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Background: Historically, the focus of cost-effectiveness analyses has been on the costs to operate and deliver interventions after their initial design and launch. The costs related to design and implementation of interventions have often been omitted. Ignoring these costs leads to an underestimation of the true price of interventions and biases economic analyses toward favoring new interventions. This is especially true in low- and middle-income countries (LMICs), where implementation may require substantial up-front investment. This scoping review was conducted to explore the topics, depth, and availability of scientific literature on integrating implementation science into economic evaluations of health interventions in LMICs.Methods: We searched Web of Science and PubMed for papers published between January 1, 2010, and December 31, 2021, that included components of both implementation science and economic evaluation. Studies from LMICs were prioritized for review, but papers from high-income countries were included if their methodology/findings were relevant to LMIC settings.Results: Six thousand nine hundred eighty-six studies were screened, of which 55 were included in full-text review and 23 selected for inclusion and data extraction. Most papers were theoretical, though some focused on a single disease or disease subset, including: mental health (n = 5), HIV (n = 3), tuberculosis (n = 3), and diabetes (n = 2). Manuscripts included a mix of methodology papers, empirical studies, and other (e.g., narrative) reviews. Authorship of the included literature was skewed toward high-income settings, with 22 of the 23 papers featuring first and senior authors from high-income countries. Of nine empirical studies included, no consistent implementation cost outcomes were measured, and only four could be mapped to an existing costing or implementation framework. There was also substantial heterogeneity across studies in how implementation costs were defined, and the methods used to collect them.Conclusion: A sparse but growing literature explores the intersection of implementation science and economic evaluation. Key needs include more research in LMICs, greater consensus on the definition of implementation costs, standardized methods to collect such costs, and identifying outcomes of greatest relevance. Addressing these gaps will result in stronger links between implementation science and economic evaluation and will create more robust and accurate estimates of intervention costs.Trial Registration: The protocol for this manuscript was published on the Open Science Framework. It is available at: https://osf.io/ms5fa/ (DOI: 10.17605/OSF.IO/32EPJ). [ABSTRACT FROM AUTHOR]

Background: Venous thromboembolism (VTE) is a preventable medical condition which has substantial impact on patient morbidity, mortality, and disability. Unfortunately, adherence to the published best practices for VTE prevention, based on patient centered outcomes research (PCOR), is highly variable across U.S. hospitals, which represents a gap between current evidence and clinical practice leading to adverse patient outcomes. This gap is especially large in the case of traumatic brain injury (TBI), where reluctance to initiate VTE prevention due to concerns for potentially increasing the rates of intracranial bleeding drives poor rates of VTE prophylaxis. This is despite research which has shown early initiation of VTE prophylaxis to be safe in TBI without increased risk of delayed neurosurgical intervention or death. Clinical decision support (CDS) is an indispensable solution to close this practice gap; however, design and implementation barriers hinder CDS adoption and successful scaling across health systems. Clinical practice guidelines (CPGs) informed by PCOR evidence can be deployed using CDS systems to improve the evidence to practice gap. In the Scaling AcceptabLE cDs (SCALED) study, we will implement a VTE prevention CPG within an interoperable CDS system and evaluate both CPG effectiveness (improved clinical outcomes) and CDS implementation. Methods: The SCALED trial is a hybrid type 2 randomized stepped wedge effectiveness-implementation trial to scale the CDS across 4 heterogeneous healthcare systems. Trial outcomes will be assessed using the RE2-AIM planning and evaluation framework. Efforts will be made to ensure implementation consistency. Nonetheless, it is expected that CDS adoption will vary across each site. To assess these differences, we will evaluate implementation processes across trial sites using the Exploration, Preparation, Implementation, and Sustainment (EPIS) implementation framework (a determinant framework) using mixed-methods. Finally, it is critical that PCOR CPGs are maintained as evidence evolves. To date, an accepted process for evidence maintenance does not exist. We will pilot a "Living Guideline" process model for the VTE prevention CDS system. Discussion: The stepped wedge hybrid type 2 trial will provide evidence regarding the effectiveness of CDS based on the Berne-Norwood criteria for VTE prevention in patients with TBI. Additionally, it will provide evidence regarding a successful strategy to scale interoperable CDS systems across U.S. healthcare systems, advancing both the fields of implementation science and health informatics. Trial registration: Clinicaltrials.gov – NCT05628207. Prospectively registered 11/28/2022, https://classic.clinicaltrials.gov/ct2/show/NCT05628207. [ABSTRACT FROM AUTHOR]

Background: Dual randomized controlled trials (DRCT) are type 2 hybrid studies that include two randomized trials: one testing implementation strategies and one testing an intervention. We argue that this study design offers efficiency by providing rigorous investigation of both implementation and intervention in one study and has potential to accelerate generation of the evidence needed to translate interventions that work into real-world practice. Nevertheless, studies using this design are rare in the literature. Main text: We construct a paradigm that breaks down the components of the DRCT and provide a step-by-step explanation of features of the design and recommendations for use. A clear distinction is made between the dual strands that test the implementation versus the intervention, and a minimum of three randomized arms is advocated. We suggest an active treatment arm that includes both the implementation strategy and intervention that are hypothesized to be superior. We suggest two comparison/control arms: one to test the implementation strategy and the second to test the intervention. Further, we recommend selection criteria for the two control arms that place emphasis on maximizing the utility of the study design to advance public health practice. Conclusions: On the surface, the design of a DRCT can appear simple, but actual application is complex. We believe it is that complexity that has limited its use in the literature. We hope that this paper will give both implementation scientists and trialists who are not familiar with implementation science a better understanding of the DRCT design and encouragement to use it. [ABSTRACT FROM AUTHOR]

Background: The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods. Main text: This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly. Conclusions: Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically. [ABSTRACT FROM AUTHOR]

Background: Evidence-based practices (EBPs) for patients receiving invasive mechanical ventilation vary in the quality of their underlying evidence and ease of implementation. Research question: How do researchers and clinicians prioritize EBPs to help guide clinical decision-making and focus implementation efforts to improve patient care using existing, validated measures? Study design and methods: We developed a 4-step rapid method using existing criteria to prioritize EBPs associated with lower mortality and/or shorter duration of invasive mechanical ventilation for patients suffering from acute respiratory failure or acute respiratory distress syndrome. Using different types of data including surveys, we (1) identified relevant EBPs, (2) rated EBPs using the Guideline Implementability Appraisal (GLIA) tool, (3) surveyed practicing ICU clinicians from different hospital systems using a subset of GLIA criteria, and (4) developed metrics to assess EBP performance. In this paper, we describe steps 2 and 3. Results: In step 2, we prioritized 11 EBPs from an initial list of 30, using surveys and ratings among a small group of clinician researchers. In step 3, 42 clinicians from 8 different hospital systems provided assessments of these 11 EBPs which inform the final step of metric development. Interpretation: Our prioritization process allowed us to identify 11 EBPs out of a larger group that clinicians perceive is most likely to help optimize invasive mechanical ventilation and improve the outcomes of this vulnerable patient population. While this method was developed in critical care related to adults receiving invasive mechanical ventilation, it is adaptable to other health contexts. [ABSTRACT FROM AUTHOR]

This debate paper asserts that implementation science needs to incorporate a key concept from entrepreneurship-market demand-and demonstrates how assessing an innovation's potential market viability might advance the pace and success of innovation adoption and sustainment. We describe key concepts, language distinctions, and questions that entrepreneurs pose to implementation scientists-many of which implementation scientists appear ill-equipped to answer. The paper concludes with recommendations about how concepts from entrepreneurship, notably market viability assessment, can enhance the translation of research discoveries into real-world adoption, sustained use, and population health benefits. The paper further proposes activities that can advance implementation science's capacity to draw from the field of entrepreneurship, along with the data foundations required to assess and cultivate market demand. [ABSTRACT FROM AUTHOR]

There is increasing attention being given to opportunities and approaches to advance health equity using implementation science. To reduce disparities in health, it is crucial that an equity lens is integrated from the earliest stages of the implementation process. In this paper, we outline four key pre-implementation steps and associated questions for implementation researchers to consider that may help guide selection and design of interventions and associated implementation strategies that are most likely to reach and be effective in reducing health disparities among vulnerable persons and communities. [ABSTRACT FROM AUTHOR]

Background: Implementation research may play an important role in reducing research waste by identifying strategies that support translation of evidence into practice. Implementation of healthcare interventions is influenced by multiple factors including the organisational context, implementation strategies and features of the intervention as perceived by people delivering and receiving the intervention. Recently, concepts relating to perceived features of interventions have been gaining traction in published literature, namely, acceptability, fidelity, feasibility, scalability and sustainability. These concepts may influence uptake of healthcare interventions, yet there seems to be little consensus about their nature and impact. The aim of this paper is to develop a testable conceptual framework of implementability of healthcare interventions that includes these five concepts.Methods: A multifaceted approach was used to develop and refine a conceptual framework of implementability of healthcare interventions. An overview of reviews identified reviews published between January 2000 and March 2021 that focused on at least one of the five concepts in relation to a healthcare intervention. These findings informed the development of a preliminary framework of implementability of healthcare interventions which was presented to a panel of experts. A nominal group process was used to critique, refine and agree on a final framework.Results: A total of 252 publications were included in the overview of reviews. Of these, 32% were found to be feasible, 4% reported sustainable changes in practice and 9% were scaled up to other populations and/or settings. The expert panel proposed that scalability and sustainability of a healthcare intervention are dependent on its acceptability, fidelity and feasibility. Furthermore, acceptability, fidelity and feasibility require re-evaluation over time and as the intervention is developed and then implemented in different settings or with different populations. The final agreed framework of implementability provides the basis for a chronological, iterative approach to planning for wide-scale, long-term implementation of healthcare interventions.Conclusions: We recommend that researchers consider the factors acceptability, fidelity and feasibility (proposed to influence sustainability and scalability) during the preliminary phases of intervention development, evaluation and implementation, and iteratively check these factors in different settings and over time. [ABSTRACT FROM AUTHOR]

Abstract Background Historically, the focus of cost-effectiveness analyses has been on the costs to operate and deliver interventions after their initial design and launch. The costs related to design and implementation of interventions have often been omitted. Ignoring these costs leads to an underestimation of the true price of interventions and biases economic analyses toward favoring new interventions. This is especially true in low- and middle-income countries (LMICs), where implementation may require substantial up-front investment. This scoping review was conducted to explore the topics, depth, and availability of scientific literature on integrating implementation science into economic evaluations of health interventions in LMICs. Methods We searched Web of Science and PubMed for papers published between January 1, 2010, and December 31, 2021, that included components of both implementation science and economic evaluation. Studies from LMICs were prioritized for review, but papers from high-income countries were included if their methodology/findings were relevant to LMIC settings. Results Six thousand nine hundred eighty-six studies were screened, of which 55 were included in full-text review and 23 selected for inclusion and data extraction. Most papers were theoretical, though some focused on a single disease or disease subset, including: mental health (n = 5), HIV (n = 3), tuberculosis (n = 3), and diabetes (n = 2). Manuscripts included a mix of methodology papers, empirical studies, and other (e.g., narrative) reviews. Authorship of the included literature was skewed toward high-income settings, with 22 of the 23 papers featuring first and senior authors from high-income countries. Of nine empirical studies included, no consistent implementation cost outcomes were measured, and only four could be mapped to an existing costing or implementation framework. There was also substantial heterogeneity across studies in how implementation costs were defined, and the methods used to collect them. Conclusion A sparse but growing literature explores the intersection of implementation science and economic evaluation. Key needs include more research in LMICs, greater consensus on the definition of implementation costs, standardized methods to collect such costs, and identifying outcomes of greatest relevance. Addressing these gaps will result in stronger links between implementation science and economic evaluation and will create more robust and accurate estimates of intervention costs. Trial registration The protocol for this manuscript was published on the Open Science Framework. It is available at: https://osf.io/ms5fa/ (DOI: 10.17605/OSF.IO/32EPJ).

Background: Germline genetic testing is recommended for an increasing number of conditions with underlying genetic etiologies, the results of which impact medical management. However, genetic testing is underutilized in clinics due to system, clinician, and patient level barriers. Behavioral economics provides a framework to create implementation strategies, such as nudges, to address these multi-level barriers and increase the uptake of genetic testing for conditions where the results impact medical management. Methods: Patients meeting eligibility for germline genetic testing for a group of conditions will be identified using electronic phenotyping algorithms. A pragmatic, type 3 hybrid cluster randomization study will test nudges to patients and/or clinicians, or neither. Clinicians who receive nudges will be prompted to either refer their patient to genetics or order genetic testing themselves. We will use rapid cycle approaches informed by clinician and patient experiences, health equity, and behavioral economics to optimize these nudges before trial initiation. The primary implementation outcome is uptake of germline genetic testing for the pre-selected health conditions. Patient data collected through the electronic health record (e.g. demographics, geocoded address) will be examined as moderators of the effect of nudges. Discussion: This study will be one of the first randomized trials to examine the effects of patient- and clinician-directed nudges informed by behavioral economics on uptake of genetic testing. The pragmatic design will facilitate a large and diverse patient sample, allow for the assessment of genetic testing uptake, and provide comparison of the effect of different nudge combinations. This trial also involves optimization of patient identification, test selection, ordering, and result reporting in an electronic health record-based infrastructure to further address clinician-level barriers to utilizing genomic medicine. The findings may help determine the impact of low-cost, sustainable implementation strategies that can be integrated into health care systems to improve the use of genomic medicine. Trial registration: ClinicalTrials.gov. NCT06377033. Registered on March 31, 2024. https://clinicaltrials.gov/study/NCT06377033?term=NCT06377033&rank=1 [ABSTRACT FROM AUTHOR]

Background: The implementation of clinical practice guidelines (CPGs) is a cyclical process in which the evaluation stage can facilitate continuous improvement. Implementation science has utilized theoretical approaches, such as models and frameworks, to understand and address this process. This article aims to provide a comprehensive overview of the models and frameworks used to assess the implementation of CPGs. Methods: A systematic review was conducted following the Cochrane methodology, with adaptations to the "selection process" due to the unique nature of this review. The findings were reported following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Electronic databases were searched from their inception until May 15, 2023. A predetermined strategy and manual searches were conducted to identify relevant documents from health institutions worldwide. Eligible studies presented models and frameworks for assessing the implementation of CPGs. Information on the characteristics of the documents, the context in which the models were used (specific objectives, level of use, type of health service, target group), and the characteristics of each model or framework (name, domain evaluated, and model limitations) were extracted. The domains of the models were analyzed according to the key constructs: strategies, context, outcomes, fidelity, adaptation, sustainability, process, and intervention. A subgroup analysis was performed grouping models and frameworks according to their levels of use (clinical, organizational, and policy) and type of health service (community, ambulatorial, hospital, institutional). The JBI's critical appraisal tools were utilized by two independent researchers to assess the trustworthiness, relevance, and results of the included studies. Results: Database searches yielded 14,395 studies, of which 80 full texts were reviewed. Eight studies were included in the data analysis and four methodological guidelines were additionally included from the manual search. The risk of bias in the studies was considered non-critical for the results of this systematic review. A total of ten models/frameworks for assessing the implementation of CPGs were found. The level of use was mainly policy, the most common type of health service was institutional, and the major target group was professionals directly involved in clinical practice. The evaluated domains differed between the models and there were also differences in their conceptualization. All the models addressed the domain "Context", especially at the micro level (8/12), followed by the multilevel (7/12). The domains "Outcome" (9/12), "Intervention" (8/12), "Strategies" (7/12), and "Process" (5/12) were frequently addressed, while "Sustainability" was found only in one study, and "Fidelity/Adaptation" was not observed. Conclusions: The use of models and frameworks for assessing the implementation of CPGs is still incipient. This systematic review may help stakeholders choose or adapt the most appropriate model or framework to assess CPGs implementation based on their specific health context. Trial registration: PROSPERO (International Prospective Register of Systematic Reviews) registration number: CRD42022335884. Registered on June 7, 2022. [ABSTRACT FROM AUTHOR]

Abstract This debate paper asserts that implementation science needs to incorporate a key concept from entrepreneurship—market demand—and demonstrates how assessing an innovation’s potential market viability might advance the pace and success of innovation adoption and sustainment. We describe key concepts, language distinctions, and questions that entrepreneurs pose to implementation scientists—many of which implementation scientists appear ill-equipped to answer. The paper concludes with recommendations about how concepts from entrepreneurship, notably market viability assessment, can enhance the translation of research discoveries into real-world adoption, sustained use, and population health benefits. The paper further proposes activities that can advance implementation science’s capacity to draw from the field of entrepreneurship, along with the data foundations required to assess and cultivate market demand.

Background: Implementing evidence that changes practice in emergency departments (EDs) is notoriously difficult due to well-established barriers including high levels of uncertainty arising from undifferentiated nature of ED patients, resource shortages, workload unpredictability, high staff turnover, and a constantly changing environment. We developed and implemented a behaviour-change informed strategy to mitigate these barriers for a clinical trial to implement the evidence-based emergency nursing framework HIRAID® (History including Infection risk, Red flags, Assessment, Interventions, Diagnostics, communication, and reassessment) to reduce clinical variation, and increase safety and quality of emergency nursing care. Aim: To evaluate the behaviour-change-informed HIRAID® implementation strategy on reach, effectiveness, adoption, quality (dose, fidelity) and maintenance (sustainability). Methods: An effectiveness-implementation hybrid design including a step–wedge cluster randomised control trial (SW-cRCT) was used to implement HIRAID® with 1300 + emergency nurses across 29 Australian rural, regional, and metropolitan EDs. Evaluation of our behaviour-change informed strategy was informed by the RE-AIM Scoring Instrument and measured using data from (i) a post HIRAID® implementation emergency nurse survey, (ii) HIRAID® Instructor surveys, and (iii) twelve-week and 6-month documentation audits. Quantitative data were analysed using descriptive statistics to determine the level of each component of RE-AIM achieved. Qualitative data were analysed using content analysis and used to understand the 'how' and 'why' of quantitative results. Results: HIRAID® was implemented in all 29 EDs, with 145 nurses undertaking instructor training and 1123 (82%) completing all four components of provider training at 12 weeks post-implementation. Modifications to the behaviour-change informed strategy were minimal. The strategy was largely used as intended with 100% dose and very high fidelity. We achieved extremely high individual sustainability (95% use of HIRAID® documentation templates) at 6 months and 100% setting sustainability at 3 years. Conclusion: The behaviour-change informed strategy for the emergency nursing framework HIRAID® in rural, regional, and metropolitan Australia was highly successful with extremely high reach and adoption, dose, fidelity, individual and setting sustainability across substantially variable clinical contexts. Trial registration: ANZCTR, ACTRN12621001456842. Registered 25 October 2021. [ABSTRACT FROM AUTHOR]

Background: There are no criteria specifically for evaluating the quality of implementation research and recommending implementation strategies likely to have impact to practitioners. We describe the development and application of the Best Practices Tool, a set of criteria to evaluate the evidence supporting HIV-specific implementation strategies. Methods: We developed the Best Practices Tool from 2022–2023 in three phases. (1) We developed a draft tool and criteria based on a literature review and key informant interviews. We purposively selected and recruited by email interview participants representing a mix of expertise in HIV service delivery, quality improvement, and implementation science. (2) The tool was then informed and revised through two e-Delphi rounds using a survey delivered online through Qualtrics. The first and second round Delphi surveys consisted of 71 and 52 open and close-ended questions, respectively, asking participants to evaluate, confirm, and make suggestions on different aspects of the rubric. After each survey round, data were analyzed and synthesized as appropriate; and the tool and criteria were revised. (3) We then applied the tool to a set of research studies assessing implementation strategies designed to promote the adoption and uptake of evidence-based HIV interventions to assess reliable application of the tool and criteria. Results: Our initial literature review yielded existing tools for evaluating intervention-level evidence. For a strategy-level tool, additions emerged from interviews, for example, a need to consider the context and specification of strategies. Revisions were made after both Delphi rounds resulting in the confirmation of five evaluation domains – research design, implementation outcomes, limitations and rigor, strategy specification, and equity – and four evidence levels – best, promising, more evidence needed, and harmful. For most domains, criteria were specified at each evidence level. After an initial pilot round to develop an application process and provide training, we achieved 98% reliability when applying the criteria to 18 implementation strategies. Conclusions: We developed a tool to evaluate the evidence supporting implementation strategies for HIV services. Although specific to HIV in the US, this tool is adaptable for evaluating strategies in other health areas. [ABSTRACT FROM AUTHOR]

Background: Antibiotics are globally overprescribed for the treatment of upper respiratory tract infections (URTI), especially in persons living with HIV. However, most URTIs are caused by viruses, and antibiotics are not indicated. De-implementation is perceived as an important area of research that can lead to reductions in unnecessary, wasteful, or harmful practices, such as excessive or inappropriate antibiotic use for URTI, through the employment of evidence-based interventions to reduce these practices. Research into strategies that lead to successful de-implementation of unnecessary antibiotic prescriptions within the primary health care setting is limited in Mozambique. In this study, we propose a protocol designed to evaluate the use of a clinical decision support algorithm (CDSA) for promoting the de-implementation of unnecessary antibiotic prescriptions for URTI among ambulatory HIV-infected adult patients in primary healthcare settings. Methods: This study is a multicenter, two-arm, cluster randomized controlled trial, involving six primary health care facilities in Maputo and Matola municipalities in Mozambique, guided by an innovative implementation science framework, the Dynamic Adaption Process. In total, 380 HIV-infected patients with URTI symptoms will be enrolled, with 190 patients assigned to both the intervention and control arms. For intervention sites, the CDSAs will be posted on either the exam room wall or on the clinician´s exam room desk for ease of reference during clinical visits. Our sample size is powered to detect a reduction in antibiotic use by 15%. We will evaluate the effectiveness and implementation outcomes and examine the effect of multi-level (sites and patients) factors in promoting the de-implementation of unnecessary antibiotic prescriptions. The effectiveness and implementation of our antibiotic de-implementation strategy are the primary outcomes, whereas the clinical endpoints are the secondary outcomes. Discussion: This research will provide evidence on the effectiveness of the use of the CDSA in promoting the de-implementation of unnecessary antibiotic prescribing in treating acute URTI, among ambulatory HIV-infected patients. Findings will bring evidence for the need to scale up strategies for the de-implementation of unnecessary antibiotic prescription practices in additional healthcare sites within the country. Trial registration: ISRCTN, ISRCTN88272350. Registered 16 May 2024, https://www.isrctn.com/ISRCTN88272350 [ABSTRACT FROM AUTHOR]

In their article on "Navigating the Field of Implementation Science Towards Maturity: Challenges and Opportunities," Chambers and Emmons describe the rapid growth of implementation science along with remaining challenges. A significant gap remains in training and capacity building. Formats for capacity building include university degree programs, summer training institutes, workshops, and conferences. In this letter, we describe and amplify on five key areas, including the need to (1) identify advanced competencies, (2) increase the volume and reach of trainings, (3) sustain trainings, (4) build equity focused trainings, and (5) develop global capacity. We hope that the areas we highlight will aid in addressing several key challenges to prioritize in future efforts to build greater capacity in implementation science. [ABSTRACT FROM AUTHOR]

Background: Evidence-based interventions (EBIs) often address normative behaviors. If a behavior is also common among clinicians, they may be skeptical about the necessity or effectiveness of an EBI. Alternatively, clinicians' attitudes and behaviors may be misaligned, or they may lack the knowledge and self-efficacy to deliver the EBI. Several EBIs address unhealthy alcohol use, a common and often culturally acceptable behavior. But unhealthy alcohol use may be particularly harmful to people with HIV (PWH). Here, we present an implementation trial using an experiential implementation strategy to address clinicians' knowledge, attitudes, and behaviors. Clinicians receive the experiential intervention before they begin delivering an evidence-based brief alcohol intervention (BAI) to PWH with unhealthy alcohol use. Methods: Design: In this hybrid type 3 implementation-effectiveness cluster randomized controlled trial, ART clinics (n = 30) will be randomized 1:1 to facilitation, a flexible strategy to address implementation barriers, or facilitation plus the experiential brief alcohol intervention (EBAI). In the EBAI arm, clinicians, irrespective of their alcohol use, will be offered the BAI as experiential learning. EBAI will address clinicians' alcohol-related attitudes and behaviors and increase their knowledge and confidence to deliver the BAI. Participants: ART clinic staff will be enrolled and assessed at pre-BAI training, post-BAI training, 3, 12, and 24 months. All PWH at the ART clinics who screen positive for unhealthy alcohol use will be offered the BAI. A subset of PWH (n = 810) will be enrolled and assessed at baseline, 3, and 12 months. Outcomes: We will compare implementation outcomes (acceptability, fidelity, penetration, costs, and sustainability) and effectiveness outcomes (viral suppression and alcohol use) between the two arms. We will assess the impact of site-level characteristics on scaling-up the BAI. We will also evaluate how experiencing the BAI affected clinical staff's alcohol use and clinic-level alcohol expectations in the EBAI arm. Discussion: This trial contributes to implementation science by testing a novel strategy to implement a behavior change intervention in a setting in which clinicians themselves may engage in the behavior. Experiential learning may be useful to address normative and difficult to change lifestyle behaviors that contribute to chronic diseases. Trial Registration: NCT06358885 (04/10/2024), https://clinicaltrials.gov/study/NCT06358885. [ABSTRACT FROM AUTHOR]

Background: HIV burden in the US among people who inject drugs (PWID) is driven by overlapping syndemic factors such as co-occurring health needs and environmental factors that synergize to produce worse health outcomes among PWID. This includes stigma, poverty, and limited healthcare access (e.g. medication to treat/prevent HIV and for opioid use disorder [MOUD]). Health services to address these complex needs, when they exist, are rarely located in proximity to each other or to the PWID who need them. Given the shifting drug use landscapes and geographic heterogeneity in the US, we evaluate a data-driven approach to guide the delivery of such services to PWID in local communities. Methods: We used a hybrid, type I, embedded, mixed method, data-driven approach to identify and characterize viable implementation neighborhoods for the HPTN 094 complex intervention, delivering integrated MOUD and HIV treatment/prevention through a mobile unit to PWID across five US cities. Applying the PRISM framework, we triangulated geographic and observational pre-implementation phase data (epidemiological overdose and HIV surveillance data) with two years of implementation phase data (weekly ecological assessments, study protocol meetings) to characterize environmental factors that affected the viability of implementation neighborhoods over time and across diverse settings. Results: Neighborhood-level drug use and geographic diversity alongside shifting socio-political factors (policing, surveillance, gentrification) differentially affected the utility of epidemiological data in identifying viable implementation neighborhoods across sites. In sites where PWID are more geographically dispersed, proximity to structural factors such as public transportation and spaces where PWID reside played a role in determining suitable implementation sites. The utility of leveraging additional data from local overdose and housing response systems to identify viable implementation neighborhoods was mixed. Conclusions: Our findings suggest that data-driven approaches provide a contextually relevant pragmatic strategy to guide the real-time implementation of integrated care models to better meet the needs of PWID and help inform the scale-up of such complex interventions. This work highlights the utility of implementation science methods that attend to the impact of local community environmental factors on the implementation of complex interventions to PWID across diverse drug use, sociopolitical, and geographic landscapes in the US. Trial registration: ClincalTrials.gov, Registration Number: NCT04804072. Registered 18 February 2021. [ABSTRACT FROM AUTHOR]

Humanitarian assistance is hindered by a lack of strategies to optimize care delivery through research and organized networks. Distinct from global health, humanitarian assistance struggles to address its multifaceted challenges, including duplicative resources, uncoordinated communication, unregulated staff expertise and safety, financial waste, and poor-quality metrics and care delivery. Implementation science provides an exciting and underutilized approach that can be applied to address these challenges, by studying how to effectively design, implement, optimize, and scale evidence-based interventions. Though successful in well-resourced and global health systems, implementation science approaches are rare in humanitarian assistance. Adopting implementation science approaches including identifying determinants, creating accessible evidence-based intervention bundles, adapting study methodologies for the humanitarian context, and partnering with implementation experts could make these promising approaches more accessible for thousands of humanitarian actors delivering healthcare for millions of vulnerable patients worldwide. [ABSTRACT FROM AUTHOR]

Abstract Background The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods. Main text This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of “why” the field of implementation science should consider artificial intelligence, for “what” (the purpose and methods), and the “what” (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly. Conclusions Artificial intelligence holds promise to advance implementation science methods (“why”) and accelerate its goals of closing the evidence-to-practice gap (“purpose”). However, evaluation of artificial intelligence’s potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.

Background: Familial hypercholesterolemia (FH) is a heritable disorder affecting 1.3 million individuals in the USA. Eighty percent of people with FH are undiagnosed, particularly minoritized populations including Black or African American people, Asian or Asian American people, and women across racial groups. Family cascade screening is an evidence-based practice that can increase diagnosis and improve health outcomes but is rarely implemented in routine practice, representing an important care gap. In pilot work, we leveraged best practices from behavioral economics and implementation science—including mixed-methods contextual inquiry with clinicians, patients, and health system constituents—to co-design two patient-facing implementation strategies to address this care gap: (a) an automated health system-mediated strategy and (b) a nonprofit foundation-mediated strategy with contact from a foundation-employed care navigator. This trial will test the comparative effectiveness of these strategies on completion of cascade screening for relatives of individuals with FH, centering equitable reach. Methods: We will conduct a hybrid effectiveness-implementation type III randomized controlled trial testing the comparative effectiveness of two strategies for implementing cascade screening with 220 individuals with FH (i.e., probands) per arm identified from a large northeastern health system. The primary implementation outcome is reach, or the proportion of probands with at least one first-degree biological relative (parent, sibling, child) in the USA who is screened for FH through the study. Our secondary implementation outcomes include the number of relatives screened and the number of relatives meeting the American Heart Association criteria for FH. Our secondary clinical effectiveness outcome is post-trial proband cholesterol level. We will also use mixed methods to identify implementation strategy mechanisms for implementation strategy effectiveness while centering equity. Discussion: We will test two patient-facing implementation strategies harnessing insights from behavioral economics that were developed collaboratively with constituents. This trial will improve our understanding of how to implement evidence-based cascade screening for FH, which implementation strategies work, for whom, and why. Learnings from this trial can be used to equitably scale cascade screening programs for FH nationally and inform cascade screening implementation efforts for other genetic disorders. Trial registration: ClinicalTrials.gov, NCT05750667. Registered 15 February 2023—retrospectively registered, https://clinicaltrials.gov/study/NCT05750667. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI), particularly generative AI, has emerged as a transformative tool in healthcare, with the potential to revolutionize clinical decision-making and improve health outcomes. Generative AI, capable of generating new data such as text and images, holds promise in enhancing patient care, revolutionizing disease diagnosis and expanding treatment options. However, the utility and impact of generative AI in healthcare remain poorly understood, with concerns around ethical and medico-legal implications, integration into healthcare service delivery and workforce utilisation. Also, there is not a clear pathway to implement and integrate generative AI in healthcare delivery. Methods: This article aims to provide a comprehensive overview of the use of generative AI in healthcare, focusing on the utility of the technology in healthcare and its translational application highlighting the need for careful planning, execution and management of expectations in adopting generative AI in clinical medicine. Key considerations include factors such as data privacy, security and the irreplaceable role of clinicians' expertise. Frameworks like the technology acceptance model (TAM) and the Non-Adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) model are considered to promote responsible integration. These frameworks allow anticipating and proactively addressing barriers to adoption, facilitating stakeholder participation and responsibly transitioning care systems to harness generative AI's potential. Results: Generative AI has the potential to transform healthcare through automated systems, enhanced clinical decision-making and democratization of expertise with diagnostic support tools providing timely, personalized suggestions. Generative AI applications across billing, diagnosis, treatment and research can also make healthcare delivery more efficient, equitable and effective. However, integration of generative AI necessitates meticulous change management and risk mitigation strategies. Technological capabilities alone cannot shift complex care ecosystems overnight; rather, structured adoption programs grounded in implementation science are imperative. Conclusions: It is strongly argued in this article that generative AI can usher in tremendous healthcare progress, if introduced responsibly. Strategic adoption based on implementation science, incremental deployment and balanced messaging around opportunities versus limitations helps promote safe, ethical generative AI integration. Extensive real-world piloting and iteration aligned to clinical priorities should drive development. With conscientious governance centred on human wellbeing over technological novelty, generative AI can enhance accessibility, affordability and quality of care. As these models continue advancing rapidly, ongoing reassessment and transparent communication around their strengths and weaknesses remain vital to restoring trust, realizing positive potential and, most importantly, improving patient outcomes. [ABSTRACT FROM AUTHOR]

Background: The field of implementation science has significantly expanded in size and scope over the past two decades, although work related to understanding implementation processes have of course long preceded the more systematic efforts to improve integration of evidence-based interventions into practice settings. While this growth has had significant benefits to research, practice, and policy, there are some clear challenges that this period of adolescence has uncovered. Main body: This invited commentary reflects on the development of implementation science, its rapid growth, and milestones in its establishment as a viable component of the biomedical research enterprise. The authors reflect on progress in research and training, and then unpack some of the consequences of rapid growth, as the field has grappled with the competing challenges of legitimacy among the research community set against the necessary integration and engagement with practice and policy partners. The article then enumerates a set of principles for the field's next developmental stage and espouses the aspirational goal of a "big tent" to support the next generation of impactful science. Conclusion: For implementation science to expand its relevance and impact to practice and policy, researchers must not lose sight of the original purpose of the field—to support improvements in health and health care at scale, the importance of building a community of research and practice among key partners, and the balance of rigor, relevance, and societal benefit. [ABSTRACT FROM AUTHOR]

Background: Despite the increased risk of cervical cancer (CC) among women living with HIV (WLHIV), CC screening and treatment (CCST) rates remain low in Africa. The integration of CCST services into established HIV programs in Africa can improve CC prevention and control. However, the paucity of evidence on effective implementation strategies (IS) has limited the success of integration in many countries. In this study, we seek to identify effective IS to enhance the integration of CCST services into existing HIV programs in Nigeria. Methods: Our proposed study has formative and experimental activities across the four phases of the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework. Through an implementation mapping conducted with stakeholders in the exploration phase, we identified a core package of IS (Core) and an enhanced package of IS (Core+) mostly selected from the Expert Recommendations for Implementing Change. In the preparation phase, we refined and tailored the Core and Core+ IS with the implementation resource teams for local appropriateness. In the implementation phase, we will conduct a cluster-randomized hybrid type III trial to assess the comparative effectiveness of Core versus Core+. HIV comprehensive treatment sites (k = 12) will be matched by region and randomized to Core or Core+ in the ratio of 1:1 stratified by region. In the sustainment phase, we will assess the sustainment of CCST at each site. The study outcomes will be assessed using RE-AIM: reach (screening rate), adoption (uptake of IS by study sites), IS fidelity (degree to which the IS occurred according to protocol), clinical intervention fidelity (delivery of CC screening, onsite treatment, and referral according to protocol), clinical effectiveness (posttreatment screen negative), and sustainment (continued integrated CCST service delivery). Additionally, we will descriptively explore potential mechanisms, including organizational readiness, implementation climate, CCST self-efficacy, and implementation intentions. Discussion: The assessment of IS to increase CCST rates is consistent with the global plan of eliminating CC as a public health threat by 2030. Our study will identify a set of evidence-based IS for low-income settings to integrate evidence-based CCST interventions into routine HIV care in order to improve the health and life expectancy of WLHIV. Trial registration: Prospectively registered on November 7, 2023, at ClinicalTrials.gov no. NCT06128304. https://classic.clinicaltrials.gov/ct2/show/study/NCT06128304 [ABSTRACT FROM AUTHOR]

Introduction: Clinical practice guidelines recommend against the routine use of psychotropic medications in residential aged care facilities (RACFs). Knowledge brokers are individuals or groups who facilitate the transfer of knowledge into practice. The objective of this trial is to evaluate the effectiveness and cost-effectiveness of using knowledge brokers to translate Australia's new Clinical Practice Guidelines for the Appropriate Use of Psychotropic Medications in People Living with Dementia and in Residential Aged Care. Methods and analysis: The Evidence-based Medication knowledge Brokers in Residential Aged CarE (EMBRACE) trial is a helix-counterbalanced randomised controlled trial. The 12-month trial will be conducted in up to 19 RACFs operated by four Australian aged care provider organisations in Victoria, New South Wales, Western Australia and Queensland. RACFs will be randomised to receive three levels of implementation strategies (knowledge broker service, pharmacist-led quality use of medications education activities and distribution of the Guidelines and supporting materials) across three medication contexts (antipsychotics, benzodiazepines and antidepressants). Implementation strategies will be delivered by an embedded on-site aged care pharmacist working at a system level across each participating RACF. All RACFs will receive all implementation strategies simultaneously but for different medication contexts. The primary outcome will be a composite dichotomous measure of 6-month RACF-level concordance with Guideline recommendations and good practice statements among people using antipsychotics, benzodiazepines and antidepressants for changed behaviours. Secondary outcomes will include proportion of residents with Guideline concordant use of antipsychotics, benzodiazepines and antidepressants measured at the RACF-level and proportion of residents with psychotropic medication use, hospitalisation, falls, falls with injury, polypharmacy, quality of life, activities of daily living, medication incidents and behavioural incidents measured at the RACF-level. Discussion: The EMBRACE trial investigates a novel guideline implementation strategy to improve the safe and effective use of psychotropic medications in RACFs. We anticipate that the findings will provide new information on the potential role of knowledge brokers for successful and cost-effective guideline implementation. Trial registration: Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12623001141639. Registered 6 November 2023 — retrospectively registered, https://www.anzctr.org.au/TrialSearch.aspx. [ABSTRACT FROM AUTHOR]

Background: The United States (US) continues to face decades-long increases in opioid overdose fatalities. As an opioid overdose reversal medication, naloxone can dramatically reduce opioid overdose mortality rates when distributed to people likely to experience or witness an opioid overdose and packaged with education on its use, known as overdose education and naloxone distribution (OEND). Syringe services programs (SSPs) are ideal venues for OEND with staff who are culturally competent in providing services for people who are at risk of experiencing or observing an opioid overdose. We carried out a randomized controlled trial of SSPs to understand the effectiveness of the organize and mobilize for implementation effectiveness (OMIE) approach at improving OEND implementation effectiveness within SSPs. Methods: Using simple randomization, 105 SSPs were enrolled into the trial and assigned to one of two study arms — (1) dissemination of OEND best practice recommendations (Control SSPs) or the OMIE approach along with dissemination of the OEND best practice recommendations (i.e., OMIE SSPs). OMIE SSPs could participate in 60-min OMIE sessions once a month for up to 12 months. At 12-month post-baseline, 102 of 105 SSPs (97%) responded to the follow-up survey. Results: The median number of sessions completed by OMIE SSPs was 10. Comparing OMIE SSPs to control SSPs, we observed significant increases in the number of participants receiving naloxone (incidence rate ratio: 2.15; 95% CI: 1.42, 3.25; p < 0.01) and the rate of naloxone doses distributed per SSP participant (adjusted incidence rate ratio: 1.97; 95% CI: 1.18, 3.30; p = 0.01). We observed no statistically significant difference in the number of adopted best practices between conditions (difference in means 0.2, 95% CI: − 0.7, 1.0; p = 0.68). We also observed a threshold effect where SSPs receiving a higher OMIE dose had greater effect sizes with regard to the number of people given naloxone and the number of naloxone doses distributed. Conclusions: In conclusion, the multifaceted OMIE approach was effective at increasing naloxone distribution from SSPs, despite substantial external shocks during the trial. These findings have major implications for addressing the overdose crisis, which has continued unabated for decades. Trial registration: ClinicalTrials.gov, NCT03924505. Registered 19 April 2019. [ABSTRACT FROM AUTHOR]

Background: This study’s goal is to identify the existing variation in how, why, and by whom anthropological practice is conducted as part of implementation science projects. As doctorally trained anthropologists, we sought to characterize how and why the term “ethnography” was variously applied in the implementation science literature and characterize the practice of anthropology within and across the field. Methods: While we follow the PRISMA-ScR checklist, we present the work with a narrative approach to accurately reflect our review process. A health services librarian developed a search strategy using subject headings and keywords for the following databases: PubMed, Embase (Elsevier), Cochrane CENTRAL (Wiley), CIHAHL (EBSCO), PsycINFO (EBSCO), Web of Science Core Collection, and Anthropology Plus (EBSCO). We focused on the practice of anthropology in implementation research conducted in a healthcare setting, in English, with no date restrictions. Studies were included if they applied one or several elements of anthropological methods in terms of study design, data collection, and/or analysis. Results: The database searches produced 3450 results combined after duplicates were removed, which were added to Rayyan for two rounds of screening by title and abstract. A total of 487 articles were included in the full-text screening. Of these, 227 were included and received data extraction that we recorded and analyzed with descriptive statistics in three main domains: (1) anthropological methods; (2) implementation science methods; and (3) study context. We found the use of characteristic tools of anthropology like ethnography and field notes are usually not systematically described but often mentioned. Further, we found that research design decisions and compromises (e.g., length of time in the field, logistics of stakeholder involvement, reconciling diverse firsthand experiences) that often impact anthropological approaches are not systematically described. Conclusions: Anthropological work often supports larger, mixed-methods implementation projects without being thoroughly reported. Context is essential to anthropological practice and implicitly fundamental to implementation research, yet the goals of anthropology and how its practice informs larger research projects are often not explicitly stated. [ABSTRACT FROM AUTHOR]

Abstract Background Dual randomized controlled trials (DRCT) are type 2 hybrid studies that include two randomized trials: one testing implementation strategies and one testing an intervention. We argue that this study design offers efficiency by providing rigorous investigation of both implementation and intervention in one study and has potential to accelerate generation of the evidence needed to translate interventions that work into real-world practice. Nevertheless, studies using this design are rare in the literature. Main text We construct a paradigm that breaks down the components of the DRCT and provide a step-by-step explanation of features of the design and recommendations for use. A clear distinction is made between the dual strands that test the implementation versus the intervention, and a minimum of three randomized arms is advocated. We suggest an active treatment arm that includes both the implementation strategy and intervention that are hypothesized to be superior. We suggest two comparison/control arms: one to test the implementation strategy and the second to test the intervention. Further, we recommend selection criteria for the two control arms that place emphasis on maximizing the utility of the study design to advance public health practice. Conclusions On the surface, the design of a DRCT can appear simple, but actual application is complex. We believe it is that complexity that has limited its use in the literature. We hope that this paper will give both implementation scientists and trialists who are not familiar with implementation science a better understanding of the DRCT design and encouragement to use it.

Background: Unwarranted clinical variation in hospital care includes the underuse, overuse, or misuse of services. Audit and feedback is a common strategy to reduce unwarranted variation, but its effectiveness varies widely across contexts. We aimed to identify implementation strategies, mechanisms, and contextual circumstances contributing to the impact of audit and feedback on unwarranted clinical variation. Methods: Realist study examining a state-wide value-based healthcare program implemented between 2017 and 2021 in New South Wales, Australia. Three initiatives within the program included audit and feedback to reduce unwarranted variation in inpatient care for different conditions. Multiple data sources were used to formulate the initial audit and feedback program theory: a systematic review, realist review, program document review, and informal discussions with key program stakeholders. Semi-structured interviews were then conducted with 56 participants to refute, refine, or confirm the initial program theories. Data were analysed retroductively using a context-mechanism-outcome framework for 11 transcripts which were coded into the audit and feedback program theory. The program theory was validated with three expert panels: senior health leaders (n = 19), Agency for Clinical Innovation (n = 11), and Ministry of Health (n = 21) staff. Results: The program's audit and feedback implementation strategy operated through eight mechanistic processes. The strategy worked well when clinicians (1) felt ownership and buy-in, (2) could make sense of the information provided, (3) were motivated by social influence, and (4) accepted responsibility and accountability for proposed changes. The success of the strategy was constrained when the audit process led to (5) rationalising current practice instead of creating a learning opportunity, (6) perceptions of unfairness and concerns about data integrity, 7) development of improvement plans that were not followed, and (8) perceived intrusions on professional autonomy. Conclusions: Audit and feedback strategies may help reduce unwarranted clinical variation in care where there is engagement between auditors and local clinicians, meaningful audit indicators, clear improvement plans, and respect for clinical expertise. We contribute theoretical development for audit and feedback by proposing a Model for Audit and Feedback Implementation at Scale. Recommendations include limiting the number of audit indicators, involving clinical staff and local leaders in feedback, and providing opportunities for reflection. [ABSTRACT FROM AUTHOR]

Background: Increased breast density augments breast cancer risk and reduces mammography sensitivity. Supplemental breast MRI screening can significantly increase cancer detection among women with dense breasts. However, few women undergo this exam, and screening is consistently lower among racially minoritized populations. Implementation strategies informed by behavioral economics ("nudges") can promote evidence-based practices by improving clinician decision-making under conditions of uncertainty. Nudges directed toward clinicians and patients may facilitate the implementation of supplemental breast MRI. Methods: Approximately 1600 patients identified as having extremely dense breasts after non-actionable mammograms, along with about 1100 clinicians involved with their care at 32 primary care or OB/GYN clinics across a racially diverse academically based health system, will be enrolled. A 2 × 2 randomized pragmatic trial will test nudges to patients, clinicians, both, or neither to promote supplemental breast MRI screening. Before implementation, rapid cycle approaches informed by clinician and patient experiences and behavioral economics and health equity frameworks guided nudge design. Clinicians will be clustered into clinic groups based on existing administrative departments and care patterns, and these clinic groups will be randomized to have the nudge activated at different times per a stepped wedge design. Clinicians will receive nudges integrated into the routine mammographic report or sent through electronic health record (EHR) in-basket messaging once their clinic group (i.e., wedge) is randomized to receive the intervention. Independently, patients will be randomized to receive text message nudges or not. The primary outcome will be defined as ordering or scheduling supplemental breast MRI. Secondary outcomes include MRI completion, cancer detection rates, and false-positive rates. Patient sociodemographic information and clinic-level variables will be examined as moderators of nudge effectiveness. Qualitative interviews conducted at the trial's conclusion will examine barriers and facilitators to implementation. Discussion: This study will add to the growing literature on the effectiveness of behavioral economics-informed implementation strategies to promote evidence-based interventions. The design will facilitate testing the relative effects of nudges to patients and clinicians and the effects of moderators of nudge effectiveness, including key indicators of health disparities. The results may inform the introduction of low-cost, scalable implementation strategies to promote early breast cancer detection. Trial registration: ClinicalTrials.gov NCT05787249. Registered on March 28, 2023. [ABSTRACT FROM AUTHOR]

Background: Germline genetic testing is recommended by the National Comprehensive Cancer Network (NCCN) for individuals including, but not limited to, those with a personal history of ovarian cancer, young-onset (< 50 years) breast cancer, and a family history of ovarian cancer or male breast cancer. Genetic testing is underused overall, and rates are consistently lower among Black and Hispanic populations. Behavioral economics-informed implementation strategies, or nudges, directed towards patients and clinicians may increase the use of this evidence-based clinical practice. Methods: Patients meeting eligibility for germline genetic testing for breast and ovarian cancer will be identified using electronic phenotyping algorithms. A pragmatic cohort study will test three sequential strategies to promote genetic testing, two directed at patients and one directed at clinicians, deployed in the electronic health record (EHR) for patients in OB-GYN clinics across a diverse academic medical center. We will use rapid cycle approaches informed by relevant clinician and patient experiences, health equity, and behavioral economics to optimize and de-risk our strategies and methods before trial initiation. Step 1 will send patients messages through the health system patient portal. For non-responders, step 2 will reach out to patients via text message. For non-responders, Step 3 will contact patients' clinicians using a novel "pend and send" tool in the EHR. The primary implementation outcome is engagement with germline genetic testing for breast and ovarian cancer predisposition, defined as a scheduled genetic counseling appointment. Patient data collected through the EHR (e.g., race/ethnicity, geocoded address) will be examined as moderators of the impact of the strategies. Discussion: This study will be one of the first to sequentially examine the effects of patient- and clinician-directed strategies informed by behavioral economics on engagement with breast and ovarian cancer genetic testing. The pragmatic and sequential design will facilitate a large and diverse patient sample, allow for the assessment of incremental gains from different implementation strategies, and permit the assessment of moderators of strategy effectiveness. The findings may help determine the impact of low-cost, highly transportable implementation strategies that can be integrated into healthcare systems to improve the use of genomic medicine. Trial registration: ClinicalTrials.gov. NCT05721326. Registered February 10, 2023. https://www.clinicaltrials.gov/study/NCT05721326 [ABSTRACT FROM AUTHOR]

Background: The status of randomised controlled trials (RCTs) as the 'gold standard' for evaluating efficacy in healthcare interventions is increasingly debated among the research community, due to often insufficient consideration for implementation. Normalisation Process Theory (NPT), which focuses on the work required to embed processes into practice, offers a potentially useful framework for addressing these concerns. While the theory has been deployed in numerous RCTs to date, more work is needed to consolidate understanding of if, and how, NPT may aid implementation planning and processes within RCTs. Therefore, this review seeks to understand how NPT contributes to understanding the dynamics of implementation processes within RCTs. Specifically, this review will identify and characterise NPT operationalisation, benefits and reported challenges and limitations in RCTs. Methods: A qualitative systematic review with narrative synthesis of peer-reviewed journal articles from eight databases was conducted. Studies were eligible for inclusion if they reported sufficient detail on the use of NPT within RCTs in a healthcare domain. A pre-specified data extraction template was developed based on the research questions of this review. A narrative synthesis was performed to identify recurrent findings. Results: Searches identified 48 articles reporting 42 studies eligible for inclusion. Findings suggest that NPT is primarily operationalised prospectively during the data collection stage, with limited sub-construct utilisation overall. NPT is beneficial in understanding implementation processes by aiding the identification and analysis of key factors, such as understanding intervention fidelity in real-world settings. Nearly three-quarters of studies failed to report the challenges and limitations of utilising NPT, though coding difficulties and data falling outside the NPT framework are most common. Conclusions: NPT appears to be a consistent and generalisable framework for explaining the dynamics of implementation processes within RCTs. However, operationalisation of the theory to its full extent is necessary to improve its use in practice, as it is currently deployed in varying capacities. Recommendations for future research include investigation of NPT alongside other frameworks, as well as earlier operationalisation and greater use of NPT sub-constructs. Trial Registration: The protocol for this systematic review was accepted for public registration on PROSPERO (registration number: CRD42022345427) on 26 July 2022. [ABSTRACT FROM AUTHOR]

Background: Routine data are increasingly used in randomised controlled trials evaluating healthcare interventions. They can aid participant identification, outcome assessment, and intervention delivery. Randomised implementation trials evaluate the effect of implementation strategies on implementation outcomes. Implementation strategies, such as reminders, are used to increase the uptake of evidence-based interventions into practice, while implementation outcomes, such as adoption, are key measures of the implementation process. The use of routine data in effectiveness trials has been explored; however, there are no reviews on implementation trials. We therefore aimed to describe how routine data have been used in randomised implementation trials and the design characteristics of these trials. Methods: We searched MEDLINE (Ovid) and Cochrane Central Register of Controlled Trials from Jan 2000 to Dec 2021 and manually searched protocols from trial registers. We included implementation trials and type II and type III hybrid effectiveness-implementation trials conducted using routine data. We extracted quantitative and qualitative data and narratively synthesised findings. Results: From 4206 titles, we included 80 trials, of which 22.5% targeted implementation of evidence-based clinical guidelines. Multicomponent implementation strategies were more commonly evaluated (70.0%) than single strategies. Most trials assessed adoption as the primary outcome (65.0%). The majority of trials extracted data from electronic health records (EHRs) (62.5%), and 91.3% used routine data for outcome ascertainment. Reported reasons for using routine data were increasing efficiency, assessing outcomes, reducing research burden, improving quality of care, identifying study samples, confirming findings, and assessing representativeness. Data quality, the EHR system, research governance, and external factors such as government policy could act either as facilitators or barriers. Conclusions: Adherence to guidance on designing and reporting implementation studies, and specifically to harmonise the language used in describing implementation strategies and implementation outcomes, would aid identification of studies and data extraction. Routine healthcare data are widely used for participant identification, outcome assessment and intervention delivery. Researchers should familiarise themselves with the barriers and facilitators to using routine data, and efforts could be made to improve data quality to overcome some of the barriers. Registration: PROSPERO CRD42022292321. [ABSTRACT FROM AUTHOR]

Background: A proliferation of theories, models, and frameworks (TMFs) have been developed in the implementation science field to facilitate the implementation process. The basic features of these TMFs have been identified by several reviews. However, systematic appraisals on the quality of these TMFs are inadequate. To fill this gap, this study aimed to assess the usability, applicability, and testability of the current TMFs in a structured way. Methods: A scoping review method was employed. Electronic databases were searched to locate English and Chinese articles published between January 2000 and April 2022. Search terms were specific to implementation science. Additionally, hand searches were administered to identify articles from related reviews. Purpose and characteristics such as the type of TMF, analytical level, and observation unit were extracted. Structured appraisal criteria were adapted from Birken et al.'s Theory Comparison and Selection Tool (T-CaST) to conduct an in-depth analysis of the TMFs' usability, applicability, and testability. Results: A total of 143 TMFs were included in this analysis. Among them, the most common purpose was to identify barriers and facilitators. Most TMFs applied the descriptive method to summarize the included constructs or the prescriptive method to propose courses of implementation actions. TMFs were mainly mid-range theories built on existing conceptual frameworks or demonstrated grand theories. The usability of the TMFs needs to be improved in terms of the provision of conceptually matched strategies to barriers and facilitators and instructions on the TMFs usage. Regarding the applicability, little attention was paid to the constructs of macro-level context, stages of scale-up and sustainability, and implementation outcomes like feasibility, cost, and penetration. Also, fewer TMFs could propose recommended research and measurement methods to apply the TMFs. Lastly, explicit hypotheses or propositions were lacking in most of the TMFs, and empirical evidence was lacking to support the claimed mechanisms between framework elements in testability. Conclusions: Common limitations were found in the usability, application, and testability of the current TMFs. The findings of this review could provide insights for developers of TMFs for future theoretical advancements. [ABSTRACT FROM AUTHOR]

Implementation researchers often find themselves as research partners in practice improvement projects, clinical trials or other applied health studies. The implementation science component in these projects can be described as supportive, descriptive or explanatory. This commentary reflects on the potential contributions of such projects to implementation science. They may provide evidence on implementation strategies, so it is essential to identify and evaluate these separately from the clinical and preventive interventions of interest. The use of theory on implementation processes and associated factors can contribute to knowledge accumulation, particularly if the focus is on what actually gets implemented when, why and how. The development and validation of relevant measures is a third potential contribution to implementation science. Although not all issues in implementation science can be addressed in this way, capitalization on the opportunities beyond the field can contribute to implementation science. [ABSTRACT FROM AUTHOR]

Background: Successful implementation and utilization of Computerized Clinical Decision Support Systems (CDSS) in hospitals is complex and challenging. Implementation science, and in particular the Nonadoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework, may offer a systematic approach for identifying and addressing these challenges. This review aimed to identify, categorize, and describe barriers and facilitators to CDSS implementation in hospital settings and map them to the NASSS framework. Exploring the applicability of the NASSS framework to CDSS implementation was a secondary aim. Methods: Electronic database searches were conducted (21 July 2020; updated 5 April 2022) in Ovid MEDLINE, Embase, Scopus, PyscInfo, and CINAHL. Original research studies reporting on measured or perceived barriers and/or facilitators to implementation and adoption of CDSS in hospital settings, or attitudes of healthcare professionals towards CDSS were included. Articles with a primary focus on CDSS development were excluded. No language or date restrictions were applied. We used qualitative content analysis to identify determinants and organize them into higher-order themes, which were then reflexively mapped to the NASSS framework. Results: Forty-four publications were included. These comprised a range of study designs, geographic locations, participants, technology types, CDSS functions, and clinical contexts of implementation. A total of 227 individual barriers and 130 individual facilitators were identified across the included studies. The most commonly reported influences on implementation were fit of CDSS with workflows (19 studies), the usefulness of the CDSS output in practice (17 studies), CDSS technical dependencies and design (16 studies), trust of users in the CDSS input data and evidence base (15 studies), and the contextual fit of the CDSS with the user's role or clinical setting (14 studies). Most determinants could be appropriately categorized into domains of the NASSS framework with barriers and facilitators in the "Technology," "Organization," and "Adopters" domains most frequently reported. No determinants were assigned to the "Embedding and Adaptation Over Time" domain. Conclusions: This review identified the most common determinants which could be targeted for modification to either remove barriers or facilitate the adoption and use of CDSS within hospitals. Greater adoption of implementation theory should be encouraged to support CDSS implementation. [ABSTRACT FROM AUTHOR]

Background: Disentangling the interplay between experience-based intuition and theory-informed implementation is crucial for identifying the direct contribution theory can make for generating behaviour changes needed for successful evidence translation. In the context of 'clinicogenomics', a complex and rapidly evolving field demanding swift practice change, we aimed to (a) describe a combined clinician intuition- and theory-driven method for identifying determinants of and strategies for implementing clinicogenomics, and (b) articulate a structured approach to standardise hypothesised behavioural pathways and make potential underlying theory explicit. Methods: Interview data from 16 non-genetic medical specialists using genomics in practice identified three target behaviour areas across the testing process: (1) identifying patients, (2) test ordering and reporting, (3) communicating results. The Theoretical Domains Framework (TDF) was used to group barriers and facilitators to performing these actions. Barriers were grouped by distinct TDF domains, with 'overarching' TDF themes identified for overlapping barriers. Clinician intuitively-derived implementation strategies were matched with corresponding barriers, and retrospectively coded against behaviour change techniques (BCTs). Where no intuitive strategies were provided, theory-driven strategies were generated. An algorithm was developed and applied to articulate how implementation strategies address barriers to influence behaviour change. Results: Across all target behaviour areas, 32 identified barriers were coded across seven distinct TDF domains and eight overarching TDF themes. Within the 29 intuitive strategies, 21 BCTs were represented and used on 49 occasions to address 23 barriers. On 10 (20%) of these occasions, existing empirical links were found between BCTs and corresponding distinct TDF-coded barriers. Twenty additional theory-driven implementation strategies (using 19 BCTs on 31 occasions) were developed to address nine remaining barriers. Conclusion: Clinicians naturally generate their own solutions when implementing clinical interventions, and in this clinicogenomics example these intuitive strategies aligned with theoretical recommendations 20% of the time. We have matched intuitive strategies with theory-driven BCTs to make potential underlying theory explicit through proposed structured hypothesised causal pathways. Transparency and efficiency are enhanced, providing a novel method to identify determinants of implementation. Operationalising this approach to support the design of implementation strategies may optimise practice change in response to rapidly evolving scientific advances requiring swift translation into healthcare. [ABSTRACT FROM AUTHOR]

Background: COVID-19 inequities are abundant in low-income communities of color. Addressing COVID-19 vaccine hesitancy to promote equitable and sustained vaccination for underserved communities requires a multi-level, scalable, and sustainable approach. It is also essential that efforts acknowledge the broader healthcare needs of these communities including engagement in preventive services. Methods: This is a hybrid type 3 effectiveness-implementation study that will include a multi-level, longitudinal, mixed-methods data collection approach designed to assess the sustained impact of a co-created multicomponent strategy relying on bidirectional learning, shared decision-making, and expertise by all team members. The study capitalizes on a combination of implementation strategies including mHealth outreach with culturally appropriate messaging, care coordination to increase engagement in high priority preventive services, and the co-design of these strategies using community advisory boards led by Community Weavers. Community Weavers are individuals with lived experience as members of an underserved community serving as cultural brokers between communities, public health systems, and researchers to co-create community-driven, culturally sensitive public health solutions. The study will use an adaptive implementation approach operationalized in a sequential multiple assignment randomized trial design of 300 participants from three sites in a Federally Qualified Health Center in Southern California. This design will allow examining the impact of various implementation strategy components and deliver more intensive support to those who benefit from it most. The primary effectiveness outcomes are COVID-19 vaccine completion, engagement in preventive services, and vaccine confidence. The primary implementation outcomes are reach, adoption, implementation, and maintenance of the multicomponent strategy over a 12-month follow-up period. Mixed-effects logistic regression models will be used to examine program impacts and will be triangulated with qualitative data from participants and implementers. Discussion: This study capitalizes on community engagement, implementation science, health equity and communication, infectious disease, and public health perspectives to co-create a multicomponent strategy to promote the uptake of COVID-19 vaccination and preventive services for underserved communities in San Diego. The study design emphasizes broad engagement of our community and clinic partners leading to culturally sensitive and acceptable strategies to produce lasting and sustainable increases in vaccine equity and preventive services engagement. Trial registration: ClinicalTrials.gov Identifier: NCT05841810 May 3, 2023 [ABSTRACT FROM AUTHOR]