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      Understanding the multilevel determinants of clinicians’ imaging decision-making: setting the stage for de-implementation of low-value imaging

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          Abstract

          Background

          De-implementation requires understanding and targeting multilevel determinants of low-value care. The objective of this study was to identify multilevel determinants of imaging for prostate cancer (PCa) and asymptomatic microhematuria (AMH), two common urologic conditions that have contributed substantially to the annual spending on unnecessary imaging in the US.

          Methods

          We used a convergent mixed-methods approach involving survey and interview data. Using a survey, we asked 33 clinicians (55% response-rate) to indicate their imaging approach to 8 clinical vignettes designed to elicit responses that would demonstrate guideline-concordant/discordant imaging practices for patients with PCa or AMH. A subset of survey respondents (N = 7) participated in semi-structured interviews guided by a combination of two frameworks that offered a comprehensive understanding of multilevel determinants. We analyzed the interviews using a directed content analysis approach and identified subthemes to better understand the differences and similarities in the imaging determinants across two clinical conditions.

          Results

          Survey results showed that the majority of clinicians chose guideline-concordant imaging behaviors for PCa; guideline-concordant imaging intentions were more varied for AMH. Interview results informed what influenced imaging decisions and provided additional context to the varying intentions for AMH. Five subthemes touching on multiple levels were identified from the interviews: National Guidelines, Supporting Evidence and Information Exchange, Organization of the Imaging Pathways, Patients’ Clinical and Other Risk Factors, and Clinicians’ Beliefs and Experiences Regarding Imaging. Imaging decisions for both PCa and AMH were often driven by national guidelines from major professional societies. However, when clinicians felt guidelines were inadequate, they reported that their decision-making was influenced by their knowledge of recent scientific evidence, past clinical experiences, and the anticipated benefits of imaging (or not imaging) to both the patient and the clinician. In particular, clinicians referred to patients’ anxiety and uncertainty or patients’ clinical factors. For AMH patients, clinicians additionally expressed concerns regarding legal liability risk.

          Conclusion

          Our study identified comprehensive multilevel determinants of imaging to inform development of de-implementation interventions to reduce low-value imaging, which we found useful for identifying determinants of de-implementation. De-implementation interventions should be tailored to address the contextual determinants that are specific to each clinical condition.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12913-022-08600-3.

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          Most cited references25

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          Cancer statistics, 2022

          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence and outcomes. Incidence data (through 2018) were collected by the Surveillance, Epidemiology, and End Results program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2019) were collected by the National Center for Health Statistics. In 2022, 1,918,030 new cancer cases and 609,360 cancer deaths are projected to occur in the United States, including approximately 350 deaths per day from lung cancer, the leading cause of cancer death. Incidence during 2014 through 2018 continued a slow increase for female breast cancer (by 0.5% annually) and remained stable for prostate cancer, despite a 4% to 6% annual increase for advanced disease since 2011. Consequently, the proportion of prostate cancer diagnosed at a distant stage increased from 3.9% to 8.2% over the past decade. In contrast, lung cancer incidence continued to decline steeply for advanced disease while rates for localized-stage increased suddenly by 4.5% annually, contributing to gains both in the proportion of localized-stage diagnoses (from 17% in 2004 to 28% in 2018) and 3-year relative survival (from 21% to 31%). Mortality patterns reflect incidence trends, with declines accelerating for lung cancer, slowing for breast cancer, and stabilizing for prostate cancer. In summary, progress has stagnated for breast and prostate cancers but strengthened for lung cancer, coinciding with changes in medical practice related to cancer screening and/or treatment. More targeted cancer control interventions and investment in improved early detection and treatment would facilitate reductions in cancer mortality.
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            Clinically Localized Prostate Cancer: AUA/ASTRO/SUO Guideline. Part I: Risk Stratification, Shared Decision Making, and Care Options

            This guideline is structured to provide a clinical framework stratified by cancer severity to facilitate care decisions and guide the specifics of implementing the selected management options. The summary presented represents Part I of the two-part series dedicated to Clinically Localized Prostate Cancer: AUA/ASTRO/SUO Guideline discussing risk stratification and care options by cancer severity.
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              Implementation Mapping: Using Intervention Mapping to Develop Implementation Strategies

              Background: The ultimate impact of a health innovation depends not only on its effectiveness but also on its reach in the population and the extent to which it is implemented with high levels of completeness and fidelity. Implementation science has emerged as the potential solution to the failure to translate evidence from research into effective practice and policy evident in many fields. Implementation scientists have developed many frameworks, theories and models, which describe implementation determinants, processes, or outcomes; yet, there is little guidance about how these can inform the development or selection of implementation strategies (methods or techniques used to improve adoption, implementation, sustainment, and scale-up of interventions) (1, 2). To move the implementation science field forward and to provide a practical tool to apply the knowledge in this field, we describe a systematic process for planning or selecting implementation strategies: Implementation Mapping. Methods: Implementation Mapping is based on Intervention Mapping (a six-step protocol that guides the design of multi-level health promotion interventions and implementation strategies) and expands on Intervention Mapping step 5. It includes insights from both the implementation science field and Intervention Mapping. Implementation Mapping involves five tasks: (1) conduct an implementation needs assessment and identify program adopters and implementers; (2) state adoption and implementation outcomes and performance objectives, identify determinants, and create matrices of change objectives; (3) choose theoretical methods (mechanisms of change) and select or design implementation strategies; (4) produce implementation protocols and materials; and (5) evaluate implementation outcomes. The tasks are iterative with the planner circling back to previous steps throughout this process to ensure all adopters and implementers, outcomes, determinants, and objectives are addressed. Discussion: Implementation Mapping provides a systematic process for developing strategies to improve the adoption, implementation, and maintenance of evidence-based interventions in real-world settings.
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                Author and article information

                Contributors
                soohwang@live.unc.edu
                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central (London )
                1472-6963
                5 October 2022
                5 October 2022
                2022
                : 22
                : 1232
                Affiliations
                [1 ]GRID grid.10698.36, ISNI 0000000122483208, Department of Health Policy and Management, Gillings School of Global Public Health, , The University of North Carolina at Chapel Hill, ; 27599-7411 Chapel Hill, NC USA
                [2 ]GRID grid.241167.7, ISNI 0000 0001 2185 3318, Department of Implementation Science, School of Medicine, , Wake Forest University, ; Winston-Salem, USA
                [3 ]GRID grid.10698.36, ISNI 0000000122483208, UNC Lineberger Comprehensive Cancer Center, , The University of North Carolina at Chapel Hill, ; Chapel Hill, USA
                [4 ]GRID grid.10698.36, ISNI 0000000122483208, Department of Epidemiology, Gillings School of Global Public Health, , The University of North Carolina at Chapel Hill, ; Chapel Hill, USA
                [5 ]GRID grid.10698.36, ISNI 0000000122483208, Department of Urology, School of Medicine, , The University of North Carolina at Chapel Hill, ; Chapel Hill, USA
                [6 ]GRID grid.10698.36, ISNI 0000000122483208, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, ; Chapel Hill, USA
                Article
                8600
                10.1186/s12913-022-08600-3
                9535949
                36199082
                d2cc8ab8-0833-4f6e-a761-5ec157215cd0
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 13 June 2022
                : 15 September 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100011106, Graduate School, University of North Carolina at Chapel Hill;
                Award ID: Dissertation Completion Fellowship
                Funded by: FundRef http://dx.doi.org/10.13039/100008615, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill;
                Award ID: T32-CA-116339
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Health & Social care
                hematuria,prostate cancer,imaging,de-implementation,low-value care,behavior change,mixed methods,cfir

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