Abstract:Background
Reduction or elimination of inappropriate, ineffective, or potentially harmful healthcare services and public health programs can help to ensure limited resources are used effectively. Frameworks and models (FM) are valuable tools in conceptualizing and guiding the study of de-implementation. This scoping review sought to identify and characterize FM that can be used to study de-implementation as a phenomenon and identify gaps in the literature to inform future model development and… Show more
“…For example, Helfrich et al highlighted that de-implementation may require a process of unlearning to change knowledge, intentions, and beliefs about a low-value service [ 46 ]. Recent reviews identified some theories, models, or frameworks specifically developed for the de-implementation of low-value care [ 59 , 60 ]. Using theoretical frameworks specifically developed for de-implementation of low-value care therefore may help researchers to better evaluate the de-implementation process, identify determinants of de-implementing low-valued care, and explore interactions among de-implementation determinants (e.g., peer pressure may moderate the influence of guidelines) [ 35 ].…”
Background
Accumulating evidence suggests that interventions to de-implement low-value services are urgently needed. While medical societies and educational campaigns such as Choosing Wisely have developed several guidelines and recommendations pertaining to low-value care, little is known about interventions that exist to de-implement low-value care in oncology settings. We conducted this review to summarize the literature on interventions to de-implement low-value care in oncology settings.
Methods
We systematically reviewed the published literature in PubMed, Embase, CINAHL Plus, and Scopus from 1 January 1990 to 4 March 2021. We screened the retrieved abstracts for eligibility against inclusion criteria and conducted a full-text review of all eligible studies on de-implementation interventions in cancer care delivery. We used the framework analysis approach to summarize included studies’ key characteristics including design, type of cancer, outcome(s), objective(s), de-implementation interventions description, and determinants of the de-implementation interventions. To extract the data, pairs of authors placed text from included articles into the appropriate cells within our framework. We analyzed extracted data from each cell to describe the studies and findings of de-implementation interventions aiming to reduce low-value cancer care.
Results
Out of 2794 studies, 12 met our inclusion criteria. The studies covered several cancer types, including prostate cancer (n = 5), gastrointestinal cancer (n = 3), lung cancer (n = 2), breast cancer (n = 2), and hematologic cancers (n = 1). Most of the interventions (n = 10) were multifaceted. Auditing and providing feedback, having a clinical champion, educating clinicians through developing and disseminating new guidelines, and developing a decision support tool are the common components of the de-implementation interventions. Six of the de-implementation interventions were effective in reducing low-value care, five studies reported mixed results, and one study showed no difference across intervention arms. Eleven studies aimed to de-implement low-value care by changing providers’ behavior, and 1 de-implementation intervention focused on changing the patients’ behavior. Three studies had little risk of bias, five had moderate, and four had a high risk of bias.
Conclusions
This review demonstrated a paucity of evidence in many areas of the de-implementation of low-value care including lack of studies in active de-implementation (i.e., healthcare organizations initiating de-implementation interventions purposefully aimed at reducing low-value care).
“…For example, Helfrich et al highlighted that de-implementation may require a process of unlearning to change knowledge, intentions, and beliefs about a low-value service [ 46 ]. Recent reviews identified some theories, models, or frameworks specifically developed for the de-implementation of low-value care [ 59 , 60 ]. Using theoretical frameworks specifically developed for de-implementation of low-value care therefore may help researchers to better evaluate the de-implementation process, identify determinants of de-implementing low-valued care, and explore interactions among de-implementation determinants (e.g., peer pressure may moderate the influence of guidelines) [ 35 ].…”
Background
Accumulating evidence suggests that interventions to de-implement low-value services are urgently needed. While medical societies and educational campaigns such as Choosing Wisely have developed several guidelines and recommendations pertaining to low-value care, little is known about interventions that exist to de-implement low-value care in oncology settings. We conducted this review to summarize the literature on interventions to de-implement low-value care in oncology settings.
Methods
We systematically reviewed the published literature in PubMed, Embase, CINAHL Plus, and Scopus from 1 January 1990 to 4 March 2021. We screened the retrieved abstracts for eligibility against inclusion criteria and conducted a full-text review of all eligible studies on de-implementation interventions in cancer care delivery. We used the framework analysis approach to summarize included studies’ key characteristics including design, type of cancer, outcome(s), objective(s), de-implementation interventions description, and determinants of the de-implementation interventions. To extract the data, pairs of authors placed text from included articles into the appropriate cells within our framework. We analyzed extracted data from each cell to describe the studies and findings of de-implementation interventions aiming to reduce low-value cancer care.
Results
Out of 2794 studies, 12 met our inclusion criteria. The studies covered several cancer types, including prostate cancer (n = 5), gastrointestinal cancer (n = 3), lung cancer (n = 2), breast cancer (n = 2), and hematologic cancers (n = 1). Most of the interventions (n = 10) were multifaceted. Auditing and providing feedback, having a clinical champion, educating clinicians through developing and disseminating new guidelines, and developing a decision support tool are the common components of the de-implementation interventions. Six of the de-implementation interventions were effective in reducing low-value care, five studies reported mixed results, and one study showed no difference across intervention arms. Eleven studies aimed to de-implement low-value care by changing providers’ behavior, and 1 de-implementation intervention focused on changing the patients’ behavior. Three studies had little risk of bias, five had moderate, and four had a high risk of bias.
Conclusions
This review demonstrated a paucity of evidence in many areas of the de-implementation of low-value care including lack of studies in active de-implementation (i.e., healthcare organizations initiating de-implementation interventions purposefully aimed at reducing low-value care).
“…Although disinvestment is frequently associated with budgetary concerns and affordability, it can also be prompted to enhance efficiency and quality of care through reformation of service provision (26). Based on our analysis (see Supplementary Tables 5 and 6), the purpose of disinvestment initiatives can be grouped into four themes (Figure 3): (i) enhance value-based spending (13;14;16–19;25); (ii) resource reallocation (3;12;14–21;24;25); (iii) improving quality of health care (3;7;11–14;16;19;21;22;25); and (iv) informed policy making (12;17). Clarifying the goals of disinvestment would help people understand that it is a tool for improving access to effective solutions, not for eliminating technologies and withdrawing resources on a large scale.Figure 3.Rationale and purpose of disinvestment.…”
Objectives
Disinvestment from low value health technologies is growing globally. Diverse evidence gathering and assessment methods were used to implement disinvestment initiatives, however, less than half of the empirical studies report reduced use of the low-value services. This scoping review aimed to synthesize the information from available reviews on the concepts and purposes of disinvestment in healthcare, the approaches and methods used, the role of stakeholders and facilitators and barriers in its implementation.
Methods
This scoping review was guided by the Joanna Briggs Institute Manual for Evidence Synthesis and PRISMA statement for scoping review. Published reviews on disinvestment were identified from scientific databases including health technology assessment (HTA) Web sites using the terms “disinvestment,” “health technology reassessment,” and “healthcare.” The data obtained was synthesized narratively to identify similarities and differences across the approaches based on the prespecified categories.
Results
Seventeen reviews were included with thirty-four initiatives identified across sixteen countries at various levels of implementation and responsible agencies for the activities. Two most used methods to facilitate disinvestment decisions are Programme Budgeting and Marginal Analysis (PBMA) and HTA. Stakeholder involvement is the most important aspect to be addressed, as it acts as both facilitator and barrier in disinvestment initiatives implementation.
Conclusions
Disinvestment programs have been implemented at multilevel, involving multistakeholders and using multiple methods such as PBMA and HTA. However, there is a lack of clarity on the additional dimensions of technical analysis related to these tools. Further research could focus on technology optimization in healthcare as part of overall health technology management.
“…This data must be used in combination with state, national, and global data on case counts; the best available scientific evidence for understanding disease spread mechanisms and the effectiveness, and failure, of disease mitigation procedures to support dynamic decision‐making about implementation and de‐implementation strategies as local conditions change. 22 One example of community use of local data to identify vulnerable populations to target community outreach is the non‐profit Parkland Center for Clinical Innovation (PCCI) in Dallas, Texas. PCCI has led the creation of the Dallas Connected Community of Care (CCC) that brings together health systems, social service agencies and over a hundred community organizations to improve the well‐being of Dallas residents.…”
Introduction
The persisting and evolving COVID‐19 pandemic has made apparent that no singular policy of mitigation at a regional, national or global level has achieved satisfactory and universally acceptable results. In the United States, carefully planned and executed pandemic policies have been neither effective nor popular and COVID‐19 risk management decisions have been relegated to individual citizens and communities. In this paper, we argue that a more effective approach is to equip and strengthen community coalitions to become
local learning health communities
(LLHCs) that use data over time to make adaptive decisions that can optimize the equity and well‐being in their communities.
Methods
We used data from the North Carolina (NC) county and zip code levels from May to August 2020 to demonstrate how a LLHC could use statistical process control (SPC) charts and simple statistical analysis to make local decisions about how to respond to COVID‐19.
Results
We found many patterns of COVID‐19 progression at the local (county and zip code) levels during the same time period within the state that were completely different from the aggregate NC state level data used for policy making.
Conclusions
Systematic approaches to learning from local data to support effective decisions have promise well beyond the current pandemic. These tools can help address other complex public health issues, and advance outcomes and equity. Building this capacity requires investment in data infrastructure and the strengthening of data competencies in community coalitions to better interpret data with limited need for advanced statistical expertise. Additional incentives that build trust, support data transparency, encourage truth‐telling and promote meaningful teamwork are also critical. These must be carefully designed, contextually appropriate and multifaceted to motivate citizens to create and sustain an effective learning system that works for their communities.
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