Sustaining the collaborative chronic care model in outpatient mental health: a matrixed multiple case study

The sustainability of evidence-based practices (EBPs) over time is crucial to maximize the public health impact of EBPs implemented into routine care. Implementation evaluators focus on sustainability as a central implementation outcome, and funders of implementation efforts seek sustained long-term returns on their investment. Furthermore, practitioners and leadership at implementation sites face the task of sustaining an EBP’s usage even after implementation funding, support, and associated evaluation efforts conclude. The circumstances and influences contributing to EBP sustainability are therefore of high interest to the field of implementation science.

Sustainability depends on the specific EBP being implemented, the individuals undergoing the implementation, the contexts in which the implementation takes place, and the facilitation of (i.e., support for) the implementation. Hence, universal conditions that invariably lead to sustainability are challenging to establish. Even if a set of conditions could be identified as being associated with high sustainability “on average,” its usefulness is questionable when most real-world implementation contexts may deviate from “average” on key implementation-relevant metrics.

Thus, when seeking a better understanding of EBP sustainability, there is a critical need for methods that examine the ways in which sustainability varies in diverse contexts. One such method is Matrixed Multiple Case Study (MMCS) [1], which is beginning to be applied in implementation research to identify factors related to implementation [2‐5]. MMCS capitalizes on the many contextual variations and heterogeneous outcomes that are expected when an EBP is implemented across multiple sites. Specifically, MMCS provides a formalized sequence of steps for cross-site analysis by arranging data into an array of matrices, which are sorted and filtered to test for expected factors and identify less expected factors influencing an implementation outcome of interest.

Although the MMCS represents a promising method for systematically exploring the “black box” of the ways in which implementation is more or less successful, it has not yet been applied to investigate the long-term sustainability of implemented EBPs. Therefore, we applied MMCS to identify factors related to the sustainability of the evidence-based Collaborative Chronic Care Model (CCM), previously implemented using implementation facilitation [6‐8], at nine VA medical centers’ outpatient general mental health clinics. An earlier interview-based investigation of CCM provider perspectives had identified key determinants of CCM sustainability at the sites, yet characteristics related to the ways in which CCM sustainability differed at the sites are still not well understood. For this reason, our objective was to apply MMCS to examine the interview data to determine factors associated with CCM sustainability at each site.

For the nine sites included in this project, we found the degree of CCM sustainability to be split evenly across the sites—three high-, three medium-, and three low-sustainability. Twenty-five total influencing factors were identified under the i-PARIHS domains of Innovation (6), Recipients (6), Context (8), and Facilitation (5). Table 2 shows these identified influencing factors by domain. Figure 1 shows 11 influencing factors that were identified for at least two sites within a group of high/medium/low sustainability sites—e.g., the factor “consistent and strong internal facilitator” is shown as being present at high sustainability sites with an enabling influence on sustainability, because it was identified as such at two or more of the high sustainability sites. Of these 11 influencing factors, four were identified only for sites with high CCM sustainability and two were identified only for sites with medium or low CCM sustainability.

In this project, we examined factors influencing the sustainability of CCM-aligned care practices at general mental health clinics within nine VA medical centers that previously participated in a CCM implementation trial. Guided by the core CCM elements and i-PARIHS domains, we conducted and analyzed CCM provider interviews. Using MMCS, we found CCM sustainability to be split evenly across the nine sites (three high, three medium, and three low), and that sustainability may be related most strongly to provider collaboration, knowledge retention during staff/leadership transitions, and availability of skilled internal facilitators.

Our previous qualitative analysis of these interview data suggested that in order to achieve sustainability, it is important to establish appropriate infrastructure, organizational readiness, and mental health service- or department-wide coordination for CCM implementation [10]. The findings from the current project augment these previous findings by highlighting the specific factors associated with higher and lower CCM sustainability across the project sites. This additional knowledge provides two important insights into what CCM implementation efforts should prioritize with regard to the previously recommended appropriate infrastructure, readiness, and coordination. First, for knowledge retention and coordination during personnel changes (including any changes in internal facilitators through and following implementation), care processes and their specific procedures should be established and documented in order to bring new personnel up to speed on those care processes. Management sciences, as applied to health care and other fields, suggest that such organizational knowledge retention can be maximized when there are (i) structures set up to formally recognize/praise staff when they share key knowledge, (ii) succession plans to be applied in the event of staff turnover, (iii) opportunities for mentoring and shadowing, and (iv) after action reviews of conducted care processes, which allow staff to learn about and shape the processes themselves [30‐33]. Future CCM implementation efforts may thus benefit from enacting these suggestions alongside establishing and documenting CCM-based care processes and associated procedures.

Second, efforts to implement CCM-aligned practices into routine care should account for the extent to which sites’ more fundamental operational needs are met or being addressed. That information can be used to appropriately scope the plan, expectations, and timeline for implementation. For instance, ongoing critical staffing shortages or high turnover [34] at a site are unlikely to be resolved through a few months of CCM implementation. In fact, in that situation, it is possible that CCM implementation efforts could lead to reduced team effectiveness in the short term, given the effort required to establish more collaborative and coordinated care processes [35]. Should CCM implementation move forward at a given site, implementation goals ought to be set on making progress in realms that are within the implementation effort’s control (e.g., designing CCM-aligned practices that take staffing challenges into consideration) [36, 37] rather than on factors outside of the effort’s control (e.g., staffing shortages). As healthcare systems determine how to deploy support (e.g., facilitators) to sites for CCM implementation, they would benefit from considering whether it is primarily CCM expertise that the site needs at the moment, or more foundational organizational resources (e.g., mental health staffing, clinical space, leadership enhancement) [38] to first reach an operational state that can most benefit from CCM implementation efforts at a later point in time. There is growing consensus across the field that the readiness of a healthcare organization to innovate is a prerequisite to successful innovation (e.g., CCM implementation) regardless of the specific innovation [39, 40]. Several promising strategies specifically target these organizational considerations for implementing evidence-based practices (e.g., [41, 42]). Further, recent works have begun to more clearly delineate leadership-related, climate-related, and other contextual factors that contribute to organizations’ innovation readiness [43], which can inform healthcare systems’ future decisions regarding preparatory work leading to, and timing of, CCM implementation at their sites.

These considerations informed by MMCS may have useful implications for implementation strategy selection and tailoring for future CCM implementation efforts, especially in delineating the target level (e.g., system, organizational, clinic, individual) and timeline of implementation strategies to be deployed. For instance, of the three factors found to most notably trend with CCM sustainability, Collaborativeness and teamwork may be strengthened through shorter-term team-building interventions at the organizational and/or clinic levels [38], Turnover of clinic staff and leadership may be mitigated by aiming for longer-term culture/climate change at the system and/or organizational levels [44‐46], and Having a consistent and strong internal facilitator may be ensured more immediately by selecting an individual with fitting expertise/characteristics to serve in the role [15] and imparting innovation/facilitation knowledge to them [47]. Which of these factors to focus on, and through what specific strategies, can be decided in partnership with an implementation site—for instance, candidate strategies can be identified based on ones that literature points to for addressing these factors [48], systematic selection of the strategies to move forward can happen with close input from site personnel [49], and explicit further specification of those strategies [50] can also happen in collaboration with site personnel to amply account for site-specific contexts [51].

As is common for implementation projects, the findings of this project are highly context-dependent. It involves the implementation of a specific evidence-based practice (the CCM) using a specific implementation strategy (implementation facilitation) at specific sites (BHIP teams within general mental health clinics at nine VA medical centers). For such context-dependent findings to be transferable [52, 53] to meaningfully inform future implementation efforts, sources of variation in the findings and how the findings were reached must be documented and traceable. This means being explicit about each step and decision that led up to cross-site analysis, as MMCS encourages, so that future implementation efforts can accurately view and consider why and how findings might be transferable to their own work. For instance, beyond the finding that Turnover of clinic staff and leadership was a factor present at most of the examined sites, MMCS’ traceable documentation of qualitative data associated with this factor at high sustainability sites also allowed highlighting the perception that CCM implementation is contributing to mitigating turnover of providers in the clinic over time, which may be a crucial piece of information that fuels future CCM implementation efforts.

Furthermore, to compare findings and interpretations across projects, consistent procedures for setting up and conducting these multi-site investigations are indispensable [54‐56]. Although many projects involve multiple sites and assess variations across the sites, it is less common to have clearly delineated protocols for conducting such assessments. MMCS is meant to target this very gap, by offering a formalized sequence of steps that prompt specification of analytical procedures and decisions that are often interpretive and left less specified. MMCS uses a concrete data structure (the matrix) to traceably organize information and knowledge gained from a project, and the matrix can accommodate various data sources and conceptual groundings (e.g., guiding theories, models, and frameworks) that may differ from project to project – for instance, although our application of MMCS aligned to i-PARIHS, other projects applying MMCS [2, 5] use different conceptual guides (e.g., Consolidated Framework for Implementation Research [57], Theoretical Domains Framework [58]). Therefore, as more projects align to the MMCS steps [1] to identify factors related to implementation and sustainability, better comparisons, consolidations, and transfers of knowledge between projects may become possible.

This project has several limitations. First, the high, medium, and low sustainability assigned to the sites were based on the sites’ CCM sustainability relative to one another, rather than based on an external metric of sustainability. As measures of sustainability such as the Program Sustainability Assessment Tool [59, 60] and the Sustainment Measurement System Scale [61] become increasingly developed and tested, future projects may consider the feasibility of incorporating such measures to assess each site’s sustainability. In our case, we worked on addressing this limitation by using a consensus approach within our project team to assign sustainability levels to sites, as well as by confirming that the sites that we designated as high sustainability exhibited CCM elements that we had previously observed at the end of their participation in the original CCM implementation trial [19]. Second, we did not assign strict thresholds above/below which the counts or proportions of data regarding a factor would automatically indicate whether the factor (i) was present, somewhat present, or minimally present and (ii) had an enabling, hindering, or neutral/unclear influence on sustainability. This follows widely accepted qualitative analytical guidance that discourages characterizing findings solely based on the frequency with which a notion is mentioned by participants [62‐64], in order to prevent unsubstantiated inferences or conclusions. We sought to address this limitation in two ways: We carefully documented the project team’s rationale for each consensus reached, and we reviewed all consensuses reached in their entirety to ensure that any two factors with the same designation (e.g., “minimally present”) do not have associated rationale that conflict across those factors. These endeavors we undertook closely adhere to established case study research methods [65], which MMCS builds on, that emphasize strengthening the validity and reliability of findings through documenting a detailed analytic protocol, as well as reviewing data to ensure that patterns match across analytic units (e.g., factors, interviewees, sites). Third, our findings are based on three sites each for high/medium/low sustainability, and although we identified single factors associated with sustainability, we found no specific combinations of factors’ presence and influence that were repeatedly existent at a majority of the sites designated as high/medium/low sustainability. Examining additional sites on the factors identified through this work (as we will for our subsequent CCM implementation trial described below) will allow more opportunities for repeated combinations and other factors to emerge, making possible firmer conclusions regarding the extent to which the currently identified factors and absence of identified combinations are applicable beyond the sites included in this study. Fourth, the identified influencing factor “leadership support for CCM” (under the Context domain of the i-PARIHS framework) substantially overlaps in concept with the core “organizational/leadership support” element of the CCM. To avoid circular reasoning, we used leadership support-related data to inform our assignment of sites’ high, medium, or low CCM sustainability, rather than as a reason for the sites’ CCM sustainability. In reality, strong leadership support may both result from and contribute to implementation and sustainability [16, 66], and thus causal relationships between the i-PARIHS-aligned influencing factors and the CCM elements (possibly with feedback loops) warrant further examination to most appropriately use leadership support-related data in future analyses of CCM sustainability. Fifth, findings may be subject to both social desirability bias in participants providing more positive than negative evidence of sustainability (especially participants who are responsible for implementing and sustaining CCM-aligned care at their site) and the project team members’ bias in interpreting the findings to align to their expectations of further effort being necessary to sustainably implement the CCM. To help mitigate this challenge, the project interviewers strove to elicit from participants both positive and negative perceptions and experiences related to CCM-based care delivery, both of which were present in the examined interview data.

Future work stemming from this project is twofold. Regarding CCM implementation, we will conduct a subsequent CCM implementation trial involving eight new sites to prospectively examine how implementation facilitation with an enhanced focus on these findings affects CCM sustainability. We started planning for sustainability prior to implementation, looking to this work for indicators of specific modifications needed to the previous way in which we used implementation facilitation to promote the uptake of CCM-based care [67]. Findings from this work suggest that sustainability may be related most strongly to (i) provider collaboration, (ii) knowledge retention during staff/leadership transitions, and (iii) availability of skilled internal facilitators. Hence, we will accordingly prioritize developing procedures for (i) regular CCM-related information exchange amongst BHIP team members, as well as between the BHIP team and clinic leadership, (ii) both translating knowledge to and keeping knowledge documented at the site, and (iii) supporting the sites’ own personnel to take the lead in driving CCM implementation.

Regarding MMCS, we will continuously refine and improve the method by learning from other projects applying, testing, and critiquing MMCS. Outside of our CCM-related projects, examinations of implementation data using MMCS are actively underway for various implementation efforts including that of a data dashboard for decision support on transitioning psychiatrically stable patients from specialty mental health to primary care [2], a peer-led healthy lifestyle intervention for individuals with serious mental illness [3], screening programs for intimate partner violence [4], and a policy- and organization-based health system strengthening intervention to improve health systems in sub-Saharan Africa [5]. As MMCS is used by more projects that differ from one another in their specific outcome of interest, and especially in light of our MMCS application that examines factors related to sustainability, we are curious whether certain proximal to distal outcomes are more subject to heterogeneity in influencing factors than other outcomes. For instance, sustainability outcomes, which are tracked following a longer passage of time than some other outcomes, may be subject to more contextual variations that occur over time and thus could particularly benefit from being examined using MMCS. We will also explore MMCS’ complementarity with coincidence analysis and other configurational analytical approaches [68] for examining implementation phenomena. We are excited about both the step-by-step traceability that MMCS can bring to such methods and those methods’ computational algorithms that can be beneficial to incorporate into MMCS for projects with larger numbers of sites. For example, Salvati and colleagues [69] described both the inspiration that MMCS provided in structuring their data as well as how they addressed MMCS’ visualization shortcomings through their innovative data matrix heat mapping, which led to their selection of specific factors to include in their subsequent coincidence analysis. Coincidence analysis is an enhancement to qualitative comparative analysis and other configurational analytical methods, in that it is formulated specifically for causal inference [70]. Thus, in considering improved reformulations of MMCS’ steps to better characterize examined factors as explicit causes to the outcomes of interest, we are inspired by and can draw on coincidence analysis’ approach to building and evaluating causal chains that link factors to outcomes. Relatedly, we have begun to actively consider the potential contribution that MMCS can make to hypothesis generation and theory development for implementation science. As efforts to understand the mechanisms through which implementation strategies work are gaining momentum [71‐73], there is an increased need for methods that help decompose our understanding of factors that influence the mechanistic pathways from strategies to outcomes [74]. Implementation science is facing the need to develop theories, beyond frameworks, which delineate hypotheses for observed implementation phenomena that can be subsequently tested [75]. The methodical approach that MMCS offers can aid this important endeavor, by enabling data curation and examination of pertinent factors in a consistent way that allows meaningful synthesis of findings across sites and studies. We see these future directions as concrete steps toward elucidating the factors related to sustainable implementation of EBPs, especially leveraging data from projects where the number of sites is much smaller than the number of factors that may matter—which is indeed the case for most implementation projects.

Using MMCS, we found that provider collaboration, knowledge retention during staff/leadership transitions, and availability of skilled internal facilitators may be most strongly related to CCM sustainability in VA outpatient mental health clinics. Informed by these findings, we have a subsequent CCM implementation trial underway to prospectively test whether increasing the aforementioned factors within implementation facilitation enhances sustainability. The MMCS steps used here for systematic multi-site examination can also be applied to determining sustainability-related factors relevant to various other EBPs and implementation contexts.