Dataset Information

Determinants for the use and de-implementation of low-value care in health care: a scoping review.

ABSTRACT:

Background

A considerable proportion of interventions provided to patients lack evidence of their effectiveness. This implies that patients may receive ineffective, unnecessary or even harmful care. However, despite some empirical studies in the field, there has been no synthesis of determinants impacting the use of low-value care (LVC) and the process of de-implementing LVC.

Aim

The aim was to identify determinants influencing the use of LVC, as well as determinants for de-implementation of LVC practices in health care.

Methods

A scoping review was performed based on the framework by Arksey and O'Malley. We searched four scientific databases, conducted snowball searches of relevant articles and hand searched the journal Implementation Science for peer-reviewed journal articles in English. Articles were included if they were empirical studies reporting on determinants for the use of LVC or de-implementation of LVC. The abstract review and the full-text review were conducted in duplicate and conflicting decisions were discussed until consensus was reached. Data were charted using a piloted data charting form and the determinants were inductively coded and categorised in an iterative process conducted by the project group.

Results

In total, 101 citations were included in the review. Of these, 92 reported on determinants for the use of LVC and nine on determinants for de-implementation. The studies were conducted in a range of health care settings and investigated a variety of LVC practices with LVC medication prescriptions, imaging and screening procedures being the most common. The identified determinants for the use of LVC as well as for de-implementation of LVC practices broadly concerned: patients, professionals, outer context, inner context, process and evidence and LVC practice. The results were discussed in relation to the Consolidated Framework for Implementation Research.

Conclusion

The identified determinants largely overlap with existing implementation frameworks, although patient expectations and professionals' fear of malpractice appear to be more prominent determinants for the use and de-implementation of LVC. Thus, existing implementation determinant frameworks may require adaptation to be transferable to de-implementation. Strategies to reduce the use of LVC should specifically consider determinants for the use and de-implementation of LVC.

Registration

The review has not been registered.

SUBMITTER: Augustsson H

PROVIDER: S-EPMC7860215 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Determinants for the use and de-implementation of low-value care in health care: a scoping review.

Augustsson Hanna H Ingvarsson Sara S Nilsen Per P von Thiele Schwarz Ulrica U Muli Irene I Dervish Jessica J Hasson Henna H

Implementation science communications 20210204 1

<h4>Background</h4>A considerable proportion of interventions provided to patients lack evidence of their effectiveness. This implies that patients may receive ineffective, unnecessary or even harmful care. However, despite some empirical studies in the field, there has been no synthesis of determinants impacting the use of low-value care (LVC) and the process of de-implementing LVC.<h4>Aim</h4>The aim was to identify determinants influencing the use of LVC, as well as determinants for de-implem ...[more]

PMID: 33541443

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Project description:BackgroundThe use of low-value care (LVC) is a persistent problem that calls for knowledge about strategies for de-implementation. However, studies are dispersed across many clinical fields, and there is no overview of strategies that can be used to support the de-implementation of LVC. The extent to which strategies used for implementation are also used in de-implementing LVC is unknown. The aim of this scoping review is to (1) identify strategies for the de-implementation of LVC described in the scientific literature and (2) compare de-implementation strategies to implementation strategies as specified in the Expert Recommendation for Implementing Change (ERIC) and strategies added by Perry et al. METHOD: A scoping review was conducted according to recommendations outlined by Arksey and O'Malley. Four scientific databases were searched, relevant articles were snowball searched, and the journal Implementation Science was searched manually for peer-reviewed journal articles in English. Articles were included if they were empirical studies of strategies designed to reduce the use of LVC. Two reviewers conducted all abstract and full-text reviews, and conflicting decisions were discussed until consensus was reached. Data were charted using a piloted data-charting form. The strategies were first coded inductively and then mapped onto the ERIC compilation of implementation strategies.ResultsThe scoping review identified a total of 71 unique de-implementation strategies described in the literature. Of these, 62 strategies could be mapped onto ERIC strategies, and four strategies onto one added category. Half (50%) of the 73 ERIC implementation strategies were used for de-implementation purposes. Five identified de-implementation strategies could not be mapped onto any of the existing strategies in ERIC.ConclusionsSimilar strategies are used for de-implementation and implementation. However, only a half of the implementation strategies included in the ERIC compilation were represented in the de-implementation studies, which may imply that some strategies are being underused or that they are not applicable for de-implementation purposes. The strategies assess and redesign workflow (a strategy previously suggested to be added to ERIC), accountability tool, and communication tool (unique new strategies for de-implementation) could complement the existing ERIC compilation when used for de-implementation purposes.

Project description:Background/aimsConsiderable efforts have been made to improve guideline adherence in healthcare through de-implementation, such as decreasing the prescription of inappropriate medicines. However, we have limited knowledge about the effectiveness, barriers, facilitators and consequences of de-implementation strategies targeting inappropriate medication prescribing in secondary care settings. This review was conducted to understand these factors to contribute to better replication and optimisation of future de-implementation efforts to reduce low-value care.MethodsA systematic review of randomised control trials was conducted. Papers were identified through CINAHL, EMBASE, MEDLINE and Cochrane register of controlled trials to February 2021. Eligible studies were randomised control trials evaluating behavioural strategies to de-implement inappropriate prescribing in secondary healthcare. Risk of bias was assessed using the Cochrane Risk of Bias tool. Intervention characteristics, effectiveness, barriers, facilitators and consequences were identified in the study text and tabulated.ResultsEleven studies were included, of which seven were reported as effectively de-implementing low-value prescribing. Included studies were judged to be mainly at low to moderate risk for selection biases and generally high risk for performance and reporting biases. The majority of these strategies were clinical decision support at the 'point of care'. Clinical decision support tools were the most common and effective. They were found to be a low-cost and simple strategy. However, barriers such as clinician's reluctance to accept recommendations, or the clinical setting were potential barriers to their success. Educational strategies were the second most reported intervention type however the utility of educational strategies for de-implementation remains varied. Multiple barriers and facilitators relating to the environmental context, resources and knowledge were identified across studies as potentially influencing de-implementation. Various consequences were identified; however, few measured the impact of de-implementation on usual appropriate practice.ConclusionThis review offers insight into the intervention strategies, potential barriers, facilitators and consequences that may affect the de-implementation of low-value prescribing in secondary care. Identification of these key features helps understand how and why these strategies are effective and the wider (desirable or undesirable) impact of de-implementation. These findings can contribute to the successful replication or optimisation of strategies used to de-implement low-value prescribing practices in future.Trial registrationThe review protocol was registered at PROSPERO (ID: CRD42021243944).

Project description:Background/Aims De-implementation, including the removal or reduction of unnecessary or inappropriate prescribing, is crucial to ensure patients receive appropriate evidence-based health care. The utilization of de-implementation efforts is contingent on the quality of strategy reporting. To further understand effective ways to de-implement medical practices, specification of behavioural targets and components of de-implementation strategies are required. This paper aims to critically analyse how well the behavioural targets and strategy components, in studies that focused on de-implementing unnecessary or inappropriate prescribing in secondary healthcare settings, were reported.MethodsA supplementary analysis of studies included in a recently published review of de-implementation studies was conducted. Article text was coded verbatim to two established specification frameworks. Behavioural components were coded deductively to the five elements of the Action, Actor, Context, Target, Time (AACTT) framework. Strategy components were mapped to the nine elements of the Proctor's 'measuring implementation strategies' framework.ResultsThe behavioural components of low-value prescribing, as coded to the AACTT framework, were generally specified well. However, the Actor and Time components were often vague or not well reported. Specification of strategy components, as coded to the Proctor framework, were less well reported. Proctor's Actor, Action target: specifying targets, Dose and Justification elements were not well reported or varied in the amount of detail offered. We also offer suggestions of additional specifications to make, such as the 'interactions' participants have with a strategy.ConclusionSpecification of behavioural targets and components of de-implementation strategies for prescribing practices can be accommodated by the AACTT and Proctor frameworks when used in conjunction. These essential details are required to understand, replicate and successfully de-implement unnecessary or inappropriate prescribing. In general, standardisation in the reporting quality of these components is required to replicate any de-implementation efforts.Trial registrationNot registered.

Project description:BackgroundMany decisions regarding health resource utilization flow through the patient-clinician interaction. Thus, it represents a place where de-implementation interventions may have considerable effect on reducing the use of clinical interventions that lack efficacy, have risks that outweigh benefits, or are not cost-effective (i.e., low-value care). The objective of this systematic review with meta-analysis was to determine the effect of de-implementation interventions that engage patients within the patient-clinician interaction on use of low-value care.MethodsMEDLINE, EMBASE, and CINAHL were searched from inception to November 2019. Gray literature was searched using the CADTH tool. Studies were screened independently by two reviewers and were included if they (1) described an intervention that engaged patients in an initiative to reduce low-value care, (2) reported the use of low-value care with and without the intervention, and (3) were randomized clinical trials (RCTs) or quasi-experimental designs. Studies describing interventions solely focused on clinicians or published in a language other than English were excluded. Data was extracted independently in duplicate and pertained to the low-value clinical intervention of interest, components of the strategy for patient engagement, and study outcomes. Quality of included studies was assessed using the Cochrane Risk of Bias tool for RCTs and a modified Downs and Black checklist for quasi-experimental studies. Random effects meta-analysis (reported as risk ratio, RR) was used to examine the effect of de-implementation interventions on the use of low-value care.ResultsFrom 6736 unique citations, 9 RCTs and 13 quasi-experimental studies were included in the systematic review. Studies mostly originated from the USA (n = 13, 59%), targeted treatments (n = 17, 77%), and took place in primary care (n = 10, 45%). The most common intervention was patient-oriented educational material (n = 18, 82%), followed by tools for shared decision-making (n = 5, 23%). Random effects meta-analysis demonstrated that de-implementation interventions that engage patients within the patient-clinician interaction led to a significant reduction in low-value care in both RCTs (RR 0.74; 95% CI 0.66-0.84) and quasi-experimental studies (RR 0.61; 95% CI 0.43-0.87). There was significant inter-study heterogeneity; however, intervention effects were consistent across subgroups defined by low-value practice and patient-engagement strategy.ConclusionsDe-implementation interventions that engage patients within the patient-clinician interaction through patient-targeted educational materials or shared decision-making tools are effective in decreasing the use of low-value care. Clinicians and policymakers should consider engaging patients within initiatives that seek to reduce low-value care.RegistrationOpen Science Framework (https://osf.io/6fsxm).

Project description:BackgroundLow-value care initiatives are rapidly growing; however, it is not clear how members of the public should be involved. The objective of this scoping review was to systematically examine the literature describing public involvement in initatives to reduce low-value care.MethodsEvidence sources included MEDLINE, EMBASE, and CINAHL databases from inception to November 26, 2019, grey literature (CADTH Tool), reference lists of included articles, and expert consultation. Citations were screened in duplicate and included if they referred to the public's perception and/or involvement in reducing low-value care. Public included patients or citizens without any advanced healthcare knowledge. Low-value care included medical tests or treatments that lack efficacy, have risks that exceed benefit, or are not cost-effective. Extracted data pertained to study characteristics, low-value practice, clinical setting, and level of public involvement (i.e., patient-clinician interaction, research, or policy-making).ResultsThe 218 included citations were predominantly original research (n = 138, 63%), published since 2010 (n = 192, 88%), originating from North America (n = 146, 67%). Most citations focused on patient engagement within the patient-clinician interaction (n = 156, 72%), using tools that included shared decision-making (n = 66, 42%) and patient-targeted educational materials (n = 72, 46%), and reported both reductions in low-value care and improved patient perceptions regarding low-value care. Fewer citations examined public involvement in low-value care policy-making (n = 33, 15%). Among citations that examined perspectives regarding public involvement in initiatives to reduce low-value care (n = 10, 5%), there was consistent support for the utility of tools applied within the patient-clinician interaction and less consistent support for involvement in policy-making.ConclusionsEfforts examining public involvement in low-value care concentrate within the patient-clinician interaction, wherein patient-oriented educational materials and shared decision-making tools have been commonly studied and are associated with reductions in low-value care. This contrasts with inclusion of the public in low-value care policy decisions wherein tools to promote engagement are less well-developed and involvement not consistently viewed as valuable.Trial registrationOpen Science Framework (https://osf.io/6fsxm).

Project description:BackgroundSignificant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, health care professionals still struggle to implement AI in their daily practice.ObjectiveThis paper aims to identify the implementation frameworks used to understand the application of AI in health care practice.MethodsA scoping review was conducted using the Cochrane, Evidence Based Medicine Reviews, Embase, MEDLINE, and PsycINFO databases to identify publications that reported frameworks, models, and theories concerning AI implementation in health care. This review focused on studies published in English and investigating AI implementation in health care since 2000. A total of 2541 unique publications were retrieved from the databases and screened on titles and abstracts by 2 independent reviewers. Selected articles were thematically analyzed against the Nilsen taxonomy of implementation frameworks, and the Greenhalgh framework for the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health care technologies.ResultsIn total, 7 articles met all eligibility criteria for inclusion in the review, and 2 articles included formal frameworks that directly addressed AI implementation, whereas the other articles provided limited descriptions of elements influencing implementation. Collectively, the 7 articles identified elements that aligned with all the NASSS domains, but no single article comprehensively considered the factors known to influence technology implementation. New domains were identified, including dependency on data input and existing processes, shared decision-making, the role of human oversight, and ethics of population impact and inequality, suggesting that existing frameworks do not fully consider the unique needs of AI implementation.ConclusionsThis literature review demonstrates that understanding how to implement AI in health care practice is still in its early stages of development. Our findings suggest that further research is needed to provide the knowledge necessary to develop implementation frameworks to guide the future implementation of AI in clinical practice and highlight the opportunity to draw on existing knowledge from the field of implementation science.

Project description:BackgroundAs health care spending reaches unsustainable levels, improving value has become an increasingly important policy priority. Relatively little research has explored factors driving value. As a first step towards filling this gap, we performed a scoping review of the literature to identify potential drivers of health care value.MethodsSearches of PubMed, Embase, Google Scholar, Policy File, and SCOPUS were conducted between February and March 2020. Empirical studies that explored associations between any range of factors and value (loosely defined as quality or outcomes relative to cost) were eligible for inclusion. We created a template in Microsoft Excel for data extraction and evaluated the quality of included articles using the Critical Appraisal Skills Programme (CASP) quality appraisal tool. Data was synthesized using narrative methods.ResultsTwenty-two studies were included in analyses, of which 20 focused on low value service utilization. Independent variables represented a range of system-, hospital-, provider-, and patient-level characteristics. Although results were mixed, several consistent findings emerged. First, insurance incentive structures may affect value. For example, patients in Accountable Care Organizations had reduced rates of low value care utilization compared to patients in traditionally structured insurance plans. Second, higher intensity of care was associated with higher rates of low value care. Third, culture is likely to contribute to value. This was suggested by findings that recent medical school graduation and allopathic training were associated with reduced low value service utilization and that provider organizations had larger effects on value than did individual physicians.ConclusionsSystem, hospital, provider, and community characteristics influence low value care provision. To improve health care value, strategies aiming to reduce utilization of low value services and promote high value care across various levels will be essential.

Dataset Information

Determinants for the use and de-implementation of low-value care in health care: a scoping review.

Background

Aim

Methods

Results

Conclusion

Registration

Publications

Determinants for the use and de-implementation of low-value care in health care: a scoping review.

Similar Datasets

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