- Open Access
The Cognitive Walkthrough for Implementation Strategies (CWIS): a pragmatic method for assessing implementation strategy usability
Implementation Science Communications volume 2, Article number: 78 (2021)
Implementation strategies have flourished in an effort to increase integration of research evidence into clinical practice. Most strategies are complex, socially mediated processes. Many are complicated, expensive, and ultimately impractical to deliver in real-world settings. The field lacks methods to assess the extent to which strategies are usable and aligned with the needs and constraints of the individuals and contexts who will deliver or receive them. Drawn from the field of human-centered design, cognitive walkthroughs are an efficient assessment method with potential to identify aspects of strategies that may inhibit their usability and, ultimately, effectiveness. This article presents a novel walkthrough methodology for evaluating strategy usability as well as an example application to a post-training consultation strategy to support school mental health clinicians to adopt measurement-based care.
The Cognitive Walkthrough for Implementation Strategies (CWIS) is a pragmatic, mixed-methods approach for evaluating complex, socially mediated implementation strategies. CWIS includes six steps: (1) determine preconditions; (2) hierarchical task analysis; (3) task prioritization; (4) convert tasks to scenarios; (5) pragmatic group testing; and (6) usability issue identification, classification, and prioritization. A facilitator conducted two group testing sessions with clinician users (N = 10), guiding participants through 6 scenarios and 11 associated subtasks. Clinicians reported their anticipated likelihood of completing each subtask and provided qualitative justifications during group discussion. Following the walkthrough sessions, users completed an adapted quantitative assessment of strategy usability.
Average anticipated success ratings indicated substantial variability across participants and subtasks. Usability ratings (scale 0–100) of the consultation protocol averaged 71.3 (SD = 10.6). Twenty-one usability problems were identified via qualitative content analysis with consensus coding, and classified by severity and problem type. High-severity problems included potential misalignment between consultation and clinical service timelines as well as digressions during consultation processes.
CWIS quantitative usability ratings indicated that the consultation protocol was at the low end of the “acceptable” range (based on norms from the unadapted scale). Collectively, the 21 resulting usability issues explained the quantitative usability data and provided specific direction for usability enhancements. The current study provides preliminary evidence for the utility of CWIS to assess strategy usability and generate a blueprint for redesign.
The past two decades have brought growing realization that research evidence—often codified in evidence-based interventions and assessments—is used infrequently, inconsistently, or inadequately in standard clinical care across numerous domains [1, 2]. Implementation strategies are techniques used to enhance the adoption, implementation, and sustainment of new practices and may be discrete (i.e., involving single actions or processes) or multifaceted (i.e., combining two or more discrete strategies) . These strategies are now rapidly proliferating, with multiple compilations identified across a variety of health service delivery sectors [4, 5].
The complexity of implementation strategies varies widely, but most strategies are socially mediated processes that rely, in large part, on interactions among providers, implementation intermediaries/practitioners, or researchers . Multifaceted and multi-level strategies are increasingly common, some of which are intended to be delivered over one or more years [7,8,9]. Implementation strategy complexity has been fueled, in part, by assumptions that multi-component and multi-level strategies may be more effective in promoting positive implementation outcomes [10, 11]. However, this perspective has been disputed , and significant complexity may leave implementation strategies unwieldy, expensive, and ultimately impractical. For instance, the Availability, Responsiveness, and Continuity strategy  is an effective multifaceted organizational approach for improving children’s mental health services, but its year-long delivery timeline may create barriers. Although the selection and tailoring of implementation strategies to address contextual determinants has emerged as a major focus of contemporary implementation research [7, 13, 14], we lack methods to assess the extent to which strategies are usable and aligned with the specific needs and constraints of the individuals who will use them. To be maximally relevant and useful, it is important to ensure that such methods are pragmatic [15, 16], meaning that they should be efficient and low burden, feasible to conduct in real-world settings, and yield actionable information that directly informs decisions about implementation strategy design.
Human-centered design (HCD)
Methods from the field of human-centered design (HCD) have potential to drive assessment of implementation strategy usability and ensure contextual fit in ways that meet pragmatic criteria. HCD is focused on developing compelling and intuitive products, grounded in knowledge about the people and contexts where an innovation will ultimately be deployed [17, 18]. This is accomplished using many techniques to understand user experiences, such as heuristic (i.e., principle-based) evaluation, cognitive walkthroughs, and co-creation sessions . Although little work has applied HCD specifically to implementation strategies, an emerging literature has begun to discuss the potential of HCD methods, processes, and frameworks for strategy development and redesign [4, 20]. Nevertheless, despite the value of overarching frameworks, specific methods are needed surrounding data collection and synthesis to drive implementation strategy development and adaptation.
Usability, the extent to which a product can be used by specified individuals to achieve specified goals in a specified context , is a key outcome of HCD processes. Usability is also a critical factor driving the adoption and delivery of new innovations, including implementation strategies [19, 22, 23]. Although potentially overlapping with perceptual implementation outcomes such as acceptability, feasibility, and appropriateness, usability can be distinguished as a characteristic of an innovation (e.g., a complex implementation strategy) and an “upstream” implementation determinant. Indeed, recent literature has described the overlap among these four constructs based on their level of contextual dependence, and identified usability as the most contextually independent . Nevertheless, a lack of studies exploring usability in implementation has impeded examination of these relationships .
A major advantage of HCD is that it emphasizes rapid and efficient information collection to assess the degree to which a product is compelling and usable. A second advantage is to identify usability problems, or aspects of an innovation and/or a demand on the user which make it unpleasant, inefficient, onerous, or impossible for the user to achieve their goals in typical usage situations . Existing methods such as concept mapping, group model building, conjoint analysis, and intervention mapping have great potential for strategy tailoring , but do not address the core issue of strategy usability.
Cognitive walkthroughs are a low-cost assessment method commonly used in HCD usability evaluations, with the potential to identify aspects of complex implementation strategies that may inhibit their use (and ultimately their effectiveness) in community contexts. Walkthroughs may be used in conjunction with other strategy selection or tailoring approaches (e.g., concept mapping). Most typically, cognitive walkthroughs are designed to simulate the cognitive behavior of users by specifically asking questions related to users’ internal cognitive models and expectations for particular scenarios and tasks , especially in the context of “first time use” (i.e., use without prior or significant exposure to a specific product, interface, or protocol) . Many variants exist , and walkthroughs may be conducted either one-on-one or in a group format. Relative to individual methods, group walkthrough procedures may minimize associated costs and capitalize on opportunities for interactions among users, thus enhancing the depth and quality of the resulting data . Despite their near ubiquity in much of the HCD literature, cognitive walk-throughs have not been applied to the evaluation of implementation strategies.
This article presents a novel, pragmatic cognitive walkthrough methodology for evaluating implementation strategy usability by identifying, organizing, and prioritizing usability issues as a component of a larger strategy redesign process. We also describe an example application of the walkthrough methodology to a single implementation strategy: post-training consultation for child and adolescent mental health clinicians working in the education sector, who had recently completed training in measurement-based care. Schools have long been the most common setting in which children and adolescents receive mental health services in the USA [31, 32]. Measurement-based care (MBC)—the systematic collection and use of patient symptom and functioning data to drive clinical decision making —is a well-supported practice for improving mental healthcare delivery [34, 35]. MBC is well aligned with the school setting, but inconsistently applied by school-based mental health clinicians [36, 37]. For clarity of presentation, the example used includes just one implementation strategy (consultation), one service sector (education sector mental health), one system level/user group (clinician service providers—a primary user group for post-training consultation), and one evaluation cycle, rather than all aspects of a multifaceted implementation initiative with multiple strategy iterations. Nevertheless, the walkthrough method is designed to be generalizable across implementation strategies, settings, system levels, and users. The methodology is intended for application by implementation researchers or practitioners who seek to ensure that the strategies they employ are easy to use and useful for relevant stakeholders. As such, it is expected to be most useful during pre-implementation phases of an initiative, prior to strategy deployment.
Methodology and case study application
Cognitive Walkthrough for Implementation Strategies (CWIS) overview
The Cognitive Walkthrough for Implementation Strategies (CWIS; pronounced “swiss”) is a streamlined walkthrough method adapted to evaluate complex, socially mediated implementation strategies in healthcare. CWIS is pragmatic  and uses a parsimonious group-based data collection format to maximize the efficiency of information gathering. As described below, the CWIS methodology includes six steps: (1) determine preconditions; (2) hierarchical task analysis; (3) task prioritization; (4) convert tasks to scenarios; (5) pragmatic group testing; and (6) usability issue identification, classification, and prioritization (Fig. 1).
Example application: post-training consultation
Below, our descriptions of the CWIS steps are followed by an application to post-training, expert consultation for clinicians. Consultation involves ongoing support from one or more experts in the innovation being implemented and the implementation process [5, 39]. Given that studies consistently document initial training alone is insufficient to effect changes in professional behavior [10, 40, 41], post-consultation has become a cornerstone implementation strategy in mental health [42, 43]. In our example, CWIS was used to evaluate a brief (2–8 weeks) consultation strategy intended for school clinicians who had recently completed a self-paced online training in MBC. All clinicians worked either for school districts or community-based organizations providing individualized mental health services in elementary, middle, or high schools in a major urban area in the Pacific Northwest of the USA. The consultation strategy included (1) weekly use of an asynchronous message board to support knowledge gains and accountability, as well as (2) live, biweekly group calls to discuss cases, solidify skills, and promote the application of MBC practices.
Step 1: Determine preconditions for the implementation strategy
Preconditions reflect the situations under which an implementation strategy is likely to be indicated or effective . In CWIS, articulation of preconditions (e.g., characteristics of the appropriate initiatives, settings, individuals, etc.) by individuals with detailed knowledge of the strategy (e.g., strategy developers or intermediaries) is necessary to ensure a valid usability test. Explicit identification of end users is a key aspect of precondition articulation, a hallmark of HCD processes , and critical if product developers are to avoid inadvertently basing designs on individuals like themselves [46, 47]. In CWIS, if preconditions for implementation strategies are not met, the scenarios or users with which the strategy may be applied in subsequent steps will be non-representative of its intended application. For instance, the strategy, “change accreditation or membership requirements”  may require clinicians or organizations who are active members of relevant professional guilds as a precondition. Context (e.g., service sector) is also relevant when articulating preconditions, as different settings may influence users’ experiences of implementation strategy usability.
When applied to post-training consultation for MBC, the research team identified individual-level preconditions that made clinicians appropriate candidates to receive the consultation strategy. These included that clinicians provided mental health services in the education sector for some or all of their professional deployment; had expressed (by way of their participation) an interest in adopting MBC practices; and had previously completed the online, self-paced training in MBC practices that the consultation model was designed to support. Detailed personas (i.e., research-based profiles of hypothetical users and use case situations ;) were developed to reflect identified target users.
Step 2: Hierarchical implementation strategy task analysis
Hierarchical task analysis includes identifying all tasks and subtasks that have independent meaning and collectively compose the implementation strategy . Tasks may be behavioral/physical (e.g., taking notes; speaking) or cognitive (e.g., prioritizing cases) [50, 51]. Cognitive tasks are groups of related mental activities directed toward a goal . These activities are often unobservable, but are frequently relevant to the decision making and problem-solving activities that are central to many implementation strategies. In CWIS, tasks, subtasks, and task sequences (including those that are behavioral and/or cognitive) are articulated by individuals with knowledge of the strategy by asking themselves a series of reflective questions: First, for each articulated larger task or task category, asking “how?” can facilitate subtask identification. Second, asking “why?” for each task elicits information about how activities fit into a wider context or grouping. Third, asking “what happens before?” and/or “what happens after?” can allow aspects of task temporality and sequencing to emerge. All tasks identified in Step 2 can be represented either as a table or as a flow chart.
Tasks in the existing MBC consultation model and tested in the CWIS study were originally informed by the core consultation functions articulated by Nadeem et al.  (including continued training, problem-solving, engagement, case applications, accountability, adaptation, mastery skill building, and sustainment planning). Members of the CWIS project team with expertise in clinical consultation procedures identified the tasks and subtasks in the model via an iterative and consensus-driven process that involved task generation, review, and revision. In this process, a task analysis of the protocol was completed using the three questions described above. Tasks were placed in three categories, depending on whether they related to live consultation calls, the asynchronous message board, or work between consultation sessions. A list of hierarchically organized tasks was distributed to the rest of the consultation protocol developers for review and feedback. The first author then revised the task list and distributed it a second time to confirm that all relevant tasks had been identified. A number of tasks were added or combined through this process to produce the final set of 24 unique tasks for further review and prioritization in Step 3 (Table 1).
Step 3: Task prioritization ratings
Owing to their complexity, it is rarely feasible to conduct a usability evaluation that includes the full range of tasks contained within an implementation strategy. In CWIS, tasks are prioritized for testing based on (1) the anticipated likelihood that users might encounter issues or errors when completing a task, and (2) the criticality or importance of completing the task correctly. Separate Likert-style ratings for each of these two dimensions are collected, ranging from “1” (extremely unlikely to make errors/unimportant) to “5” (extremely likely to make errors/extremely important). These ratings should be completed by individuals who have expertise in the implementation strategy, the context or population with which it will be applied, or both. Tasks are then selected and prioritized based first on importance and then on error likelihood. CWIS does not specify cutoffs for task selection, as such decisions should be made in the context of the resources and information available to the research team.
Tasks identified in Step 2 were reviewed and rated by four members of the research team with experience in post-training consultation and MBC. Mean importance/criticality and error likelihood ratings were calculated across respondents (Table 1). Across tasks, the two ratings were correlated at r = 0.71. Top-rated tasks (i.e., those with high ratings on both importance and error likelihood) were selected for testing and scenario development (see below). One highly rated task (“Log into message board”) was deprioritized since it was a fully digital process and could be readily addressed in a more traditional usability evaluation. In all, Step 3 resulted in five consultation tasks being identified for testing in the CWIS process.
Step 4: Convert top tasks to testing scenarios
Task-based, scenario-driven usability evaluations are a hallmark of HCD processes. Once the top tasks (approximately 4–6) have been identified, they need to be represented in an accessible format for presentation and testing in cognitive walkthroughs by the research team. In CWIS, tasks from Step 3 are used to develop overarching scenarios that provide important background information and contextualize the tasks. Scenarios are generally role-specific, so the target of an implementation strategy (e.g., clinicians) might be presented with a different set of scenarios and tasks than the deliverer of an implementation strategy (e.g., expert consultants). CWIS scenarios provide contextual background information on timing (e.g., “it is the first meeting of the implementation team”), information available (e.g., “you have been told by your organization that you should begin using [clinical practice]”), or objectives (e.g., “you are attempting to modify your practice to incorporate a new innovation”). Tasks are sometimes expanded or divided into more discrete subtasks at this stage. Some scenarios might contain a single subtask while other scenarios might have multiple subtasks. Regardless, each scenario presented in CWIS should include the following components to ensure clear communication to participants: (1) a brief written description of the scenario and subtasks, (2) a script for a facilitator to use when introducing each subtask, and (3) an image or visual cue that represents the scenario and can quickly communicate the subtasks’ intent.
Based on the prioritized tasks, the research team generated six scenarios for CWIS testing. These scenarios reflected common situations that users would be likely to encounter when participating in consultation. Each scenario contained 1–3 specific subtasks. Figure 2 displays an example scenario and its subtasks whereas Additional file 1 contains all scenarios and subtasks.
Step 5: Group testing with representative users
In Step 5, the testing materials (Step 4) are presented to small groups of individuals (i.e., 4–6) who represent the user characteristics identified in Step 1. CWIS’ pragmatism is driven, in part, by its efficient use of user participants. Because HCD typically relies on purposive sampling of representative users, it is common to test with as few as five to seven individuals per user group. Individuals recruited reflect primary user groups (Step 1) or the core individuals who are expected to use a strategy or product [45, 53]. The primary users of implementation strategies often include both the targets of those strategies and the implementation practitioners who deliver them. For instance, testing components of a leadership-focused implementation strategy (e.g., Leadership and Organizational Change for Implementation ;) could include representative leaders from the organizations in which the strategy is likely to be applied as well as leadership coaches from the implementation team. Regardless, it is advantageous to construct testing groups that reflect single user types to allow for targeted understanding of their needs. In addition to primary users, secondary users (i.e., individuals whose needs may be accommodated as long as they do not interfere with strategy’s ability to meet the needs of primary users) may also be specified.
CWIS sessions are led by a facilitator and involve presentation of a scenario/subtask, quantitative ratings, and open-ended discussion, with notes taken by a dedicated scribe. CWIS uses note takers instead of transcribed audio recordings to help ensure pragmatism and efficiency. First, each scenario is presented in turn to the group, followed by its specific subtasks. For each subtask, participants reflect on the activity, have an opportunity to ask clarifying questions, and then respond to three items about the extent to which they anticipate being able to (1) know what to do (i.e., discovering that the correct action is an option), (2) complete the subtask correctly (i.e., performing the correct action or response), and (3) learn that they have performed the task subtask correctly (i.e., receiving sufficient feedback to understand that they have performed the right action). They record these ratings using a 1–4 scale independently on a rating form (Additional file 2), the primary function of which is to provide participants with a concrete structure for considering each task and ultimately facilitate usability issues identification (Step 6). Next, participants sequentially provide verbal justifications or “failure/success stories,” which reveal the assumptions underlying their rating choices . Any anticipated problems that arise are noted as well as any assumptions made by the participants surrounding the strategy, its objectives, or the sequence of activities. Finally, having heard each other’s justifications for their ratings, the participants engage in additional open-ended discussion about the subtask and what might interfere with or facilitate its successful completion. During this discussion, note takers attend specifically to additional comments about usability issues for subsequent classification and prioritization.
At the conclusion of a CWIS session, participants complete a quantitative measure designed to assess the overall usability of the strategy. For CWIS, our research team adapted the widely used 10-item System Usability Scale [55, 56]. The resulting Implementation Strategy Usability Scale (ISUS; Additional file 3) is CWIS’s default instrument for assessing overall usability and efficiently comparing usability across different strategies or iterations of the same strategy.
Potential primary users included both clinicians and MBC expert consultants (Step 1), but only clinicians were selected for testing given the modest goals of the CWIS pilot and because the deliverers of the consultation protocol (i.e., expert consultants) were already directly involved in its development. CWIS participants (n = 10) were active mental health clinicians who primarily provided services in K-12 education settings and had completed a self-paced, online MBC training (see Step 1: Preconditions). Participating clinicians came from a variety of organizations (i.e., multiple school districts and school-serving agencies), were 90% female, and had been in their roles for 2–18 years. Table 2 displays all participant demographics. Human subjects approval was obtained by the University of Washington Institutional Review Board, and all participants completed standard consent processes.
A facilitator (first author) conducted two CWIS sessions (including 4 and 6 clinicians, respectively), lasting approximately 90 min each, and guided each group through the six scenarios and eleven associated subtasks (Additional file 1). As detailed above, users were asked to rate each task based on their personal anticipated likelihood of success discovering the correct action, likelihood of performing that action, and likelihood that they would know about the success or failure of their action. Average success ratings for each subtask were calculated as the mean of all questions and user ratings and incorporated into a matrix cross-walking the team’s original importance ratings with the success ratings generated by users.
Next, clinicians provided open-ended rating justifications and engaged in group discussion, including describing why some subtasks were considered more difficult than others and what aspects of subtasks they found particularly confusing or difficult. Discussion was recorded by the note taker for subsequent synthesis by the research team. Note takers were project staff trained by the investigators to capture qualitative explanations given by providers for their ratings. These were recorded in as much detail as possible (often verbatim) using a structured guide that facilitated tracking which task was presented, which participant was speaking, and their specific comments. Following the walkthrough sessions, users completed the ISUS in reference to all aspects of the consultation protocol to which they had been exposed.
Step 6: Usability issue identification, prioritization, and classification
Within CWIS, usability issues are identified, classified, and prioritized using a structured method to ensure consistency across applications. All usability issues are identified by the research team, based on the results of Step 5 testing.
Identification and prioritization
In CWIS, identification of usability issues occurs in accordance with recent guidance articulated by the University of Washington ALACRITY Center [4, 57] for articulating usability issues for complex psychosocial interventions and strategies. Specifically, usability issues should include (1) a brief description (i.e., a concise summary of the issue, focused on how the strategy fell short of meeting the user’s needs and its consequences), (2) severity information (i.e., how problematic or dangerous the issue is likely to be on a scale ranging from 0 [“catastrophic or dangerous”] to 4 [“subtle problem”], adapted from Dumas and Redish ), (3) information about scope (i.e., the number of users and/or number of components affected by an issue), and (4) indicators of its level of complexity (i.e., how straightforward it is to address [low, medium, high]). The consequences of usability issues (a component of issue descriptions) may either be explicitly stated by participants or inferred during coding. Determinations about severity and scope are informed by the extent to which usability issues were known to impact participants’ subtask success ratings (Step 5). Usability issues that are severe and broad in scope are typically the most important to address. Those that are also low in complexity may be able to be prioritized for the most immediate changes to the strategy because they may be the easiest to immediately improve .
In CWIS, all identified usability problems are classified by the research team using a consensus coding approach and a framework adapted from the enhanced cognitive walkthrough . The first category includes issues associated with the user (U), meaning that the problem is related to the experience or knowledge a user has been able to access (e.g., insufficient information to complete a task). Second, an implementation strategy usability problem may be due to information being hidden (H) or insufficiently explicit about the availability of a function or its proper use. Third, issues can arise due to sequencing or timing (ST), which relates to when implementation strategy functions have to be performed in an unnatural sequence or at a discrete time that is problematic. Fourth, problems with strategy feedback (F) are those where the strategy gives unclear indications about what a user is doing or needs to do. Finally, cognitive or social (CS) issues are due to excessive demands placed on a user’s cognitive resources or social interactions. Usability issue classification is critical because it facilitates aggregation of data across projects and allows for more direct links between usability problems and potential implementation strategy redesign solutions. For instance, user issues may necessitate reconsideration of the target users or preconditions (e.g., amount of training/experience) whereas cognitive or social issues may suggest the need for simplification of a strategy component or enhanced supports (e.g., job aids) to decrease cognitive burden. Categories are not mutually exclusive, so a single usability issue may be classified into multiple categories as appropriate.
Using a conventional content analysis approach , the ratings and notes from each CWIS session were independently reviewed and analyzed by members of the research team who identified usability issues by independently identifying issues and then meeting to compare their coding, refine the list, and arrive at consensus judgments . No a priori codes were specified, as all codes were derived from the data. Next, coders independently rated issue severity and complexity. Outcomes of the application of CWIS Step 6 to the MBC consultation protocol are presented in the results below.
Figure 3 presents a matrix of all subtasks rated by the participants, color coded based on their anticipated success (1—very small chance of success [red], 2—small chance of success [orange], 3—probable chance of success [yellow], and 4—very good chance of success [green]). The percentage of users who felt very confident in their anticipated success is highlighted in the rightmost column. Overall, ratings indicated substantial variability in anticipated success across participants and subtasks. Participants tended to rate their success knowing what to do (mean = 3.6) and learning that they did it successfully (mean = 3.53) higher than their success actually completing the subtask (mean = 3.29). Regarding specific subtasks, linking client intervention goals to an outcome monitoring plan received the lowest ratings.
Overall strategy usability
ISUS ratings (scale 0–100) ranged from 57.5 to 82.5, with a mean of 71.3 (median = 72.5; SD = 10.6). Mean ratings for each CWIS group were similar (73.1 vs. 70.0). Based on descriptors developed for the original System Usability Scale , this range corresponds to descriptors between “low marginal” (1st quartile) and “excellent” (4th quartile) . The mean was in the lower end of the “acceptable” range.
Consensus coding yielded 21 distinct usability problems. Usability issues included potential misalignment between consultation and clinical service timelines as well as the need for tools to support real-time decision-making during consultation. Table 3 displays each of these usability problems, organized based on average severity scores completed by three members of the research team. Additional file 4 displays example excerpts from testing that supported each usability issue. Overall, usability issues ranged from the most severe at 1.33 for Focus on barriers detracts from case presentation to 4.00 for Unfamiliar language in consultation model. Usability issues rated as the most severe (1.00–2.00) demonstrated a full range of complexity levels, but were primarily high or medium complexity and, with one exception, were identified by five or more participating users. Overall, the scope of the usability issues ranged from those that affected a single user (e.g., Case presentations exceed time allotted) to those that were identified by seven separate users (Unprepared to articulate monitoring targets). Application of the adapted enhanced cognitive walkthrough categorization approach  indicated that approximately half of the issues could be classified within multiple categories. Nine issues were determined to be related to the user, three issues were related to information being hidden, two issues were connected to sequencing or timing, three issues were due to insufficient feedback, and eleven issues reflected excessive cognitive or social demands.
Complex and multifaceted implementation strategies are increasingly common in implementation science. The extent to which these strategies can be successfully applied by specified users to achieve their goals is a critical consideration when making decisions about implementation strategy selection and adaptation. Usability assessment has the potential to provide a key input into strategy adoption and tailoring decisions. CWIS is the first methodology developed to explicitly assess the usability of implementation strategies in healthcare.
CWIS findings for post-training consultation
In the current example, the results of the ISUS indicated that clinician-rated usability of the original consultation protocol was at the low end of the “acceptable” range (based on existing SUS norms) and would benefit from some revision . Although potentially workable for many users, this finding suggests that revisions to the strategy are likely indicated to improve ease of use and usefulness for its identified set of clinician primary users.
In addition to ratings of overall usability, CWIS walkthrough sessions revealed 21 discrete usability issues. Collectively, these issues explain the ISUS quantitative usability data and provide specific direction for usability enhancements. Most usability issues related either to whether the protocol had inaccurate expectations surrounding clinician preparation in consultation-related skills (e.g., Unprepared to identify solutions to barriers), various opportunities for consultation to be disrupted by participants who needed to discuss implementation barriers (e.g., Digressions derail barrier problem solving and engagement), the protocol’s built-in assumptions about service delivery timelines (e.g., Rapid assessment misaligned with available time), or digital technology-related issues (e.g., Inadequate on-site technology).
Implications for strategy redesign
Much of the utility of the CWIS methodology comes from its potential to inform user-centered redesign of implementation strategies to enhance usability. Although it is beyond the scope of this paper to articulate the full strategy adaptation process (where CWIS served as a key input), the results of the current example application indicated some clear redesign directions to improve the alignment of the consultation protocol with clinician users. Focusing redesign on the highest priority problems avoids excessive changes that may not be critical. As can be seen in Table 4, which links abbreviated descriptions of the usability problems (articulated by the research team) to redesign decisions, CWIS resulted in changes to the consultation strategy in multiple ways that were unanticipated at the outset. The highest-rated usability issues (e.g., Focus on barriers detracts from case presentation [U, F]; Inadequate on-site technology [CS]) were addressed through modifications to various consultation elements, and most redesign decisions addressed multiple usability issues. For example, the project team streamlined the consultation call time (reduced to no more than 50 min) and designed brief make-up sessions (15 min) to address how Regular calls were incompatible with time/availability (CS) (length and duration). Assignment of a problem type classification to each usability issue further facilitated redesign. For instance, two of the three highest severity problems were categorized as issues related to the implementation strategy not being aligned with the Users and their knowledge base (Focus on barriers detracts from case presentation and Unprepared to identify solutions to barriers). This indicated that additional specific supports surrounding consultation-relevant skills such as case presentations and problem-solving implementation barriers were important to improving overall usability. Modifications to address these issues included developing supplemental MBC resources, providing clear examples, and creating multiple opportunities to ask questions and get support (including asynchronously).
Limitations and future directions
The current application of CWIS has a number of limitations. First, our example application only involved applying CWIS to a single user group (clinician recipients of the strategy) and participant diversity was limited (e.g., no clinicians identified as being Black). Future applications may include more diverse professionals, including those who deliver implementation strategies (e.g., expert consultants, especially those unaffiliated with the study team) as well as other types of service providers and sectors (e.g., physicians in primary care). Second, the methodology was only applied to a single implementation strategy targeting the individual clinician level. Nevertheless, most implementation efforts include multiple strategies. CWIS is intended to be applicable across strategies and levels and could be similarly useful for assessing multifaceted strategies, such as organizationally focused approaches targeting system leaders  or complex strategies designed to simultaneously influence multiple stakeholder groups. Such applications will help to build on and broaden the preliminary evidence for CWIS generated in the current study. Third, although it is designed to approximate the hands-on experience of using an implementation strategy, CWIS still involves some level of abstraction given that participating users do not actually complete the tasks on which they are reporting. This is a common tradeoff in cognitive walkthroughs and may be one reason why walkthrough methods sometimes over-identify usability problems . Future work could determine whether group-based walkthroughs produce usability results that are comparable to more—or less—intensive (and expensive) types of testing such as walkthroughs with individuals . Fourth, the current study presented an example application of CWIS to demonstrate its utility, but the results described do not reflect a direct evaluation of the acceptability, feasibility, or impact of the approach relative to a comparison condition. Fifth, while CWIS is intended to be pragmatic and efficient, the extent to which all of its activities (e.g., qualitative content analysis) are feasible for real-world implementation practitioners is uncertain and should be a focus of future inquiry. In the current study, CWIS was delivered by a research team that was external to the implementing organizations. While the CWIS sessions themselves (Step 5) are relatively brief, there is inevitable preparation required (Steps 1–4) and, following the sessions, synthesis of the resulting information (Step 6), which could impact feasibility. Nevertheless, usability evaluation activities such as these are commonly applied in industry and often completed rapidly by small teams. Finally, pragmatic methods and instruments should ideally be sensitive to change , but the current study only involved applying CWIS at one point in the iterative development of the consultation strategy. Additional research should evaluate CWIS’s change sensitivity and ability to identify whether redesign decisions result in new usability issues or unanticipated barriers.
Despite growing interest in implementation strategy selection and tailoring processes, no methods exist to evaluate usability and ensure that strategies can be successfully applied by well-specified users in their contexts of use. The current study provides preliminary evidence for the utility of CWIS to assess strategy usability and generate a blueprint for redesign. Future work should evaluate the extent to which usability, as measured by CWIS, is predictive of the fidelity with which implementation strategies (e.g., training, consultation, leadership supports) are delivered as well as their impact on implementation and health service outcomes.
Availability of data and materials
Please contact the lead author for more information.
Advanced Laboratory for Accelerating the Reach and Impact of Treatments for Youth and Adults with Mental Illness
Cognitive Walkthrough for Implementation Strategies
Implementation Strategy Usability Scale
Balas EA, Boren SA. Managing clinical knowledge for health care improvement. Yearb Med Inform. 2000;(1):65–70. https://doi.org/10.1055/s-0038-1637943.
Eccles MP, Mittman BS. Welcome to implementation science. Implement Sci. 2006;1(1):1. https://doi.org/10.1186/1748-5908-1-1.
Powell BJ, McMillen JC, Proctor EK, Carpenter CR, Griffey RT, Bunger AC, et al. A compilation of strategies for implementing clinical innovations in health and mental health. Med Care Res Rev. 2012;69(2):123–57. https://doi.org/10.1177/1077558711430690.
Lyon AR, Munson SA, Renn BN, Atkins DC, Pullmann MD, Friedman E, et al. Use of human-centered design to improve implementation of evidence-based psychotherapies in low-resource communities: protocol for studies applying a framework to assess usability. JMIR Res Protoc. 2019;8(10):e14990. https://doi.org/10.2196/14990.
Powell BJ, Waltz TJ, Chinman MJ, Damschroder LJ, Smith JL, Matthieu MM, et al. A refined compilation of implementation strategies: results from the Expert Recommendations for Implementing Change (ERIC) project. Implement Sci. 2015;10(1):21. https://doi.org/10.1186/s13012-015-0209-1.
Proctor EK, Powell BJ, McMillen JC. Implementation strategies: recommendations for specifying and reporting. Implement Sci. 2013;8(1):139. https://doi.org/10.1186/1748-5908-8-139.
Aarons GA, Powell BJ, Beidas RS, Lewis CC, McMillen JC, Proctor EK, et al. Methods to improve the selection and tailoring of implementation strategies. J Behav Health Serv Res. 2017;44(2). https://doi.org/10.1007/s11414-015-9475-6.
Glisson C, Schoenwald SK. The ARC organizational and community intervention strategy for implementing evidence-based children’s mental health treatments. Ment Health Serv Res. 2005;7(4):243–59. https://doi.org/10.1007/s11020-005-7456-1.
Kilbourne AM, Neumann MS, Pincus HA, Bauer MS, Stall R. Implementing evidence-based interventions in health care: application of the replicating effective programs framework. Implement Sci. 2007;2(1):42. https://doi.org/10.1186/1748-5908-2-42.
Beidas RS, Kendall PC. Training therapists in evidence-based practice: a critical review of studies from a systems-contextual perspective. Clin Psychol Sci Pract. 2010;17(1):1–30. https://doi.org/10.1111/j.1468-2850.2009.01187.x.
Oxman AD, Thomson MA, Davis DA, Haynes RB. No magic bullets: a systematic review of 102 trials of interventions to improve professional practice. Can Med Assoc J. 1995;152:1423–31.
Squires JE, Sullivan K, Eccles MP, Worswick J, Grimshaw JM. Are multifaceted interventions more effective than single-component interventions in changing health-care professionals’ behaviours? An overview of systematic reviews. Implement Sci. 2014;9(1):152. https://doi.org/10.1186/s13012-014-0152-6.
Baker R, Camosso-Stefinovic J, Gillies C, Shaw EJ, Cheater F, Flottorp S, et al. Tailored interventions to overcome identified barriers to change: effects on professional practice and health care outcomes. Cochrane Database Syst Rev. 2010;3. https://doi.org/10.1002/14651858.CD005470.pub2.
Wensing M, Bosch MC, Grol R. Selecting, tailoring, and implementing knowledge translation interventions. In: Knowledge Translation in Health Care: Moving from evidence to practice [Internet]. United Kingdom: Wiley-Blackwell; 2009;94–113. doi: https://doi.org/10.1503/cmaj.081335, 182, 2.
Glasgow RE. What does it mean to be pragmatic? Pragmatic methods, measures, and models to facilitate research translation. Health Educ Behav. 2013;40(3):257–65. https://doi.org/10.1177/1090198113486805.
Stanick CF, Halko HM, Dorsey CN, Weiner BJ, Powell BJ, Palinkas LA, et al. Operationalizing the ‘pragmatic’ measures construct using a stakeholder feedback and a multi-method approach. BMC Health Serv Res. 2018;18(1):882. https://doi.org/10.1186/s12913-018-3709-2.
Courage C, Baxter K. Understanding your users: a practical guide to user requirements methods, tools, and techniques: Gulf Professional Publishing; 2005. https://doi.org/10.1016/B978-1-55860-935-8.X5029-5.
Norman DA, Draper SW. User centered system design; new perspectives on human-computer interaction. USA: L. Erlbaum Associates Inc.; 1986. https://doi.org/10.1201/b15703.
Dopp AR, Parisi KE, Munson SA, Lyon AR. A glossary of user-centered design strategies for implementation experts. Transl Behav Med. 2019;9(6):1057–64. https://doi.org/10.1093/tbm/iby119.
Mohr DC, Lyon AR, Lattie EG, Reddy M, Schueller SM. Accelerating digital mental health research from early design and creation to successful implementation and sustainment. J Med Internet Res. 2017;19(5):e153. https://doi.org/10.2196/jmir.7725.
International Standards Organization. Part 11: Guidance on usability. In: Ergonomic requirements for office work with visual display terminals (VDTs). 1st ed; 1998. https://doi.org/10.3403/01879403.
Eisman AB, Kilbourne AM, Greene D, Walton M, Cunningham R. The user-program interaction: How teacher experience shapes the relationship between intervention packaging and fidelity to a state-adopted health curriculum. Prev Sci. 2020;21(6):1–10. https://doi.org/10.1007/s11121-020-01120-8.
Lyon AR, Bruns EJ. User-centered redesign of evidence-based psychosocial interventions to enhance implementation—hospitable soil or better seeds? JAMA Psychiatry. 2019;76(1):3–4. https://doi.org/10.1001/jamapsychiatry.2018.3060.
Lyon AR, Brewer SK, Arean PA. Leveraging human-centered design to implement modern psychological science: Return on an early investment. Am Psychol. 2020;75(8):1067–79 https://doi.org/10.1037/amp0000652.
Lyon AR, Pullmann MD, Jacobson J, Osterhage K, Al Achkar M, Renn BN, et al. Assessing the usability of complex psychosocial interventions: The Intervention Usability Scale. Implement Res Pract. 2021;2:263348952098782. https://doi.org/10.1177/2633489520987828.
Lavery D, Cockton G, Atkinson MP. Comparison of evaluation methods using structured usability problem reports. Behav Inf Technol. 1997;16(4–5):246–66. https://doi.org/10.1080/014492997119824.
Mahatody T, Sagar M, Kolski C. State of the art on the cognitive walkthrough method, its variants and evolutions. Int J Human–Computer Interact. 2010;26(8):741–85. https://doi.org/10.1080/10447311003781409.
Rieman J, Franzke M, Redmiles D. Usability evaluation with the cognitive walkthrough. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Denver, CO; 1995. p. 387–8. https://doi.org/10.1145/223355.223735.
Bligard L-O, Osvalder A-L. Enhanced cognitive walkthrough: development of the cognitive walkthrough method to better predict, identify, and present usability problems. Adv Hum-Comp Int. 2013;2013:1–17. https://doi.org/10.1155/2013/931698.
Gutwin C, Greenberg S. The mechanics of collaboration: developing low cost usability evaluation methods for shared workspaces. In: Proceedings of the 9th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises. Washington, DC, USA: IEEE Computer Society; 2000. p. 98–103. https://doi.org/10.1109/ENABL.2000.883711.
Duong MT, Bruns EJ, Lee K, Cox S, Coifman J, Mayworm A, et al. Rates of mental health service utilization by children and adolescents in schools and other common service settings: a systematic review and meta-analysis. Adm Policy Ment Health Ment Health Serv Res. 2020;48(3):420–39. https://doi.org/10.1007/s10488-020-01080-9.
Farmer EMZ, Burns BJ, Phillips SD, Angold A, Costello EJ. Pathways into and through mental health services for children and adolescents. Psychiatr Serv. 2003;54(1):60–6. https://doi.org/10.1175/appi.ps.54.1.60.
Scott K, Lewis CC. Using measurement-based care to enhance any treatment. Cogn Behav Pract. 2015;22(1):49–59. https://doi.org/10.1016/j.cbpra.2014.01.010.
Fortney JC, Unützer J, Wrenn G, Pyne JM, Smith GR, Schoenbaum M, et al. A tipping point for measurement-based care. Psychiatr Serv. 2017;68(2):179–88. https://doi.org/10.1176/appi.ps.201500439.
Lewis CC, Boyd M, Puspitasari A, Navarro E, Howard J, Kassab H, et al. Implementing measurement-based care in behavioral health: a review. JAMA Psychiatry. 2019;76(3):324–35. https://doi.org/10.1001/jamapsychiatry.2018.3329.
Stephan SH, Sugai G, Lever N, Connors E. Strategies for integrating mental health into schools via a multitiered system of support. Child Adolesc Psychiatr Clin N Am. 2015;24(2):211–31. https://doi.org/10.1016/j.chc.2014.12.002.
Lyon AR, Lewis CC, Boyd MR, Hendrix E, Liu F. Capabilities and characteristics of digital measurement feedback systems: results from a comprehensive review. Adm Policy Ment Health Ment Health Serv Res. 2016;43(3):441–66. https://doi.org/10.1007/s10488-016-0719-4.
Glasgow RE, Riley WT. Pragmatic measures: what they are and why we need them. Am J Prev Med. 2013;45(2):237–43. https://doi.org/10.1016/j.amepre.2013.03.010.
Nadeem E, Gleacher A, Beidas RS. Consultation as an implementation strategy for evidence-based practices across multiple contexts: Unpacking the black box. Adm Policy Ment Health Ment Health Serv Res. 2013;40(6):439–50. https://doi.org/10.1007/s10488-013-0502-8.
Herschell AD, Kolko DJ, Baumann BL, Davis AC. The role of therapist training in the implementation of psychosocial treatments: a review and critique with recommendations. Clin Psychol Rev. 2010;30(4):448–66. https://doi.org/10.1016/j.cpr.2010.02.005.
Lyon AR, Charlesworth-Attie S, Vander Stoep A, McCauley E. Modular psychotherapy for youth with internalizing problems: Implementation with therapists in school-based health centers. Sch Psychol Rev. 2011;40(4):569–81. https://doi.org/10.1080/02796015.2011.12087530.
Edmunds JM, Beidas RS, Kendall PC. Dissemination and implementation of evidence–based practices: training and consultation as implementation strategies. Clin Psychol Sci Pract. 2013;20(2):152–65. https://doi.org/10.1111/cpsp.12031.
Lyon AR, Pullmann MD, Walker SC, D’Angelo G. Community-sourced intervention programs: review of submissions in response to a statewide call for “promising practices”. Adm Policy Ment Health Ment Health Serv Res. 2017;44(1):16–28. https://doi.org/10.1007/s10488-015-0650-0.
Lewis CC, Klasnja P, Powell BJ, Lyon AR, Tuzzio L, Jones S, et al. From classification to causality: advancing understanding of mechanisms of change in implementation science. Front Public Health. 2018;6:136. https://doi.org/10.3389/fpubh.2018.00136.
Cooper A, Reimann R, Cronin D. About Face 3: the essentials of interaction design. 3rd edition. Indianapolis, IN: Wiley; 2007.
Cooper A. The inmates are running the asylum. Inc.: Macmillan Publishing Co.; 1999. https://doi.org/10.1007/978-3-322-99786-9_1.
Kujala S, Mäntylä M. How effective are user studies? In: McDonald S, Waern Y, Cockton G, editors. People and Computers XIV — Usability or Else! London: Springer; 2000. p. 61–71. https://doi.org/10.1007/978-1-4471-0515-2_5.
Grudin J, Pruitt J. Personas, participatory design and product development: an infrastructure for engagement. In: Binder J, Gregory J, Wagner I, editors. . Palo Alto, CA: Computer Professionals for Social Responsibility; 2002. p. 144–52.
Shepherd A. HTA as a framework for task analysis. Ergonomics. 1989;41(11):1537–52. https://doi.org/10.1080/001401398186063.
Jonassen DH, Tessmer M, Hannum WH. Task analysis methods for instructional design: Routledge; 1998. https://doi.org/10.4324/9781410602657.
Wei J, Salvendy G. The cognitive task analysis methods for job and task design: review and reappraisal. Behav Inf Technol. 2004;23(4):273–99. https://doi.org/10.1080/01449290410001673036.
Klein G, Militello L. Some guidelines for conducting a cognitive task analysis. Adv Hum Perform Cogn Eng Res. 1998;1:161–99. https://doi.org/10.1016/S1479-3601(01)01006-2.
Lyon AR, Koerner K. User-centered design for psychosocial intervention development and implementation. Clin Psychol Sci Pract. 2016;23(2):180–200. https://doi.org/10.1111/cpsp.12154.
Aarons GA, Ehrhart MG, Farahnak LR, Hurlburt MS. Leadership and organizational change for implementation (LOCI): a randomized mixed method pilot study of a leadership and organization development intervention for evidence-based practice implementation. Implement Sci. 2015;10(1):11. https://doi.org/10.1186/s13012-014-0192-y.
Brooke J. SUS: A quick and dirty usability scale. In: Jordan PW, Thomas B, Weerdmeester BA, McClelland IL, editors. Usability evaluation in industry. London, England: Taylor and Francis; 1996. https://doi.org/10.1201/9781498710411-35.
Sauro J. A practical guide to the system usability scale: background, benchmarks & best practices: Measuring Usability LLC; 2011.
Lyon AR. Usability testing and reporting at the UW ALACRITY Center: Association for Behavioral and Cognitive Therapies Meeting; 2020.
Dumas JS, Redish J. A practical guide to usability testing: Intellect Books; 1999. https://doi.org/10.5555/600280.
Albert W, Dixon E. Is this what you expected? The use of expectation measures in usability testing. In: Proceedings of the Usability Professionals Association 2003 Conference, Scottsdale, AZ; 2003.
Hsieh H-F, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15(9):1277–88. https://doi.org/10.1177/1049732305276687.
Hill CE, Knox S, Thompson BJ, Williams EN, Hess SA, Ladany N. Consensual qualitative research: an update. J Couns Psychol. 2005;52(2):196–205. https://doi.org/10.1037/0022-0126.96.36.199.
Kortum PT, Bangor A. Usability ratings for everyday products measured with the System Usability Scale. Int J Human–Computer Interact. 2013;29(2):67–76. https://doi.org/10.1080/10447318.2012.681221.
U.S. Department of Health and Human Services. Chapter 18 Usability testing: Use cognitive walkthroughs cautiously. In: Web Design and Usability Guidelines. 2006. https://s3.amazonaws.com/saylordotorg-resources/wwwresources/site/wp-content/uploads/2012/09/SAYLOR.ORG-CS412-Chapter-18-Usability-Testing.pdf. Accessed 13 July 2021.
Lyon AR, Koerner K, Chung J. Usability Evaluation for Evidence-Based Psychosocial Interventions (USE-EBPI): a methodology for assessing complex intervention implementability. Implement Res Pract. 2020;1:263348952093292. https://doi.org/10.1177/2633489520932924.
Thank you to Ethan Hendrix for supporting data collection for this project.
This publication was supported by grants R34MH109605 and P50MH115837, awarded by the National Institute of Mental Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Ethics approval and consent to participate
This project was approved by the University of Washington Institutional Review Board (IRB).
Consent for publication
All authors declare that they have no completing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Lyon, A.R., Coifman, J., Cook, H. et al. The Cognitive Walkthrough for Implementation Strategies (CWIS): a pragmatic method for assessing implementation strategy usability. Implement Sci Commun 2, 78 (2021). https://doi.org/10.1186/s43058-021-00183-0
- Implementation strategies
- Human-centered design
- Cognitive walkthrough