site-specific barriers included perceived lack of patient flow, time restraints, and concerns about the quality and quantity of past implementation interventions. Facilitators included passionate project champions, and an ample feedback process. Conclusions Focus groups were useful for identifying pre-implementation barriers and facilitators of an electronic orders set. Findings from this study are being refined to address the influence of COVID-19, and the data will be used to inform the intervention roll-out at each of the sites. Funding Agencies Alberta Innovates


2020 ◽  
Vol 41 (S1) ◽  
pp. s12-s13
Author(s):  
Hillary Mull ◽  
Kelly Stolzmann ◽  
Emily Kalver ◽  
Marlena Shin ◽  
Marin Schweizer ◽  
...  

Background: Antimicrobial prophylaxis is an evidence-proven strategy for reducing procedure-related infections; however, measuring this key quality metric typically requires manual review, due to the way antimicrobial prophylaxis is documented in the electronic medical record (EMR). Our objective was to combine structured and unstructured data from the Veterans’ Health Administration (VA) EMR to create an electronic tool for measuring preincisional antimicrobial prophylaxis. We assessed this methodology in cardiac device implantation procedures. Methods: With clinician input and review of clinical guidelines, we developed a list of antimicrobial names recommended for the prevention of cardiac device infection. Next, we iteratively combined positive flags for an antimicrobial order or drug fill from structured data fields in the EMR and hits on text string searches of antimicrobial names documented in electronic clinical notes to optimize an algorithm to flag preincisional antimicrobial use with high sensitivity and specificity. We trained the algorithm using existing fiscal year (FY) 2008-15 data from the VA Clinical Assessment Reporting and Tracking-Electrophysiology (CART-EP), which contains manually determined information about antimicrobial prophylaxis. We then validated the performance of the final version of the algorithm using a national cohort of VA patients who underwent cardiac device procedures in FY 2016 or 2017. Discordant cases underwent expert manual review to identify reasons for algorithm misclassification and to identify potential future implementation barriers. Results: The CART-EP dataset included 2,102 procedures at 38 VA facilities with manually identified antimicrobial prophylaxis in 2,056 cases (97.8%). The final algorithm combining structured EMR fields and text-note search results flagged 2,048 of the CART-EP cases (97.4%). Algorithm validation identified antimicrobial prophylaxis in 16,334 of 19,212 cardiac device procedures (87.9%). Misclassifications occurred due to EMR documentation issues. Conclusions: We developed a methodology with high accuracy to measure guideline-concordant use of antimicrobial prophylaxis before cardiac device procedures using data fields present in modern EMRs that does not rely on manual review. In addition to broad applicability in the VA and other healthcare systems with EMRs, this method could be adapted for other procedural areas in which antimicrobial prophylaxis is recommended but comprehensive measurement has been limited to resource-intense manual review.Funding: NoneDisclosures: None


2016 ◽  
Vol 6 (2) ◽  
pp. 16 ◽  
Author(s):  
Nina Sperber ◽  
Sara Andrews ◽  
Corrine Voils ◽  
Gregory Green ◽  
Dawn Provenzale ◽  
...  

Neurology ◽  
2017 ◽  
Vol 89 (24) ◽  
pp. 2422-2430 ◽  
Author(s):  
Teresa M. Damush ◽  
Edward J. Miech ◽  
Jason J. Sico ◽  
Michael S. Phipps ◽  
Greg Arling ◽  
...  

Objective:To identify key barriers and facilitators to the delivery of guideline-based care of patients with TIA in the national Veterans Health Administration (VHA).Methods:We conducted a cross-sectional, observational study of 70 audiotaped interviews of multidisciplinary clinical staff involved in TIA care at 14 VHA hospitals. We de-identified and analyzed all transcribed interviews. We identified emergent themes and patterns of barriers to providing TIA care and of facilitators applied to overcome these barriers.Results:Identified barriers to providing timely acute and follow-up TIA care included difficulties accessing brain imaging, a constantly rotating pool of housestaff, lack of care coordination, resource constraints, and inadequate staff education. Key informants revealed that both stroke nurse coordinators and system-level factors facilitated the provision of TIA care. Few facilities had specific TIA protocols. However, stroke nurse coordinators often expanded upon their role to include TIA. They facilitated TIA care by (1) coordinating patient care across services, communicating across service lines, and educating clinical staff about facility policies and evidence-based practices; (2) tracking individual patients from emergency departments to inpatient settings and to discharge for timely follow-up care; (3) providing and referring TIA patients to risk factor management programs; and (4) performing regular audit and feedback of quality performance data. System-level facilitators included clinical service leadership engagement and use of electronic tools for continuous care across services.Conclusions:The local organization within a health care facility may be targeted to cultivate internal facilitators and a systemic infrastructure to provide evidence-based TIA care.


2019 ◽  
Vol 76 (17) ◽  
pp. 1273-1280
Author(s):  
Douglas D DeCarolis ◽  
Yi-Chieh Chen ◽  
Anders D Westanmo ◽  
Christopher Conley ◽  
Amy A Gravely ◽  
...  

Abstract Purpose We previously reported an interaction with warfarin anticoagulation when initiating treatment with direct-acting antiviral agents for hepatitis C infection. A decreased warfarin sensitivity led to subtherapeutic anticoagulation. To study this interaction further, we expanded our research to include patients treated with the combination of elbasvir and grazoprevir concurrent with warfarin anticoagulation and investigated changes in warfarin sensitivity during and after treatment. Methods Using electronic health records of the Veterans Health Administration, patients starting treatment with elbasvir–grazoprevir for hepatitis C infection concurrent with warfarin anticoagulation were identified. Inclusion required stable warfarin anticoagulation prior to 12 weeks of treatment with elbasvir–grazoprevir. A warfarin sensitivity index (WSI) was calculated at the start and end of treatment and 12 weeks after treatment. The primary endpoint was the difference in WSI from pre- to end-treatment. The secondary endpoint was the WSI difference from pretreatment to 12 weeks posttreatment. Changes in International Normalized Ratio, warfarin doses, and time in therapeutic range were measured. Results In the final sample of 43 patients, the mean WSI decreased during treatment from 0.53 to 0.40, or 25.2%. After treatment, the mean WSI rose to 0.51. Although the mean weekly warfarin dose increased from 40.3 to 44.6 mg during treatment, the mean International Normalized Ratio decreased from 2.40 to 1.96, recovering to 2.59 after treatment. The time spent in therapeutic range decreased from 74.1% before treatment to 39.8% during treatment and back to 64.9% 12 weeks posttreatment. Conclusion When elbasvir–grazoprevir was added to stable warfarin anticoagulation, warfarin sensitivity decreased significantly during treatment and returned to baseline after treatment.


JAMIA Open ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 312-316 ◽  
Author(s):  
Bonnie L Paris ◽  
Denise M Hynes

Abstract This case study describes the implementation of the Research Electronic Data Capture (REDCap) software at the United States Department of Veterans Affairs Veterans Health Administration (VA). VA REDCap enables secure and standardized data collection, fosters collaboration with external researchers through use of a widely used data management tool, facilitates multisite studies through use of data forms that can be shared across sites within and outside the VA, is well suited to health services research studies and quality improvement projects, and enables exporting data for analysis in the VA secure computing environment. Using a diffusion of innovation framework approach, authors explore organizational factors that shaped adoption of REDCap technology and constraints on its use within the VA. Lessons learned from the VA experience are discussed.


2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Helen Lam ◽  
Michael Quinn ◽  
Toni Cipriano-Steffens ◽  
Manasi Jayaprakash ◽  
Emily Koebnick ◽  
...  

Abstract Background Many evidence-based interventions (EBIs) found to be effective in research studies often fail to translate into meaningful patient outcomes in practice. The purpose of this study was to identify facilitators and barriers that affect the implementation of three EBIs to improve colorectal cancer (CRC) screening in an urban federally qualified health center (FQHC) and offer actionable recommendations to improve future implementation efforts. Methods We conducted 16 semi-structured interviews guided by the Consolidation Framework for Implementation Research (CFIR) to describe diverse stakeholders’ implementation experience. The interviews were conducted in the participant’s clinic, audio-taped, and professionally transcribed for analysis. Results We used the five CFIR domains and 39 constructs and subconstructs as a coding template to conduct a template analysis. Based on experiences with the implementation of three EBIs, stakeholders described barriers and facilitators related to the intervention characteristics, outer setting, and inner setting. Implementation barriers included (1) perceived burden and provider fatigue with EHR (Electronic Health Record) provider reminders, (2) unreliable and ineffectual EHR provider reminders, (3) challenges to providing health care services to diverse patient populations, (4) lack of awareness about CRC screening among patients, (5) absence of CRC screening goals, (6) poor communication on goals and performance, and (7) absence of printed materials for frontline implementers to educate patients. Implementation facilitators included (1) quarterly provider assessment and feedback reports provided real-time data to motivate change, (2) integration with workflow processes, (3) pressure from funding requirement to report quality measures, (4) peer pressure to achieve high performance, and (5) a culture of teamwork and patient-centered mentality. Conclusions The CFIR can be used to conduct a post-implementation formative evaluation to identify barriers and facilitators that influenced the implementation. Furthermore, the CFIR can provide a template to organize research data and synthesize findings. With its clear terminology and meta-theoretical framework, the CFIR has the potential to promote knowledge-building for implementation. By identifying the contextual determinants, we can then determine implementation strategies to facilitate adoption and move EBIs to daily practice.


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