Implementation Science最新文献

Background: Between 2012-2022 opioid-related overdose deaths in the United States, including Washington State, have risen dramatically. Opioid use disorder (OUD) is a complex, chronic, and criminalized illness with biological, environmental, and social causes. One-fifth of people with OUD have recent criminal-legal system involvement; > 50% pass through WA jails annually. Medications for Opioid Use Disorder (MOUD) can effectively treat OUD. WA has prioritized improving access to MOUD, including for those in jails. As patients in jail settings are systematically marginalized due to incarceration, it is critical to foster connections to MOUD services upon release, an acknowledged period of high overdose risk. Currently, there is insufficient focus on developing strategies to foster linkages between jail-based MOUD and referral services. The Systems Analysis and Improvement Approach (SAIA), an evidence-based implementation strategy, may optimize complex care cascades like MOUD provision and improve linkages between jail- and community-based providers. SAIA bundles systems engineering tools into an iterative process to guide care teams to visualize cascade drop-offs and prioritize steps for improvement; identify modifiable organization-level bottlenecks; and propose, implement, and evaluate modifications to overall cascade performance. The SAIA-MOUD study aims to strengthen the quality and continuity of MOUD care across jail and referral clinics in King County, WA, and ultimately reduce recidivism and mortality.

Methods: We will conduct a quasi-experimental evaluation of SAIA effectiveness on improving MOUD care cascade quality and continuity for patients receiving care in jail and exiting to referral clinics; examine determinants of SAIA-MOUD adoption, implementation, and sustainment; and determine SAIA-MOUD's cost and cost-effectiveness. Clinic teams with study team support will deliver the SAIA-MOUD intervention at the jail-based MOUD program and three referral clinics over a two-year intensive phase, followed by a one-year sustainment phase where SAIA implementation will be led by King County Jail MOUD staff without study support to enable pragmatic evaluation of sustained implementation.

Discussion: SAIA packages user-friendly systems engineering tools to guide decision-making by front-line care providers to identify low-cost, contextually appropriate health care improvement strategies. By integrating SAIA into MOUD care provision in jail and linked services, this pragmatic trial is designed to test a model for national scale-up.

Trial registration: ClinicalTrials.gov NCT06593353 (registered 09/06/2024; https://register.

Clinicaltrials: gov/prs/beta/studies/S000EVJR00000029/recordSummary ).

Background: In healthcare research and practice, intervention and implementation fidelity represent the steadfast adherence to core components of research-supported interventions and the strategies employed for their implementation. Evaluating fidelity involves determining whether these core components were delivered as intended. Without fidelity data, the results of complex interventions cannot be meaningfully interpreted. Increasingly, the necessity for firmness and strict adherence by implementers and their organizations has been questioned, with calls for flexibility to accommodate contextual conditions. This shift makes contemporary fidelity a balancing act, requiring researchers to navigate various tensions. This debate paper explores these tensions, drawing on experiences from developing fidelity assessments in two ongoing effectiveness-implementation hybrid trials.

Main body: First, given often scarce knowledge about the core components of complex interventions and implementation strategies, decisions about fidelity requirements involve a degree of subjective reasoning. Researchers should make these decisions transparent using theory or logic models. Second, because fidelity is context-dependent and applies to both interventions and implementation strategies, researchers must rethink fidelity concepts with every study while balancing firmness and flexibility. This is particularly crucial for hybrid studies, with their differing emphasis on intervention and implementation fidelity. Third, fidelity concepts typically focus on individual behaviors. However, since organizational and system factors also influence fidelity, there is a growing need to define fidelity criteria at these levels. Finally, as contemporary fidelity concepts prioritize flexible over firm adherence, building, evaluating, and maintaining fidelity in healthcare research has become more complex. This complexity calls for intensified efforts to expand the knowledge base for pragmatic and adaptive fidelity measurement in trial and routine healthcare settings.

Conclusion: Contemporary conceptualizations of fidelity place greater demands on how fidelity is examined, necessitating the expansion of fidelity frameworks to include organizational and system levels, the service- and study-specific conceptualizations of intervention and implementation fidelity, and the development of pragmatic approaches for assessing fidelity in research and practice. Continuing to build knowledge on how to balance requirements for firmness and flexibility remains a crucial task within the field of implementation science.

Background: As dissemination and implementation (D&I) research increases, we must continue to expand training capacity and research networks. Documenting, understanding, and enhancing advice networks identifies key connectors and areas where networks are less established. In 2012 Norton et al. mapped D&I science advice and collaboration networks. The current study builds on this work and aims to map current D&I research advice networks.

Methods: D&I researchers in the United States (US) and Canada were identified through a combination of publication metrics, and key persons identified networks and were invited to participate (n = 1,576). In this social network analysis study, participants completed an online survey identifying up to 10 people from whom they sought and/or gave advice on D&I research. Participants identified four types of advice received: research methods, grant, career, or another type (e.g., work/life balance). We used descriptive statistics to characterize the sample and network metrics and visualizations to describe the composition of advice networks.

Results: A total of 482 individuals completed the survey. Eighty-six (18%) worked in Canada and 396 (82%) in the US. Respondents had varying D&I research expertise levels; 14% beginner expertise, 45% intermediate, 29% advanced, and 12% expert. The advice network included 978 connected nodes/individuals. For all research types, out-degree, or advice giving, was higher for those with advanced or expert-level expertise (6.9 and 11.9, respectively) than those with beginner or intermediate expertise (0.8 and 2.2, respectively). Respondents reporting White race reported giving (out-degree = 5.2) and receiving (in-degree = 6.1) more advice compared to individuals reporting Asian (out-degree = 2.9, in-degree = 5.3), Black (out-degree = 2.3, in-degree = 5.2), or other races (out-degree = 2.5, in-degree = 5.4). Assortativity analyses revealed 98% of network ties came from individuals within the same country. The top two reasons for advice seeking were trusting the individual to give good advice (78%) and the individual's knowledge/experience in specific D&I content (69%).

Conclusions: The D&I research network is becoming more dispersed as the field expands. Findings highlight opportunities to further connect D&I researchers in the US and Canada, individuals with emerging skills in D&I research, and minoritized racial groups. Expanding peer mentoring opportunities, especially for minoritized groups, can enhance the field's capacity for growth.

Ethical issues arise in the context of implementation science that may differ from those encountered in other research settings. This report, developed out of a workshop convened by the Center for Translation Research and Implementation Science within the United States National Heart, Lung, and Blood Institute, identifies six key themes that are important to the assessment of ethical dimensions of implementation science. First, addressing ethical challenges in implementation science does not require new ethical principles, commitments, or regulations. However, it does require understanding of the specific contexts arising in implementation research related to both study design and the intervention being implemented. Second, implementation research involves many different types of people in research, including patients, clinicians, administrators, the social networks of any of these, and the general population. These individuals play different roles that may entail different ethical considerations, obligations, and vulnerabilities. Third, the appropriateness of and need for informed consent in implementation research is connected to the role of the subject/participant, the nature of the intervention, and the design of the study. Even where traditional "full" consent processes are unnecessary or inappropriate, communication and engagement are critical. Similarly, even when research is exempt and informed consent unnecessary, Data Safety and Monitoring Board oversight of implementation studies may be advisable to ensure quality, address unexpected consequences, and identify overwhelming evidence of benefit. Fourth, implementation science is often explicitly designed to encourage specific behaviors and discourage others. There is a need for clarity regarding when efforts at behavioral change enhance or threaten autonomy and how to protect participants whose autonomy is threatened. Fifth, there is significant overlap between implementation science and quality improvement, and the ideal regulatory oversight structure for implementation science remains unclear. It is critical to encourage learning and growth while assuring appropriate protections. Sixth, implementation research takes place across a range of social and cultural contexts. Engagement and collaboration with stakeholders in designing and executing implementation trials and studies- especially when vulnerabilities exist- is essential. Attention to these themes will help ensure that implementation science fulfills its goal of advancing the practice of health care within a sound ethical framework.

Background: Perinatal mental health conditions are the most common complication of pregnancy and childbirth (1 in 8 women). When left untreated, perinatal depression and anxiety adversely affects the entire family with pregnancy complications and negative outcomes including preterm birth, impaired mother-infant bonding, impaired lactation, substance abuse, divorce, suicide, and infanticide. Significant disparities persist in the diagnosis and treatment of perinatal depression and anxiety and these inequities are often intersectional. Preliminary research with stakeholders including community advisory boards, underrepresented and minority birthing people, and state departments of health, demonstrates the importance of social support as a mechanism for reducing disparities in perinatal depression, particularly in rural geographies. Home visiting programs (HVPs) can provide the social support needed to improve mental health outcomes in pregnant and postpartum women. Our project aims to explore the impact of context on the implementation of a mental health intervention, focusing on the lived experiences of diverse populations served by HVPs to reduce disparities in adverse maternal outcomes.

Methods: Using implementation facilitation, our study will engage multilevel stakeholders (e.g., policymakers, front-line implementers, and intervention recipients) to adapt facilitation to integrate a maternal mental health intervention (i.e., Mothers and Babies) across two midwestern, rural states (Iowa and Indiana) with multiple HVP models. Given the complexity and heterogeneity of the contexts in which Mothers and Babies will be integrated, a three variable hybrid implementation-effectiveness-context trial will test the adapted facilitation strategy compared with implementation as usual (i.e., standard education) and will assess contextual factors related to the outcomes. Using an evidence-based implementation strategy that tailors implementation delivery to the needs of the specific populations and context may improve fidelity and adoption, particularly in rural states where residents have limited access to care.

Discussion: The immediate impact of this research will be to show whether adapted facilitation can improve the uptake and fidelity of Mothers and Babies across multiple HVP models and thus positively affect depressive symptoms and perceived stress of recipients. Our implementation protocol may be used by researchers, practitioners, and policy makers to better integrate evidence-based interventions into diverse contexts, leading to more equitable implementation and improved health outcomes.

Trial registration: ClinicalTrials.gov Identifier: NCT06575894, registered on August 29, 2024 https://clinicaltrials.gov/study/NCT06575894?id=NCT06575894&rank=1 .

Objective: To evaluate implementation of a patient decision aid for symptomatic uterine fibroid management to improve shared decision-making at five clinical settings across the United States.

Methods: We used a type 3 hybrid effectiveness-implementation stepped-wedge design and the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) planning and evaluation framework. We conducted clinician training, monthly reach tracking with feedback to site clinical leads, patient and clinician surveys, and visit audio-recordings. Implementation strategies included assessment of organizational readiness for shared decision-making, synchronous clinician training, audit and feedback of decision aid reach, and access to multiple decision aid formats. Outcomes and analyses included patient-level reach, clinician-level adoption, and associations of patient-reported decision aid exposure (as treated) and setting-level implementation (intention-to-treat) with patient-reported (collaboRATE measure) and observed (OPTION-5 measure) shared decision-making. We also designed and assessed setting-level plans for sustainability and other factors impacting sustained decision aid use.

Results: The decision aid was adopted by 72 of the 74 eligible gynecologists (97%) and reached 2553 patients across five settings. CollaboRATE scores improved among patients who reported receiving the decision aid (as-treated analysis, 69% vs. 59%; OR 1.6, 95% CI 1.16-2.27). CollaboRATE scores remained consistent before and after setting-level decision aid implementation (intention-to-treat analysis, 64% vs. 63%; OR 0.86, 95% CI 0.61-1.22). Participants would prefer to receive a decision aid at multiple time points (91.9% before the visit, 90.7% during the visit, 86.5% after the visit). Shared decision-making experiences did not improve when comparing pre vs. post-implementation collaboRATE scores across included settings (intention-to-treat, 64% vs. 63%; OR 0.86, 95% CI 0.61-1.22).

Conclusion: When patients with symptomatic uterine fibroids are given decision aids, they report higher shared decision-making scores. However, the differences we observed between the as-treated and intention-to-treat results suggest that unaddressed implementation challenges continue to limit the extent to which patients receive decision aids and likely hinder their overall impact. Future efforts to implement decision aids should explore enhancing their integration into clinical workflows and standard operating procedures, supported by organizational incentives that prioritize shared decision-making.

Trial registration: ClinicalTrials.gov NCT03985449; registered 6 June 2019.

Background: Research on determinants of health policy implementation is limited, and conceptualizations of evidence and implementation success are evolving in the field. This study aimed to identify determinants of perceived policy implementation success and assess whether these determinants vary according to: (1) how policy implementation success is operationally defined [i.e., broadly vs. narrowly related to evidence-based practice (EBP) reach] and (2) the role of a person's organization in policy implementation. The study focuses on policies that earmark taxes for behavioral health services.

Methods: Web-based surveys of professionals involved with earmarked tax policy implementation were conducted between 2022 and 2023 (N = 272). The primary dependent variable was a 9-item score that broadly assessed perceptions of the tax policy positively impacting multiple dimensions of outcomes. The secondary dependent variable was a single item that narrowly assessed perceptions of the tax policy increasing EBP reach. Independent variables were scores mapped to determinants in the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework. Multiple linear regression estimated associations between measures of determinants and policy implementation success.

Results: Perceptions of tax attributes (innovation determinant), tax EBP implementation climate (inner-context determinant), and inter-agency collaboration in tax policy implementation (outer-context and bridging factor determinant) were significantly associated with perceptions of policy implementation success. However, the magnitude of associations varied according to how success was operationalized and by respondent organization type. For example, the magnitude of the association between tax attributes and implementation success was 42% smaller among respondents at direct service organizations than non-direct service organizations when implementation success was operationalized broadly in terms of generating positive impacts (β = 0.37 vs. β = 0.64), and 61% smaller when success was operationalized narrowly in terms of EBP reach (β = 0.23 vs. β = 0.59). Conversely, when success was operationalized narrowly as EBP reach, the magnitude of the association between EBP implementation climate and implementation success was large and significant among respondents at direct service organizations while it was not significant among respondents from non-direct service organizations (β = 0.48 vs. β=-0.06).

Conclusion: Determinants of perceived policy implementation success may vary according to how policy implementation success is defined and the role of a person's organization in policy implementation. This has implications for implementation science and selecting policy implementation strategies.

Background: Although mobile health (mHealth) interventions have shown promise in improving health outcomes, most of them rarely translate to scale. Prevailing mHealth studies are largely small-sized, short-term and donor-funded pilot studies with limited evidence on their effectiveness. To facilitate scale-up, several frameworks have been proposed to enhance the generic implementation of health interventions. However, there is a lack of a specific focus on the implementation and integration of mHealth interventions in routine care in low-resource settings. Our scoping review aimed to synthesize and develop a framework that could guide the implementation and integration of mHealth interventions.

Methods: We searched the PubMed, Google Scholar, and ScienceDirect databases for published theories, models, and frameworks related to the implementation and integration of clinical interventions from 1st January 2000 to 31st December 2023. The data processing was guided by a scoping review methodology proposed by Arksey and O'Malley. Studies were included if they were i) peer-reviewed and published between 2000 and 2023, ii) explicitly described a framework for clinical intervention implementation and integration, or iii) available in full text and published in English. We integrated different domains and constructs from the reviewed frameworks to develop a new framework for implementing and integrating mHealth interventions.

Results: We identified eight eligible papers with eight frameworks composed of 102 implementation domains. None of the identified frameworks were specific to the integration of mHealth interventions in low-resource settings. Two constructs (skill impartation and intervention awareness) related to the training domain, four constructs (technical and logistical support, identifying committed staff, supervision, and redesigning) from the restructuring domain, two constructs (monetary incentives and nonmonetary incentives) from the incentivize domain, two constructs (organizational mandates and government mandates) from the mandate domain and two constructs (collaboration and routine workflows) from the integrate domain. Therefore, a new framework that outlines five main domains-train, restructure, incentivize, mandate, and integrate (TRIMI)-in relation to the integration and implementation of mHealth interventions in low-resource settings emerged.

Conclusion: The TRIMI framework presents a realistic and realizable solution for the implementation and integration deficits of mHealth interventions in low-resource settings.

Background: The use of rapid qualitative methods has increased substantially over the past decade in quality improvement and health services research. These methods have gained traction in implementation research and practice, wherein real-time adjustments are often made to optimize processes and outcomes. This brisk increase begs the questions: what does rigor entail in projects that use rapid qualitative analysis (RQA)? How do we define a pragmatic framework to help research teams design and conduct rigorous and valid rapid qualitative projects? How can authors articulate rigor in their methods descriptions? Lastly, how can reviewers evaluate the rigor of rapid qualitative projects?.

Methods: A team of seven interdisciplinary qualitative methods experts developed a framework for ensuring rigor and validity in RQA and methods suitable for this analytic approach. We conducted a qualitative evidence synthesis to identify gaps in the literature and then drew upon literature, standard procedures within our teams, and a repository of rapid qualitative training materials to create a planning and reporting framework. We iteratively refined this framework through 11 group working meetings (60-90 minutes each) over the course of one year and invited feedback on items to ensure their completeness, clarity, and comprehensibility.

Results: The Planning for and Assessing Rigor in Rapid Qualitative Analysis (PARRQA) framework is organized progressively across phases from design to dissemination, as follows: 1) rigorous design (rationale and staffing), 2) semi-structured data collection (pilot and planning), 3) RQA: summary template development (accuracy and calibration), 4) RQA: matrix analysis (matrices), and 5) rapid qualitative data synthesis. Eighteen recommendations across these sections specify best practices for rigor and validity.

Conclusions: Rapid qualitative methods play a central role in implementation evaluations, with the potential to yield prompt information and insights about context, processes, and relationships. However, guidance on how to assess rigor is nascent. The PARRQA framework enhances the literature by offering criteria to ensure appropriate planning for and assessment of rigor in projects that involve RQA. This framework provides a consensus-based resource to support high-level qualitative methodological rigor in implementation science.