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Identifying and selecting implementation theories, models and frameworks: a qualitative study to inform the development of a decision support tool.

ABSTRACT: BACKGROUND:Implementation theories, models and frameworks offer guidance when implementing and sustaining healthcare evidence-based interventions. However, selection can be challenging given the myriad of potential options. We propose to inform a decision support tool to facilitate the appropriate selection of an implementation theory, model or framework in practice. To inform tool development, this study aimed to explore barriers and facilitators to identifying and selecting implementation theories, models and frameworks in research and practice, as well as end-user preferences for features and functions of the proposed tool. METHODS:We used an interpretive descriptive approach to conduct semi-structured interviews with implementation researchers and practitioners in Canada, the United States and Australia. Audio recordings were transcribed verbatim. Data were inductively coded by a single investigator with a subset of 20% coded independently by a second investigator and analyzed using thematic analysis. RESULTS:Twenty-four individuals participated in the study. Categories of barriers/facilitators, to inform tool development, included characteristics of the individual or team conducting implementation and characteristics of the implementation theory, model or framework. Major barriers to selection included inconsistent terminology, poor fit with the implementation context and limited knowledge about and training in existing theories, models and frameworks. Major facilitators to selection included the importance of clear and concise language and evidence that the theory, model or framework was applied in a relevant health setting or context. Participants were enthusiastic about the development of a decision support tool that is user-friendly, accessible and practical. Preferences for tool features included key questions about the implementation intervention or project (e.g., purpose, stage of implementation, intended target for change) and a comprehensive list of relevant theories, models and frameworks to choose from along with a glossary of terms and the contexts in which they were applied. CONCLUSIONS:An easy to use decision support tool that addresses key barriers to selecting an implementation theory, model or framework in practice may be beneficial to individuals who facilitate implementation practice activities. Findings on end-user preferences for tool features and functions will inform tool development and design through a user-centered approach.

SUBMITTER: Strifler L

PROVIDER: S-EPMC7227323 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Identifying and selecting implementation theories, models and frameworks: a qualitative study to inform the development of a decision support tool.

Strifler Lisa L Barnsley Jan M JM Hillmer Michael M Straus Sharon E SE

BMC medical informatics and decision making 20200514 1

<h4>Background</h4>Implementation theories, models and frameworks offer guidance when implementing and sustaining healthcare evidence-based interventions. However, selection can be challenging given the myriad of potential options. We propose to inform a decision support tool to facilitate the appropriate selection of an implementation theory, model or framework in practice. To inform tool development, this study aimed to explore barriers and facilitators to identifying and selecting implementat ...[more]

PMID: 32408909

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Project description:BackgroundComputerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovation and its use within these care settings. These processes include context assessments to identify key determinants, implementation plans for adoption, promoting ongoing uptake, adherence, and long-term evaluation. However, there has been no prior review synthesizing the literature regarding the theories, models, and frameworks that have informed the implementation and adoption of CDSSs within hospitals.ObjectiveThis scoping review aims to identify the theory, model, and framework approaches that have been used to facilitate the implementation and adoption of CDSSs in tertiary health care settings, including hospitals. The rationales reported for selecting these approaches, including the limitations and strengths, are described.MethodsA total of 5 electronic databases were searched (CINAHL via EBSCOhost, PubMed, Scopus, PsycINFO, and Embase) to identify studies that implemented or adopted a CDSS in a tertiary health care setting using an implementation theory, model, or framework. No date or language limits were applied. A narrative synthesis was conducted using full-text publications and abstracts. Implementation phases were classified according to the "Active Implementation Framework stages": exploration (feasibility and organizational readiness), installation (organizational preparation), initial implementation (initiating implementation, ie, training), full implementation (sustainment), and nontranslational effectiveness studies.ResultsA total of 81 records (42 full text and 39 abstracts) were included. Full-text studies and abstracts are reported separately. For full-text studies, models (18/42, 43%), followed by determinants frameworks (14/42,33%), were most frequently used to guide adoption and evaluation strategies. Most studies (36/42, 86%) did not list the limitations associated with applying a specific theory, model, or framework.ConclusionsModels and related quality improvement methods were most frequently used to inform CDSS adoption. Models were not typically combined with each other or with theory to inform full-cycle implementation strategies. The findings highlight a gap in the application of implementation methods including theories, models, and frameworks to facilitate full-cycle implementation strategies for hospital CDSSs.

Project description:BackgroundModels, theories, and frameworks (MTFs) provide the foundation for a cumulative science of implementation, reflecting a shared, evolving understanding of various facets of implementation. One under-represented aspect in implementation MTFs is how intersecting social factors and systems of power and oppression can shape implementation. There is value in enhancing how MTFs in implementation research and practice account for these intersecting factors. Given the large number of MTFs, we sought to identify exemplar MTFs that represent key implementation phases within which to embed an intersectional perspective.MethodsWe used a five-step process to prioritize MTFs for enhancement with an intersectional lens. We mapped 160 MTFs to three previously prioritized phases of the Knowledge-to-Action (KTA) framework. Next, 17 implementation researchers/practitioners, MTF experts, and intersectionality experts agreed on criteria for prioritizing MTFs within each KTA phase. The experts used a modified Delphi process to agree on an exemplar MTF for each of the three prioritized KTA framework phases. Finally, we reached consensus on the final MTFs and contacted the original MTF developers to confirm MTF versions and explore additional insights.ResultsWe agreed on three criteria when prioritizing MTFs: acceptability (mean = 3.20, SD = 0.75), applicability (mean = 3.82, SD = 0.72), and usability (median = 4.00, mean = 3.89, SD = 0.31) of the MTF. The top-rated MTFs were the Iowa Model of Evidence-Based Practice to Promote Quality Care for the 'Identify the problem' phase (mean = 4.57, SD = 2.31), the Consolidated Framework for Implementation Research for the 'Assess barriers/facilitators to knowledge use' phase (mean = 5.79, SD = 1.12), and the Behaviour Change Wheel for the 'Select, tailor, implement interventions' phase (mean = 6.36, SD = 1.08).ConclusionsOur interdisciplinary team engaged in a rigorous process to reach consensus on MTFs reflecting specific phases of the implementation process and prioritized each to serve as an exemplar in which to embed intersectional approaches. The resulting MTFs correspond with specific phases of the KTA framework, which itself may be useful for those seeking particular MTFs for particular KTA phases. This approach also provides a template for how other implementation MTFs could be similarly considered in the future.Trial registrationOpen Science Framework Registration: osf.io/qgh64.

Project description:BackgroundQuantitative systematic reviews have identified clinical artificial intelligence (AI)-enabled tools with adequate performance for real-world implementation. To our knowledge, no published report or protocol synthesizes the full breadth of stakeholder perspectives. The absence of such a rigorous foundation perpetuates the "AI chasm," which continues to delay patient benefit.ObjectiveThe aim of this research is to synthesize stakeholder perspectives of computerized clinical decision support tools in any health care setting. Synthesized findings will inform future research and the implementation of AI into health care services.MethodsThe search strategy will use MEDLINE (Ovid), Scopus, CINAHL (EBSCO), ACM Digital Library, and Science Citation Index (Web of Science). Following deduplication, title, abstract, and full text screening will be performed by 2 independent reviewers with a third topic expert arbitrating. The quality of included studies will be appraised to support interpretation. Best-fit framework synthesis will be performed, with line-by-line coding completed by 2 independent reviewers. Where appropriate, these findings will be assigned to 1 of 22 a priori themes defined by the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability framework. New domains will be inductively generated for outlying findings. The placement of findings within themes will be reviewed iteratively by a study advisory group including patient and lay representatives.ResultsStudy registration was obtained from PROSPERO (CRD42021256005) in May 2021. Final searches were executed in April, and screening is ongoing at the time of writing. Full text data analysis is due to be completed in October 2021. We anticipate that the study will be submitted for open-access publication in late 2021.ConclusionsThis paper describes the protocol for a qualitative evidence synthesis aiming to define barriers and facilitators to the implementation of computerized clinical decision support tools from all relevant stakeholders. The results of this study are intended to expedite the delivery of patient benefit from AI-enabled clinical tools.Trial registrationPROSPERO CRD42021256005; https://tinyurl.com/r4x3thvp.International registered report identifier (irrid)DERR1-10.2196/33145.

Project description:BackgroundWHO has recognised the need to improve its guideline methodology to ensure that guideline decision-making processes are transparent and evidence based, and that the resulting recommendations are relevant and applicable. To help achieve this, WHO guidelines now typically enhance intervention effectiveness data with evidence on a wider range of decision-making criteria, including how stakeholders value different outcomes, equity, gender and human rights impacts, and the acceptability and feasibility of interventions. Qualitative evidence syntheses (QES) are increasingly used to provide evidence on this wider range of issues. In this paper, we describe and discuss how to use the findings from QES to populate decision-making criteria in evidence-to-decision (EtD) frameworks. This is the second in a series of three papers that examines the use of QES in developing clinical and health system guidelines.MethodsWHO convened a writing group drawn from the technical teams involved in its recent (2010-2018) guidelines employing QES. Using a pragmatic and iterative approach that included feedback from WHO staff and other stakeholders, the group reflected on, discussed and identified key methods and research implications from designing QES and using the resulting findings in guideline development.ResultsWe describe a step-wise approach to populating EtD frameworks with QES findings. This involves allocating findings to the different EtD criteria (how stakeholders value different outcomes, equity, acceptability and feasibility, etc.), weaving the findings into a short narrative relevant to each criterion, and inserting this summary narrative into the corresponding 'research evidence' sections of the EtD. We also identify areas for further methodological research, including how best to summarise and present qualitative data to groups developing guidelines, how these groups draw on different types of evidence in their decisions, and the extent to which our experiences are relevant to decision-making processes in fields other than health.ConclusionsThis paper shows the value of incorporating QES within a guideline development process, and the roles that qualitative evidence can play in integrating the views and experiences of relevant stakeholders, including groups who may not be otherwise represented in the decision-making process.

Project description:ImportanceHospitals ceased most elective procedures during the height of coronavirus disease 2019 (COVID-19) infections. As hospitals begin to recommence elective procedures, it is necessary to have a means to assess how resource intensive a given case may be.ObjectiveTo evaluate the development and performance of a clinical decision support tool to inform resource utilization for elective procedures.Design, setting, and participantsIn this prognostic study, predictive modeling was used on retrospective electronic health records data from a large academic health system comprising 1 tertiary care hospital and 2 community hospitals of patients undergoing scheduled elective procedures from January 1, 2017, to March 1, 2020. Electronic health records data on case type, patient demographic characteristics, service utilization history, comorbidities, and medications were and abstracted and analyzed. Data were analyzed from April to June 2020.Main outcomes and measuresPredicitons of hospital length of stay, intensive care unit length of stay, need for mechanical ventilation, and need to be discharged to a skilled nursing facility. These predictions were generated using the random forests algorithm. Predicted probabilities were turned into risk classifications designed to give assessments of resource utilization risk.ResultsData from the electronic health records of 42 199 patients from 3 hospitals were abstracted for analysis. The median length of stay was 2.3 days (range, 1.3-4.2 days), 6416 patients (15.2%) were admitted to the intensive care unit, 1624 (3.8%) received mechanical ventilation, and 2843 (6.7%) were discharged to a skilled nursing facility. Predictive performance was strong with an area under the receiver operator characteristic ranging from 0.76 to 0.93. Sensitivity of the high-risk and medium-risk groupings was set at 95%. The negative predictive value of the low-risk grouping was 99%. We integrated the models into a daily refreshing Tableau dashboard to guide decision-making.Conclusions and relevanceThe clinical decision support tool is currently being used by surgical leadership to inform case scheduling. This work shows the importance of a learning health care environment in surgical care, using quantitative modeling to guide decision-making.

Project description:BackgroundIn this paper we describe the use of the Consolidated Framework for Implementation Research (CFIR) to study implementation of a web-based, point-of-care, EHR-linked clinical decision support (CDS) tool designed to identify and provide care recommendations for adults with prediabetes (Pre-D CDS).MethodsAs part of a large NIH-funded clinic-randomized trial, we identified a convenience sample of interview participants from 22 primary care clinics in Minnesota, North Dakota, and Wisconsin that were randomly allocated to receive or not receive a web-based EHR-integrated prediabetes CDS intervention. Participants included 11 clinicians, 6 rooming staff, and 7 nurse or clinic managers recruited by study staff to participate in telephone interviews conducted by an expert in qualitative methods. Interviews were recorded and transcribed, and data analysis was conducted using a constructivist version of grounded theory.ResultsImplementing a prediabetes CDS tool into primary care clinics was useful and well received. The intervention was integrated with clinic workflows, supported primary care clinicians in clearly communicating prediabetes risk and management options with patients, and in identifying actionable care opportunities. The main barriers to CDS use were time and competing priorities. Finally, while the implementation process worked well, opportunities remain in engaging the care team more broadly in CDS use.ConclusionsThe use of CDS tools for engaging patients and providers in care improvement opportunities for prediabetes is a promising and potentially effective strategy in primary care settings. A workflow that incorporates the whole care team in the use of such tools may optimize the implementation of CDS tools like these in primary care settings. Trial registration Name of the registry: Clinicaltrial.gov.Trial registration numberNCT02759055. Date of registration: 05/03/2016. URL of trial registry record: https://clinicaltrials.gov/ct2/show/NCT02759055 Prospectively registered.

Dataset Information

Identifying and selecting implementation theories, models and frameworks: a qualitative study to inform the development of a decision support tool.

Publications

Identifying and selecting implementation theories, models and frameworks: a qualitative study to inform the development of a decision support tool.

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