Organizational- and system-level characteristics that influence implementation of shared decision-making and strategies to address them — a scoping review

Background Shared decision-making (SDM) is poorly implemented in routine care, despite being promoted by health policies. No reviews have solely focused on an in-depth synthesis of the literature around organizational- and system-level characteristics (i.e., characteristics of healthcare organizations and of healthcare systems) that may affect SDM implementation. A synthesis would allow exploration of interventions to address these characteristics. The study aim was to compile a comprehensive overview of organizational- and system-level characteristics that are likely to influence the implementation of SDM, and to describe strategies to address those characteristics described in the literature. Methods We conducted a scoping review using the Arksey and O’Malley framework. The search strategy included an electronic search and a secondary search including gray literature. We included publications reporting on projects that promoted implementation of SDM or other decision support interventions in routine healthcare. We screened titles and abstracts, and assessed full texts for eligibility. We used qualitative thematic analysis to identify organizational- and system-level characteristics. Results After screening 7745 records and assessing 354 full texts for eligibility, 48 publications on 32 distinct implementation projects were included. Most projects (N = 22) were conducted in the USA. Several organizational-level characteristics were described as influencing the implementation of SDM, including organizational leadership, culture, resources, and priorities, as well as teams and workflows. Described system-level characteristics included policies, clinical guidelines, incentives, culture, education, and licensing. We identified potential strategies to influence the described characteristics, e.g., examples how to facilitate distribution of decision aids in a healthcare institution. Conclusions Although infrequently studied, organizational- and system-level characteristics appear to play a role in the failure to implement SDM in routine care. A wide range of characteristics described as supporting and inhibiting implementation were identified. Future studies should assess the impact of these characteristics on SDM implementation more thoroughly, quantify likely interactions, and assess how characteristics might operate across types of systems and areas of healthcare. Organizations that wish to support the adoption of SDM should carefully consider the role of organizational- and system-level characteristics. Implementation and organizational theory could provide useful guidance for how to address facilitators and barriers to change. Electronic supplementary material The online version of this article (10.1186/s13012-018-0731-z) contains supplementary material, which is available to authorized users.


Background
Although recognized as ethically important and frequently included in healthcare policies [1], the practice of engaging patients in their healthcare decisions is infrequently implemented in routine care [2][3][4][5][6]. Research on shared decision-making (SDM) has identified this failure of implementation, but has focused primarily on the associated patient-and provider-level characteristics [7][8][9][10]. Studies of other practice-changing interventions have similarly identified implementation challenges, but in other areas, the search for solutions has extended to characteristics of healthcare delivery beyond the patient and clinician to the organizational characteristics and the system-level policies. How these findings from the implementation literature, and research on organizational-and system-level characteristics specifically, might affect efforts to implement SDM is not well known.
SDM is a widely recognized approach to cultivate patient-centered care [11,12]. It is an approach where clinicians and patients share the best available evidence when faced with the task of making decisions, and where patients are supported to consider options and to achieve informed preferences [13]. SDM is a communicative process that can be supported by the use of decision aids, also called decision support interventions. In the last several years, there has been growing interest in advancing SDM in routine healthcare. In many countries, health policies include implementation of SDM. In a series of articles recently published on the development of activities to promote SDM in 22 different countries, it was shown that 19 countries have health policies that foster or even demand SDM implementation [1]. Despite this health policy commitment to SDM and its inclusion in a range of clinical practice guidelines, study results from other countries point towards poor implementation in routine clinical practice [2][3][4][5][6].
These results have led to work that attempts to explain the difficulty of implementing SDM in routine care. Research on barriers to and facilitators of SDM mostly identifies contributing factors at the individual level of care, i.e., characteristics of individual patients, clinicians, or the direct patient-clinician interaction [8][9][10]. Two systematic reviews on perceived barriers and facilitators of SDM implementation not only reported individual factors (i.e., knowledge, attitudes, and behavior), but also included a few environmental factors (e.g., time, resources) [10,14]. A similarly narrow focus on attitudes, skills, and behavior of individual clinicians and patients manifest in most interventions developed for SDM [15]. Recent work has acknowledged the importance of taking organizational-level characteristics into account. These are the characteristics of specific healthcare organizations (i.e., entities that deliver healthcare, e.g., hospitals, practices) that affect the implementation of SDM. For example, Müller and colleagues [16] highlighted the importance of organizational culture, leadership support, and changes in workflow structures to better implement SDM in cancer care. Additionally, little is known about the role of system-level characteristics in the implementation of SDM. These are the characteristics of the healthcare system that guide the work of healthcare organizations (i.e., the political, economic, and social context in which healthcare organizations are embedded, e.g., policies and legislation) [17].
Research on the implementation of health innovations has shown that it is crucial to take into account characteristics of healthcare institutions and of the healthcare system at large in order to change practice [18][19][20]. Those characteristics may otherwise function as powerful barriers to implementing SDM at the individual encounter level. Nevertheless, implementation strategies are often targeted to change knowledge, attitudes, and behavior of individual providers [21], hindered perhaps, by the lack of measures available to assess system-level characteristics [18]. Similarly, in research on SDM, no studies have focused solely on an in-depth synthesis of the literature around organizational-and system-level characteristics that may influence the implementation of SDM in routine care. A greater understanding of the organizational-and system-level characteristics that could impede or support implementation of SDM in routine care may be helpful in finding ways to address these characteristics in implementation strategies. Thus, the aim of this scoping review is to compile a comprehensive overview of experiences with organizationaland system-level characteristics in implementing SDM in routine care. The following research questions guided this scoping review: 1. What experiences with organizational-and systemlevel characteristics are reported in SDM implementation projects? 2. What strategies to address these characteristics are discussed in the literature?

Design
We performed a scoping review rather than a systematic review due to the broad nature of our research questions, the young field of SDM research, and our anticipation of high variation in study designs and methodologies [22]. We used the definition of scoping review given by Colquhoun and colleagues: "a form of knowledge synthesis that addresses an exploratory research question aimed at mapping key concepts, types of evidence, and gaps in research related to a defined area or field by systematically searching, selecting, and synthesizing existing knowledge" [23].

Protocol
We developed our protocol based on the Arksey and O'Malley framework [22], as well as on subsequently published guidance on how to conduct scoping reviews [24][25][26]. The final version of the protocol can be found in Additional file 1.

Eligibility criteria
We included publications that reported on the results of projects, quality improvement programs, or studies that aimed to implement SDM, decision aids (i.e., tools for use inside or outside the clinical encounter [27]) or other decision support interventions (i.e., mediated by more interactive or social technologies [27]) in routine healthcare through a certain implementation strategy or effort. To be included, these full texts also needed to report on organizational-level and/or system-level characteristics described to influence the implementation, and/ or describe strategies that might address organizationallevel and/or system-level characteristics. Opinion pieces, reviews, and study protocols were excluded, but reviews were used in the secondary search process, as described below. The full list of inclusion and exclusion criteria, specifying concepts and contexts of this scoping review, is displayed in Table 1.

Search strategy
We performed an electronic literature search in Medline, CINAHL, and Web of Science Core Collection. We included articles published between January 1997, the year in which Charles and colleagues described the concept of SDM in their seminal article [28], and October 10, 2016. The search was limited to articles published in English or German, as these were the only languages spoken by a minimum of two members of the review team. Details of the search strategies in the different databases can be found in Additional file 2. Our primary electronic search was complemented by a comprehensive secondary search strategy. All records excluded through criterion E2 (systematic, scoping, and structured literature reviews) [10,14,15,[29][30][31][32][33][34][35][36][37][38][39][40][41][42] were checked to see whether they reported on studies that could potentially be relevant for this scoping review. Subsequently, the reference lists of six of these reviews  [10,15,29,36,39] were assessed for eligibility. Furthermore, two books were searched for chapters meeting the inclusion criteria [43,44], and a gray literature search was conducted on a range of websites listed in Additional file 3.

Study selection process
We imported all identified records into reference management software (Endnote) and removed duplicates. First, IS and a second reviewer (PH, AL, or RPM) performed an independent title and abstract screening to check for potential inclusion of records. A record was included into the next step of full text assessment if at least one reviewer deemed it appropriate. Second, full text assessment was conducted. To ensure quality and consistency of full text assessments, the first 20% of randomly selected full texts were assessed by two team members (IS and PH or IS and AL). In 83% of the cases, the team members agreed on inclusion or exclusion. Discrepant assessments were subsequently discussed by the team members. This process led to minor revisions in the exact wording of the inclusion and exclusion criteria and an instruction of how to use the criteria. Then, another round of double assessment using another set of 10% of randomly selected full texts was conducted, leading to agreement in 93% of the cases. The subsequent assessment of the remaining 70% of full texts was conducted by one reviewer (IS) using a conservative approach. Whenever the single assessor (IS) was in slight doubt about whether to include or exclude a full text, a second reviewer was assigned to assess that full text, and final decision regarding inclusion was made by discussion. This procedure was done for a total of 14 full texts.

Data extraction
We extracted general information on each study and specific information related to the research questions. We extracted any information on experiences related to organizational-and system-level characteristics and potential strategies to address them. As we wanted to give a broad overview, we extracted all information on experiences reported in the publications, including experiences derived from results (empirical) and from the interpretation of results (opinion-based). The number of full texts identified and selected is described using the PRISMA flowchart. The initial data extraction sheet was developed by one team member (IS), based on experience from other reviews [12,15,45,46]. It was pilot tested by IS and AL, using two included full texts [47,48]. We compared the extracted data and found only very minor differences in the level of detail of the respective extractions. As a result, the extraction sheet was slightly revised (e.g., by adding definitions of what to extract). Further data extraction was conducted by one person (either AL or IS). Whenever one data extractor was in doubt regarding what to extract for a certain category, the second person checked the full text and both met to discuss agreement on what to extract.

Methodological quality appraisal
We did not appraise the methodological quality or risk of bias of the included studies, which is consistent with guidance on the conduct of scoping reviews [22].

Synthesis
We conducted a descriptive analysis of characteristics of the included studies (e.g., types of study design, years of publication) as well as a qualitative thematic analysis of the organizational-and system-level characteristics identified in the studies. We decided to report what other studies reported as influential characteristics, rather than classify them as barriers or facilitators. This analysis drew on principles of qualitative content analysis described by Hsieh and Shannon [49] and consisted of the following steps: first, two researchers (AL and IS) read the entire set of extracted data to gain an overview. Second, one researcher (AL) coded the material (initial inductive coding). Third, comments by a second researcher (IS) led to adaptation of the coding system. Fourth, the revised codes were organized into a coding system using clusters and subcategories, agreed in discussion with two other team members (GE and SK). Fifth, the material was re-coded by one researcher (AL) using the established coding system. Sixth, the re-coded material was cross-checked by a second researcher (IS) and minimal changes were made in discussion (IS and AL). Potential strategies mentioned in the publications to address organizational-and system-level characteristics were synthesized and mapped onto identified characteristics in a team discussion (IS, AL, GE). No qualitative data analysis software was used. Analyses were conducted on the level of distinct implementation projects, i.e., publications reporting on the same implementation project were grouped under one single project ID.

Included studies
After screening 7745 titles and abstracts for eligibility, and checking 354 full texts against the inclusion and exclusion criteria, we included 48 full texts (see Fig. 1).
Reasons for exclusion of full texts are displayed in Table 1. The included full texts report on a total of 32 distinct implementation projects. While most projects were only reported in a single publication, several projects were described in two or more publications. Twenty-two projects were conducted in the USA, and 26 projects focused on the implementation of decision aids or other forms of decision support. Projects focused on various settings and a broad range of decisional contexts. Table 2 gives an overview on the included projects and publications. Organizational-level characteristics Table 3 displays the organizational-level characteristics reported in the included full texts as influencing the implementation of SDM, decision aids, or other decision support interventions. The table includes descriptions of all identified characteristics. Six main categories of organizational characteristics were described in the included studies: organizational leadership, culture, teamwork, resources, priorities, and workflows. Five of the six main categories also included several subcategories of organizational-level characteristics; for example, the category "organizational resources" included the subcategories time (that healthcare providers have per patient), financial resources (that are available for certain activities within a healthcare organization), workforce (i.e., employees available for and assigned to certain activities within a healthcare organization), and space (i.e., room available for certain activities within a healthcare organization). Both the availability of resources within an organization and organizational workflows (e.g., patient information dissemination strategies, scheduling routines, use of the electronic health record) were described to have influenced SDM implementation efforts in over three quarters of the projects, and facets of the organizational culture and teamwork within an organization were reported in only a third of the projects (see column "Project IDs" in Table 3).

System-level characteristics
While many organizational characteristics were identified in the included full texts, only four main categories of characteristics of the healthcare system were described: incentives (i.e., the role of payment models and accreditation/certification criteria), policies and guidelines (i.e. the role of healthcare legislation and clinical practice guidelines), culture of healthcare delivery, and healthcare provider education and licensing. Table 4 gives an overview of the characteristics of the healthcare system that were reported as influencing implementation. The table includes descriptions of all identified characteristics. While only four projects reported that the culture of healthcare delivery influenced SDM implementation, about one third of the projects reported that incentives, policies and guidelines, and healthcare professional education and licensing influenced SDM implementation (see column "Project IDs" in Table 4).

Strategies to address organizational-and system-level characteristics
A range of possible strategies to address organizationaland system-level characteristics and thereby potentially foster SDM implementation were discussed in the publications and mapped onto the identified characteristics. They are displayed in Table 5. Similar to the results on experienced characteristics, most proposed strategies focused on the organizational level. Most studies identified workflow as an organizational-level characteristic influencing SDM implementation and also generated potential strategies to tackle that characteristic. Few strategies were suggested to change organizational culture [50][51][52], which was also described in fewer studies. A large range of potential strategies were also described to promote leadership activities that might facilitate SDM implementation (see full list in Table 5). At the system-level, fewer strategies were described. Suggestions included changes in payment models [53][54][55], legislation [51,56,57], and health professional education [51,[58][59][60].

Summary of the review findings
We described a broad range of organizational-and system-level characteristics that were experienced as influencing the implementation of SDM in routine care, as well as strategies to potentially address those characteristics.
Included studies reported more often on characteristics          influencing the organizational level than the health system level. The reported organizational characteristics are strongly influenced by health system characteristics; for example, the amount of time that a HCP has for a patient's visit is linked to payment models, the organizational culture is influenced by the general culture of healthcare delivery, and the leadership decisions within an organization are affected by policies, payment models, and accreditation criteria. As the identified characteristics can be barriers, facilitators or both barriers and facilitators to SDM implementation, we described them in a valueneutral way.

Strengths and limitations
We extracted reports from implementation studies described in any part of the included publications. Our analyses therefore cannot differentiate between experiences based on results and those reflecting interpretation of results. However, for a young research field, we believe this broad scoping review is an important first step to gaining an overview of the topic. A second limitation is that the primary search was limited to three electronic databases, so we might have missed relevant publications. However, we prioritized sensitivity in our electronic search, which is reflected by the high number of screened abstracts, to identify most relevant work. Furthermore, we conducted an extensive secondary search, including gray literature to find more work not indexed in the electronic databases searched. Another limitation is that we did not conduct a full double assessment and double data extraction. However, we did our best to minimize error by consulting with a second reviewer whenever there was the slightest doubt. A main strength of this review is that it is the first of its kind to focus solely on the impact of organizational and system characteristics on the implementation of SDM. In previous work, the focus had mainly been on the individual clinician-patient level, and organizational-and system-level characteristics had not been examined in depth [10,14]. Furthermore, it was conducted in an inter-professional and international team.

Comparison to previous work
First, these findings need to be compared to previous work on SDM. Our results reinforce prior calls for better coordination of care, engagement of non-physician personnel, and the use of the electronic health record (EHR) to implement SDM in previous work [61]. The suggestions to use clinical practice guidelines, postgraduate training, and accreditation as means to better implement SDM [5] are also reflected in the data collected in this scoping review. Many of the characteristics identified in this review have been discussed in trials of SDM interventions or decision aids, in studies of clinicians' perceptions, or in opinion pieces, but this is the first piece of work looking at characteristics experienced in actual implementation studies.
Second, the results need to be compared to more general work in healthcare implementation science, beyond the case of SDM as a particular innovation to implement. Implementation frameworks and conceptual models like the one postulated by Greenhalgh and  For projects described in more than one publication, at least one publication had to report on a specific characteristic to be listed in this table colleagues [20] or the Consolidated Framework for Implementation Research (CFIR) [19] have described elements in the inner and outer settings to influence implementation. Our results found a range of very similar characteristics on the organizational level to the ones described in the inner setting, e.g., communication and culture within an organization, leadership engagement, resources, and priorities. However, some of the characteristics we found (e.g., workflows) were not described in the CFIR [19]. One could hypothesize that these aspects are more focused around decision aid implementation and therefore not included in a more general implementation framework. Similarly, several of our system-level characteristics map well onto the CFIR's outer setting (i.e., aspects around policies, guidelines, and incentives), but the culture of the healthcare system and education and licensing of healthcare professionals cannot be found in the framework [19]. Furthermore, a systematic review on determinants of implementation of preventive interventions on patient handling identified a total of 45 environmental barriers and facilitators [62] that overlap with experienced organizational characteristics identified in our scoping review, particularly the availability of resources, leadership support, and the organization of workflows. Overall, our results in the field of SDM display many similarities with the characteristics described in implementation science frameworks and in other fields of health innovation. However, as we also identify characteristics less described in implementation science literature, we believe it is important to not to be restricted by such frameworks, but enrich them with derived empirical evidence. Third, some of the strategies recommended by the included projects to intervene on characteristics influencing SDM implementation (Table 5) are vague and require further specification and tailoring to a specific context [63]. For example, some of the strategies that fall into the leadership category could benefit from distinguishing which level of leadership should take action for which strategy. While the people in a governing board of an organization might be the ones to revise mission statements, executive leadership, and departmental management might be the ones who create a culture that supports SDM [64]. Furthermore, all other categories identified as organizational-level characteristics, despite not specifying who should be in charge of making specific changes, imply that organizational leadership is the actor here. Although it is not specified, for example, who should implement multidisciplinary teams or create an SDM coordinator position, there is an implicit assumption that these are leadership tasks. Beyond looking at implementation literature, it might therefore be worthwhile for stakeholders working on SDM implementation to look into organizational theories in healthcare [65], e.g., on the effective organization of healthcare teams or on strategies to restructure healthcare organizations.

Implications and suggestions for further work
As healthcare systems are complex and composed of components that act nonlinearly [66], a certain identified characteristic can be a facilitator to one stakeholder and a barrier to another. Therefore, more work is needed to move beyond the descriptive stage of this review, especially as differences in the numbers of studies reporting on certain characteristics do not necessarily mean that those characteristics are the most important. Similarly to Koppelaar et al. [62], we believe there is a need to quantify the influence of the identified characteristics, especially as this scoping review's broad nature is not distinguishing between experiences based on results of implementation studies and interpretation of those The descriptions are the result of the thematic analysis *For projects described in more than one publication, at least one publication had to report on a specific characteristic to be listed in this table Organizational culture Foster a well-organized and amicable work environment [50] Align SDM implementation with organization's existing patient-centered philosophy and quality improvement spirit [51,52] Autonomy of staff Allow flexible use of decision aids and freedom on how to achieve SDM implementation goals [7,47,51] Shared views and goals Address relational dynamics of healthcare teams before SDM implementation [89] Hold regular meeting to share goals and successes [54] Organizational teamwork Communication Foster frequent, timely, accurate, and problem solving communication about SDM implementation within and between teams [7,89,97] Coordination of care Implement multidisciplinary teams [79,102] Have a patient navigator [102] Have a clear definition of team members' roles [50,53] Organizational resources Time Decrease pressure for short patient interactions [105]/expand time to spend with patient [58,103] Tailor interaction length guidelines for type of interaction [104] Financial resources Obtain funding for SDM activities [90] Have access to high quality decision aids at low or no cost [52] Space Use offices instead of clinical exam rooms for delivering decision support [74] Workforce Engage non-physician personnel (e.g., nurses, office staff) [60,70,73,90] Use unpaid or paid student interns or volunteers to deliver decision support [76,77] Reorganize workforce responsibilities from over utilized to underutilized staff [74] Fund/hire a decision support/ care coordinator [77,98] Salaried physicians for which SDM is part of employment obligations [51] Organizational priorities Integrate SDM into other interventions or changes (e.g., health coaching, chronic disease management program) [7,94,110] Align SDM with wider objectives of the organization (e.g., quality and safety) [7,58] Organizational workflows Patient information dissemination strategies Automate decision aid distribution, e.g., pre-visit [78], based on triggers [70], send by mail [58,75,90] Keep decision aids/tools accessible in exam rooms and workspaces [7,86,87] and make them easily available electronically [7,58,105] Offer in-office viewing of decision aids as well as other options (e.g., lending them to patients) [52] Align delivery of decision aids with other aspects of care (e.g., obtaining informed consent) [91] Partner with resource centers to deliver decision support [77] Clarify the place that decision aids have in the clinical pathway [103] Make decision aids available via a state-run website [51] Create protocols to prompted staff members to prescribe decision aid corresponding to the reason for referral [70] Scheduling routines and time frames Get decision aids to patients prior to consultations [50,52] Install scheduling system for SDM/decision aids/decision support [74,103,108] Require slowing down the flow of decision-making/reduce time pressure on patient path to treatment decision [58,91] Allow for flexible patient pathways and scheduling [7,75] results. By evaluating the influencing characteristics in implementation studies, we could analyze interactions between characteristics and find out which of them predict implementation outcomes [18].
As the included studies were predominantly from the USA, future work needs to assess the importance of the identified characteristics in different healthcare systems with variation in financing, coverage, spending, utilization, capacity, and performance [67], as well as different fields of healthcare (e.g., cancer care, mental healthcare). This would help to gain a more specific insight that could foster prioritization of the most important characteristics in a particular setting and strategies to address them.

Conclusion
Although infrequently studied, organizational-and system-level characteristics appear to play a role in the failure to implement SDM in routine care. A wide range of characteristics described as supporting and inhibiting implementation were identified. Future studies should quantify these characteristics' differential impact on SDM implementation, their likely interactions, and how different characteristics might operate across types of healthcare systems and areas of healthcare. Healthcare organizations that wish to support the adoption of SDM should carefully consider the role of organizational-and system-level characteristics. Implementation and organizational theory could provide useful guidance for how to address facilitators and barriers to change. Funding Support for this research was provided by The Commonwealth Fund and the B. Braun Foundation. The views presented here are those of the authors and should not be attributed to The Commonwealth Fund or its directors, officers, or staff. The funding bodies were not involved in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. Table 5 Described strategies to address identified characteristics (Continued)

Characteristics
Strategies described Electronic health record (EHR) Use EHR to prompt and document SDM process [7,54,70,73] Use EHR (and merge it with computerized scheduling data) to identify patients eligible for decision aids [69,73,78,87,90] Have decision aids available on EHR for easy access and have them available of patient portal on EHR [52,58,95,104,108] System-level strategies Incentives Payment model Use a payment model that motivates providers to engage in SDM (e.g., patient-centered medical home) [51,52,92] Reimburse the use of a decision aid and time spent engaging in SDM conversation [91,96,103] Move away from fee-for-service to alternative model (e.g., pay-for-performance) [53][54][55] Accreditation/certification criteria Revise accreditation/certification criteria by adding the implementation of SDM as criterion/quality indicator [51] Policies and guidelines Legislation Create state legislation that fosters SDM (e.g., comparable to Washington state: enhanced legal protection when doing SDM) [51,56,57] Create legislation that encourages healthcare organization structures that support SDM [51] Practice guidelines Incorporate the use of SDM in clinical practice guidelines [103,105] Quality indicators Make the use of decision aids a quality of care indicator/list SDM as performance metric [55,87,91] Health plans could collect and distribute SDM performance data [51] Use a national set of measures [58] Culture of healthcare delivery Promote culture of patient engagement in medical school [59] Education and licensing Incorporate SDM communication skills (as compulsory) into medical school and residency curricula, as well as into state medical licensing criteria [51,[58][59][60] Offer CME/CEU credits for watching decision aids/for SDM training [54,84,109] HCPs healthcare providers, EHR electronic health record, SDM shared decision-making, CME continuing medical examination, CEU continuing education units