The Effect of Intensive Implementation Support on Fidelity for Four Evidence-Based Psychosis Treatments: A Cluster Randomized Trial

Implementing Clinical Guidelines for the Treatment of Psychosis: The Frontline Leaders' Point of View. A Qualitative Study

Background

Despite the large amount of leadership and implementation theories and recommendations, healthcare services continue to struggle with efficiently incorporating new knowledge. The questioning of conventional leadership approaches in healthcare organizations prompted us to investigate how frontline leaders comprehend their own implementation intentions and actions, and how these intentions and actions may impact the implementation of clinical guidelines in mental healthcare in Norway.

Methods

Employing a theory-driven qualitative design, we conducted nine semi-structured interviews with frontline leaders who had recently led implementation of clinical guidelines for the treatment of psychosis in mental health. We employed Systematic Text Condensation, informed by Normalization Process Theory, to structure and analyze the data and used fidelity scales to measure the degree of implementation and distinguish between leaders' levels of success in implementation.

Results

Frontline leaders in units that achieved high success in implementation described their intentions and actions differently, from those with less success. The former group's actions aligned more closely with the constructs of the Normalization Process Theory compared to the latter group when describing their actions. Frontline leaders leading units with a high degree of implementation success describe relation-orientation, trust, and providing adaptive space for staff members to take initiative. In contrast, those leading units with less implementation success describe more control and guidance of co-operators and place more emphasize on information and knowledge.

Conclusion

Differences in how frontline leaders describe their actions and intentions to achieve clinical guideline implementation suggest that the leadership approach of these frontline leaders is an important factor to consider when planning and conducting implementation. To better understand the implementation process, it is important to pay attention to how frontline leaders customize their leadership approaches to the dynamics of complex organizations, and how they interact with their team and superiors.

Implementation of Guidelines on Family Involvement for Persons with Psychotic Disorders (IFIP): A Cluster Randomised Controlled Trial

Family involvement is part of the evidence-based treatment for persons with psychotic disorders, yet is under-implemented despite guideline recommendations. This study assessed whether an implementation support programme increased the adherence to guidelines on family involvement, compared to guideline/manual only. In a cluster randomised design, community mental health centre units in South-East Norway went through stratified allocation to the experimental (n = 7) or control (n = 7) arm. Experimental clusters received an implementation support programme including clinical training and supervision, appointing a family coordinator and an implementation team, a toolkit, and fidelity measurements at baseline, 12, 18, and 24 months with on-site feedback and supervision. Control clusters received no such support and had fidelity measurements at baseline and 24 months without feedback. During fidelity measurements, adherence to the guidelines was measured with the basic family involvement and support scale, the general organizational index, and the family psychoeducation fidelity scale, the latter being the primary outcome. The scales consist of 12-14 items rated from 1 to 5. Data was analysed with an independent samples t-test, linear mixed models, and a tobit regression model. At 24 months, the mean scores were 4.00 or higher on all scales in the experimental arm, and the increase in adherence to the guidelines was significantly greater than in the control arm with p-values < 0.001. Large-scale implementation of guidelines on family involvement for persons with psychotic disorders in community mental health centres may be accomplished, with substantial implementation support.Trial Registration: ClinicalTrials.gov Identifier NCT03869177. Registered 11.03.19.

Can systematic implementation support improve programme fidelity by improving care providers' perceptions of implementation factors? A cluster randomized trial

BACKGROUND

Investigations of implementation factors (e.g., collegial support and sense of coherence) are recommended to better understand and address inadequate implementation outcomes. Little is known about the relationship between implementation factors and outcomes, especially in later phases of an implementation effort. The aims of this study were to assess the association between implementation success (measured by programme fidelity) and care providers' perceptions of implementation factors during an implementation process and to investigate whether these perceptions are affected by systematic implementation support.

METHODS

Using a cluster-randomized design, mental health clinics were drawn to receive implementation support for one (intervention) and not for another (control) of four evidence-based practices. Programme fidelity and care providers' perceptions (Implementation Process Assessment Tool questionnaire) were scored for both intervention and control groups at baseline, 6-, 12- and 18-months. Associations and group differences were tested by means of descriptive statistics (mean, standard deviation and confidence interval) and linear mixed effect analysis.

RESULTS

Including 33 mental health centres or wards, we found care providers' perceptions of a set of implementation factors to be associated with fidelity but not at baseline. After 18 months of implementation effort, fidelity and care providers' perceptions were strongly correlated (B (95% CI) = .7 (.2, 1.1), p = .004). Care providers perceived implementation factors more positively when implementation support was provided than when it was not (t (140) = 2.22, p = .028).

CONCLUSIONS

Implementation support can facilitate positive perceptions among care providers, which is associated with higher programme fidelity. To improve implementation success, we should pay more attention to how care providers constantly perceive implementation factors during all phases of the implementation effort. Further research is needed to investigate the validity of our findings in other settings and to improve our understanding of ongoing decision-making among care providers, i.e., the mechanisms of sustaining the high fidelity of recommended practices.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03271242 (registration date: 05.09.2017).

Time–motion analysis of external facilitation for implementing the Collaborative Chronic Care Model in general mental health clinics: Use of an interval-based data collection approach

Background: Facilitation is an effective strategy to implement evidence-based practices, often involving external facilitators (EFs) bringing content expertise to implementation sites. Estimating time spent on multifaceted EF activities is complex. Furthermore, collecting continuous time–motion data for facilitation tasks is challenging. However, organizations need this information to allocate implementation resources to sites. Thus, our objectives were to conduct a time–motion analysis of external facilitation, and compare continuous versus noncontinuous approaches to collecting time–motion data. Methods: We analyzed EF time–motion data from six VA mental health clinics implementing the evidence-based Collaborative Chronic Care Model (CCM). We documented EF activities during pre-implementation (4–6 weeks) and implementation (12 months) phases. We collected continuous data during the pre-implementation phase, followed by data collection over a 2-week period (henceforth, “a two-week interval”) at each of three time points (beginning/middle/end) during the implementation phase. As a validity check, we assessed how closely interval data represented continuous data collected throughout implementation for two of the sites. Results: EFs spent 21.8 ± 4.5 h/site during pre-implementation off-site, then 27.5 ± 4.6 h/site site-visiting to initiate implementation. Based on the 2-week interval data, EFs spent 2.5 ± 0.8, 1.4 ± 0.6, and 1.2 ± 0.6 h/week toward the implementation’s beginning, middle, and end, respectively. Prevalent activities were preparation/planning, process monitoring, program adaptation, problem identification, and problem-solving. Across all activities, 73.6% of EF time involved email, phone, or video communication. For the two continuous data sites, computed weekly time averages toward the implementation’s beginning, middle, and end differed from the interval data’s averages by 1.0, 0.1, and 0.2 h, respectively. Activities inconsistently captured in the interval data included irregular assessment, stakeholder engagement, and network development. Conclusions: Time–motion analysis of CCM implementation showed initial higher-intensity EF involvement that tapered. The 2-week interval data collection approach, if accounting for its potential underestimation of irregular activities, may be promising/efficient for implementation studies collecting time–motion data.

Effectiveness of Quality Improvement Coaching on Process Outcomes in Health Care Settings: A Systematic Review

BACKGROUND

A culture of improvement is an important feature of high-quality health care systems. However, health care teams often need support to translate quality improvement (QI) activities into practice. One method of support is consultation from a QI coach. The literature suggests that coaching interventions have a positive impact on clinical outcomes. However, the impact of coaching on specific process outcomes, like adoption of clinical care activities, is unknown. Identifying the process outcomes for which QI coaching is most effective could provide specific guidance on when to employ this strategy.

METHODS

We searched multiple databases from inception through July 2021. Studies that addressed the effects of QI coaching on process of care outcomes were included. Two reviewers independently extracted study characteristics and assessed risk of bias. Certainty of evidence was assessed using GRADE.

RESULTS

We identified 1983 articles, of which 23 cluster-randomized trials met eligibility criteria. All but two took place in a primary care setting. Overall, interventions typically targeted multiple simultaneous processes of care activities. We found that coaching probably has a beneficial effect on composite process of care outcomes (n = 9) and ordering of labs and vital signs (n = 6), and possibly has a beneficial effect on changes in organizational process of care (n = 5), appropriate documentation (n = 5), and delivery of appropriate counseling (n = 3). We did not perform meta-analyses because of conceptual heterogeneity around intervention design and outcomes; rather, we synthesized the data narratively. Due to imprecision, inconsistency, and high risk of bias of the included studies, we judged the certainty of these results as low or very low.

CONCLUSION

QI coaching interventions may affect certain processes of care activities such as ordering of labs and vital signs. Future research that advances the identification of when QI coaching is most beneficial for health care teams seeking to implement improvement processes in pursuit of high-quality care will support efficient use of QI resources.

PROTOCOL REGISTRATION

This study was registered and followed a published protocol (PROSPERO: CRD42020165069).