Background

Clinical practice evolves in response to scientific evidence through a process of discovery (novel practice introduced into clinical practice, e.g., systemic thrombolysis for acute ST-elevation myocardial infarction (STEMI) [1]), replacement (newer, more effective practice supplants current practice, e.g. tenecteplase superior to alteplase among patients with STEMI [2]), or reversal (current practice shown to be ineffective or harmful, e.g., suppression of ventricular ectopy after a myocardial infarction using encainide, flecainide, or moricizine [3]) [4]. Discovery and replacement introduce novel, beneficial therapies into clinical practice, while reversal implies that patients receive no benefit and may be at risk of harm [5]. The adoption of clinical practices that are later de-adopted imposes substantial inefficiencies on the healthcare system wherein resources that could have been dedicated to other purposes are instead devoted to a practice that was ineffective or harmful (e.g., self-monitoring of blood glucose in patients with type 2 diabetes mellitus managed without insulin) [6].

Practice reversal is common [5, 7, 8]. A recent review of articles published in a major general medical journal between 2001 and 2010 found that 27 % of original articles re-examined the efficacy of an established practice, among which 40 % found evidence for practice reversal [7]. In another review, commissioned by the Australian government’s Comprehensive Management Framework for managing their Medical Benefits Schedule, Elshaug and colleagues triangulated data from searches of the peer-reviewed literature, targeted health technology databases, and opportunistic sampling of stakeholder groups to identify 156 potentially unsafe and/or ineffective practices [8].

Medical reversal may be an unavoidable consequence of evidence-based medicine and/or early technology adoption; however, it is important that its incidence remain low given the threat that it poses to providing high-quality healthcare. It is equally important that any intervention with evidence for medical reversal be rapidly de-adopted. We were unable to identify any knowledge synthesis that systematically examined the de-adoption of established clinical practices. We conducted this scoping review to describe the literature on de-adoption, document current terminology and frameworks, map the literature to a proposed conceptual framework (Table 1), identify gaps in the understanding of this important concept, and identify opportunities for more detailed evidence syntheses and/or empirical research.

Table 1 Proposed framework for conceptualizing de-adoption
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Methods

We developed a conceptual framework for this work that employed the key features of Everett Rogers’ Innovation-Decision model to conceptualize de-adoption (Table 1) [9]. De-adoption was defined as the discontinuation of a clinical practice after it was previously adopted [9]. We followed established scoping review methodology [10, 11], and used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to report the methods and results [12].

Eligibility criteria

We included English-language citations that referred to the de-adoption of any clinical practice in adults (mean age ≥ 18 years) with medical, surgical, or psychiatric illnesses. All original and non-original quantitative and qualitative research citations were eligible; however, we excluded citations that exclusively described the adoption of practices or appropriateness of resource use (e.g., selected use of antimicrobials, appropriate use of surgical procedures, appropriate use of lumbar spine radiography among patients with lower back pain). Although de-adoption is a component within the larger issue of resource optimization, the “appropriateness” of a clinical practice embodies more than simply discontinuing its use. Therefore, we excluded citations primarily focused on clinical practice appropriateness.

Search strategy and data sources

With the help of a medical librarian, we searched the following electronic databases from 1 January 1990 to 5 March 2014: Ovid MEDLINE, Ovid EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), the Cochrane Database of Systematic Reviews, the Cochrane Database of Abstracts and Reviews of Effects, and CINAHL Plus. Pilot searches in MEDLINE suggested that none of the currently available Medical Subject Heading (MeSH) terms were specific to articles reporting de-adoption. Therefore, the MEDLINE search was confined to use of text words that included combinations and synonyms of de-adoption and healthcare technologies (Additional file 1: Appendix). Search terms were combined using the appropriate Boolean logic, and included wildcards to account for plural words and variations in spelling. The search strategy included similar combinations of terms within the other databases. To ensure reproducibility, the MEDLINE search strategy was peer reviewed by a second medical librarian using the Peer Review of Electronic Search Strategies (PRESS) checklist [13].

To increase the sensitivity of the search strategy, we also searched the gray literature according to recommendations from the Canadian Agency for Drugs and Technologies in Health [14]. Relevant websites included The Canadian Agency for Drugs and Technologies in Health, Program for Assessment of Technology in Health, Australian Government Medical Services Advisory Committee, Austrian Institute of Technology Assessment, National Institute for Health and Care Excellence, Agency for Healthcare Research and Quality, Blue Cross & Blue Shield Association, Choosingwisely.org and Choosingwiselycanada.org, and the Trip Database. Additional citations were identified by (1) contacting experts in implementation science; (2) using the PubMed “related articles” function; and (3) hand-searching bibliographies from important implementation science/adoption of innovations textbooks [9, 15, 16], and reference lists of included citations. Reference management was performed in EndNote (version X7, Thomson Reuters).

Citation selection

Prior to the screening of titles and abstracts, the citation screening form was calibrated by three team members (DJN, KJM, JKH) independently with a random sample of 50 citations. Once consistent citation selection was achieved (kappa ≥ 0.8) [17], all citations were screened for inclusion independently and in duplicate by three reviewers through a two-stage process. During level-one screening, titles and abstracts were reviewed to determine citations that met the inclusion/exclusion criteria. The full text of any citation classified as “include” or “unclear” was reviewed to determine whether it met study inclusion criteria (level-two screening). Eligibility disagreements were resolved by consensus, or arbitration by a third reviewer. Agreement between reviewers at all stages of citation selection was quantified using the kappa statistic [17].

Data extraction and synthesis

Three reviewers independently extracted data from all included citations using a pre-designed electronic form that was pilot tested using a random sample of 10 citations. Once data were consistently abstracted (kappa ≥ 0.8) [17], reviewers proceeded with full data extraction. Extracted data pertained to (1) the citation (e.g., original research, non-original research, website); (2) the term(s) used to refer to de-adoption (e.g., discontinuance, medical reversal, rejection); (3) characteristics of the target condition(s) or clinical practice(s) (e.g., use of nesiritide in acute decompensated heart failure [18]); (4) characteristics of evidence suggesting de-adoption (e.g., original research versus non-original research); (5) whether barriers and facilitators to de-adoption were reported; and (6) whether conceptual frameworks to promote low-value practice de-adoption were used/cited.

Independently, and in duplicate, reviewers mapped the abstracted data onto the proposed conceptual framework. Articles were summarized using counts, proportions, mean (standard deviation), or median (inter-quartile range, IQR) where appropriate. Data were managed and analyzed using Stata version 13.1 (Stata Corp, College Station, TX, USA).

Results

The electronic database and gray literature searches identified 26,557 unique citations (Fig. 1) that were screened for inclusion, from which 110 full text citations were retrieved for further assessment. An additional 51 articles were identified through review of bibliographies, and consultation with knowledge translation experts. From these 161 full text citations, 109 were included in the final review. The most common reason citations were excluded after full text review was owing to an explicit focus on the adoption and/or appropriateness of clinical practices (n = 25).

Fig. 1
figure 1

Details of the article selection process. CADTH Canadian Agency for Drugs and Technologies in Health, KT Knowledge Translation

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Description of the included citations

A description of the included citations is provided in Table 2. Most citations were original research studies (65 %), with the majority being either quasi-experimental (28 %) or cohort studies (14 %). Among the non-original citations, most were editorials or letters to the editor (19 %), or narrative reviews (15 %). Most articles originated in North America (60 %) with the USA representing the most common country (47 % of all articles). The majority of articles were published from 2010 onwards (59 %), with very few published prior to 2000 (3 %). Most articles described the de-adoption of therapeutic interventions (62 %), with comparatively fewer describing the de-adoption of diagnostic interventions (30 %). The randomized clinical trial was most frequently cited (41 %) as the level of evidence that should trigger de-adoption, and most articles cited risk of harm (73 %), and/or lack of efficacy (63 %) as the reason practices should be de-adopted. Among the articles that reported the original reason for clinical practice adoption (n = 16, 15 %), most (n = 10, 63 %) cited observational research (case series and cohort studies) as the evidence that shaped adoption. A detailed, referenced bibliography of the citations is provided in Additional file 1: Table S1.

Table 2 Characteristics of included citations
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De-adoption terminology

We identified 43 unique terms representative of the process of de-adoption (Table 3). The majority of citations (65 %) referred to de-adoption using more than one term, and among these the median (IQR) number of terms per citation was 3 (2–3). Disinvest* was the most frequently cited term (39 % of included citations). Other commonly cited terms included decrease use (24 %), discontinu* (16 %), abandon* (16 %), reassess* (14 %), obsole* (12 %), medical reversal (11 %), and contradict* (10 %). Terms such as de-implement* and de-adopt* were infrequently cited (4 % and 3 %, respectively). A term representative of the process of de-adoption was found in the title or abstract of 86 % of citations and most frequently included disinvest* (31 %), decrease use (12 %), reassess* (7 %), withdraw* (7 %), medical reversal (6 %), discontinu* (6 %), and obsole* (6 %). Each of the 43 unique terms was mapped onto our conceptual framework. The majority of terms (n = 22, 51 %) referred to facilitating the de-adoption process. Seventeen terms (40 %) mapped to more than one category within the conceptual framework, with the most common cross-classification being facilitate de-adoption and sustain de-adoption (13/17, 76 %).

Table 3 De-adoption terms (n = 43) and frequency of their use within included citations
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Barriers and facilitators to de-adoption

Barriers and facilitators to de-adoption were cited within 51 and 48 of the included citations respectively. The bulk of articles citing barriers to or facilitators of de-adoption were original research (Fig. 2).

Fig. 2
figure 2

Distribution of articles citing barriers to and facilitators of de-adoption according to type of research

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Mapping citations to the de-adoption conceptual framework

Articles frequently mapped to more than one category within our conceptual framework (Fig. 3). The primary focus among included citations was evaluating de-adoption outcomes (50 %), identifying low-value practices (47 %), and facilitating the de-adoption process (40 %). Two articles (2 %) discussed sustaining de-adoption. Most articles whose focus was on evaluating de-adoption outcomes were original research (80 %), whereas the majority of articles that discussed identifying low-value practices were non-original research (63 %).

Fig. 3
figure 3

Distribution of articles according to classification within the conceptual framework and type of research

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Frameworks for the de-adoption of low-value clinical practices were provided in 11 citations (Table 4), of which half were derived from original research (n = 5, 45 %). Two citations documented clinical application of their framework [19, 20]. Seven citations described frameworks for identifying and prioritizing candidate low-value practices, and nine citations described frameworks for facilitating the de-adoption process. Among citations that described a framework for identifying low-value practices, common mechanisms included consultation with clinical stakeholders, monitoring for new scientific evidence, examining for practices with large between-provider variation, and/or embedding the notion of health technology reassessment within the life cycle of any given practice. Commonly proposed criteria for prioritizing the de-adoption of low-value practices included the availability of evidence that a candidate practice is ineffective or harmful, the safety of the low-value practice (i.e., harmful practices prioritized ahead of those that are simply ineffective), potential health and cost impact of de-adoption, and availability of alternative practices. Among citations that described frameworks for facilitating the de-adoption process, common mechanisms included restructuring of funding associated with the given practice, changes to local and/or regional policies, and more consistent integration of health technology reassessment within existing health technology assessment programs.

Table 4 Frameworks proposed to guide the de-adoption of low-value practices
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Lists of low-value practices were provided by eight citations (Table 5). Searches of the published literature were the most frequently employed means of identifying low-value practices (n = 7 citations, 88 %); however, the sources searched and the approach to defining a low-value practice varied by citation. Evidence was combined with stakeholder engagement to identify low-value practices in three citations [8, 21, 22], and one citation identified low-value practices as those shown to have high variability in rates of use between providers [23]. Among the seven citations that used the published literature to identify low-value practices, the prevalence of low-value practices ranged from 16 % [24] to 46 % [5], with two studies each identifying more than 100 low-value practices [7, 8].

Table 5 Original research citations that identified lists of low-value clinical practices
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The impact of de-adoption efforts was evaluated and reported in 39 original research citations (Table 6). Most studies used interrupted time series methodology (n = 21, 54 %) and obtained data from large administrative databases or clinical registries (n = 30, 76 %). The most common target conditions were cardiovascular disease (n = 11, 28 %), arthritides (n = 8, 21 %), and menopause (n = 7, 18 %). All but one of the practices (pulmonary artery catheter) examined were therapeutic interventions. The most frequently examined therapies included cyclo-oxygenase-2 (COX-2) inhibitors and other non-steroidal anti-inflammatory drugs (NSAIDs) (n = 8, 21 %), hormone replacement therapy (n = 7, 18 %), and percutaneous coronary intervention (n = 3, 8 %). Thirteen studies reported on de-adoption efforts that followed an active change intervention, all of which demonstrated reductions in the target low-value practice [25–37]. The most common intervention was withdrawal of a low-value drug from the market (n = 9, 23 %). Other active change interventions commonly included an education component targeted at patients and/or providers. Of the 26 studies that did not report on the effects of an active change intervention, 23 (88 %) demonstrated reductions in the target practice. Of the 27 and 11 studies that examined de-adoption efforts for harmful or ineffective practices, respectively, 25 (92 %) and 9 (81 %) demonstrated reductions in the target practice.

Table 6 Original research citations that evaluated the de-adoption of low-value clinical practicesa
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Discussion

De-adoption of low-value clinical practices is essential to improve healthcare quality and create a sustainable healthcare system. To our knowledge this is the first knowledge synthesis to comprehensively examine the de-adoption of low-value clinical practices. We identified 109 citations, most of which were published within the last five years, and concentrated on evaluating changes in practice that occurred following the publication of evidence for medical reversal. We identified 43 terms used to refer to the process of de-adoption, with disinvest being the most frequently cited term. We also identified 13 frameworks that conceptualize individual components of the de-adoption process, and from these frameworks propose a model for de-adoption (Fig. 4). These results provide foundations for guiding the de-adoption of ineffective and harmful clinical practices from patient care as well as directing future research.

Fig. 4
figure 4

Synthesis model for the process of de-adoption. a Identification of low-value practices includes the process of reviewing and selecting de-adoption knowledge. b Current literature suggests prioritizing based on safety of the low-value practice (i.e., harmful practices eliminated first), potential health and cost impact of de-adoption, and availability of alternative practices

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The first major finding from our study pertains to the diverse list of terms used to refer to de-adoption with no clearly established taxonomy. The implication of this is that communication is impaired, which may impact “branding” of de-adoption and efficient searching for relevant literature. Furthermore, it is unclear how different concepts and initiatives such as “less is more” [38], reducing research waste [39], and Choosing Wisely [40] are related. Conversely, knowledge translation and implementation science are increasingly recognized terms in healthcare research, facilitating understanding and communication of the related concepts. Terms such as de-adoption and de-implementation that have a more general connotation, and are natural antonyms of adoption and implementation, ought to be used as terms that brand the process of reducing or removing low-value clinical practices. Other terms, such as disinvest, describe specific elements of the de-adoption process and are not ideal candidates to brand this process. Interestingly, de-adoption and de-implementation were infrequently cited within the included citations, whereas disinvest was the most commonly cited term. Given this lack of clarity with regard to de-adoption terminology, there is an urgent need to develop a taxonomy of terms.

Using the proposed conceptual framework (Table 1), themes common to the frameworks identified in the scoping review (Table 4), and the Knowledge-to-Action framework [41], we derived the second major result from this study, a synthesis framework for facilitating de-adoption (Fig. 4). At the heart of this framework is the identification and prioritization of low-value practices. The identification process involves determining the low-value practice(s) and selection of the knowledge unit that defines a practice as low-value (i.e., randomized clinical trial, systematic review, and/or clinical practice guideline). With regard to prioritization when there is more than one low-value practice identified, current literature suggests prioritizing based on strength of evidence supporting lack of efficacy, safety of the low-value practice (i.e., harmful practices eliminated first), potential health and cost impact of de-adoption, and availability of alternative practices. To permit more of an integrated de-adoption process, and thus improve the probability of success, we suggest stakeholder engagement take place concomitant with practice identification and prioritization. The de-adoption process is then envisioned to follow a similar action cycle as in the original Knowledge-to-Action cycle [41]. However, given the anticipated challenges associated with discontinuing established clinical practices [42], the analysis of barriers and facilitators will require a greater in-depth exploration of both scientific (e.g., presence and quality of evidence supporting de-adoption) and non-scientific (e.g., historical, political, social, and economic factors) barriers to de-adoption [43]. In addition, the intervention that guides de-adoption will likely need to be more closely integrated into clinical care pathways compared to that for adoption, with policy changes and/or changes to funding models predicted to have the greatest likelihood of facilitating de-adoption. Implementation of the intervention will need to be evaluated, and outcomes such as low-value practice use, costs, and potential harms assessed. Finally, any de-adoption intervention should include a sustainability plan; else it is highly likely that healthcare providers will (knowingly or unknowingly) revert to using the practice to which they have become habituated [44].

The third important result from this review is the identification of key questions that require additional research to advance the science of de-adoption. For example, there are multiple factors that likely determine when a practice should be de-adopted (e.g., nature of the intervention, lack of effectiveness or degree of harm, nature of the evidence) but the role of each factor and the interplay among them that ultimately determines when to de-adopt is not clear. In addition, what do we do with clinical practices that are ineffective for a broad population, but may be effective in a small subgroup that is difficult to study? To answer these and other questions we need additional knowledge syntheses that establish a taxonomy of de-adoption terminology, summarize barriers and facilitators to de-adoption, and quantify the impact of past examples of de-adoption. We also need empirical research to examine optimal strategies for identifying candidate low-value practices, and to determine which de-adoption strategies are likely to have the greatest impact. Furthermore, given existing fiscal climates with limited resources, we also need to balance the need to refine and prioritize the science of de-adoption with the need to do the same for adopting new practices.

While we await this additional research, what can healthcare decision-makers practically do with the existing knowledge base? First, this review highlights that de-adoption requires a multi-dimensional construct that is far more complex than simply ceasing to provide a given practice. Second, several studies have demonstrated that de-adoption does occur in response to publication of new evidence (Table 6), with the most consistent de-adoption occurring in response to an active change intervention. The intervention with the greatest likelihood of de-adoption is market withdrawal of a harmful drug. However, the real challenge lies in how to actively facilitate de-adoption when market withdrawal is not possible (e.g., insulin [45]), or not clearly indicated (e.g., practices that are simply ineffective). Interventions cited as having the greatest likelihood of effecting de-adoption include changes to policies, and/or restructuring of funding associated with the low-value practice, the latter through strategies of disinvestment, reinvestment, or defunding. However, this scoping review did not identify any studies that applied a strategy of disinvestment in response to evidence for medical reversal. At this point, pending further research, we suggest use of our proposed synthesis model (Fig. 4) as a starting point for anyone interested in promoting the de-adoption of low-value practices.

There are limitations to this review. First, our search may have missed relevant articles due to the lack of indexing terminology specific to de-adoption that for practical reasons forced us to restrict the search to English language articles published from 1990 onwards. However, the majority of included citations were published after 1999, and originated in high-income countries, therefore it is unlikely that we missed any broad concepts related to de-adoption. Second, grouping articles and de-adoption terminology according to the main categories in the conceptual framework, even though completed in duplicate by independent reviewers, is partly subjective. Finally, we elected to conduct a scoping review in order to provide an inclusive and broad description of what is known about de-adoption and therefore are limited in our ability to present granular details. Our work identifies opportunity for future systematic reviews.

Conclusions

De-adoption of low-value clinical practices is essential to improve healthcare quality and create a sustainable healthcare system. We identified a large body of literature that describes current approaches, and challenges to the de-adoption of low-value clinical practices. Our results should promote future research in at least two areas. First, knowledge syntheses are required to explore areas wherein there is an abundance of literature, such as establishing a taxonomy of de-adoption terminology, summarizing barriers and facilitators to de-adoption, and quantifying the impact of past examples of de-adoption. Second, empirical research is required to examine optimal strategies for identifying candidate low-value practices, and to determine which de-adoption strategies are likely to have the greatest impact. In the meantime, we have developed a conceptual model that providers and decision-makers can use to guide efforts to de-adopt ineffective and harmful practices and describe examples of successful de-adoption that can be used to inform efforts.