A Model for Advancing Scale-Up of Complex Interventions for Vulnerable Populations: the ALACRITY Center for Health and Longevity in Mental Illness

The Future of the Psychiatric Mental Health Nursing Workforce: Using Our Skill Set to Address Incongruities in Mental Health Care Delivery

The USA is dealing with well-documented issues around mental health and its treatment. The Psychiatric Mental Health (PMH) workforce is growing and practicing in a variety of roles within the mental health system. How will PMH nurses address instances when the structure of services does not meet the mental health needs of the population? In this piece, I argue that to some degree the future of the PMH workforce will be determined by how well we use our capacity and capabilities to address incongruities in service structure and population needs. Five areas of concern with mental health services are outlined; they all involve factors that can be addressed with innovative approaches and optimum utilization of the PMH workforce. Included are suggestions on how PMH nurses might direct efforts toward these service issues, particularly by using their skill set and presence in the mental health system. Strategies include forging a tighter connection between the work of advanced practice and registered nurses in delivering care. Broadly, these efforts should be directed at building models of patient-centered care that address the needs of populations, reducing disparities, and demonstrating how engagement is a critical lever of effective inpatient and community-based care.

Establishing a Care Continuum for Cardiometabolic Conditions for Patients with Serious Mental Illness

PURPOSE OF REVIEW

Addressing cardiometabolic risk factors in persons with serious mental illness requires early screening and proactive medical management in both medical and mental health settings.

RECENT FINDINGS

Cardiovascular disease remains the leading cause of death for persons with serious mental illness (SMI), such as schizophrenia or bipolar disorder, much of which is driven by a high prevalence of metabolic syndrome, diabetes, and tobacco use. We summarize barriers and recent approaches to screening and treatment for metabolic cardiovascular risk factors within physical health and specialty mental health settings. Incorporating system-based and provider-level support within physical health and psychiatric clinical settings should contribute to improvement for screening, diagnosis, and treatment for cardiometabolic conditions for patients with SMI. Targeted education for clinicians and leveraging multi-disciplinary teams are important first steps to recognize and treat populations with SMI at risk of CVD.

Designing Practical Motivational Interviewing Training for Mental Health Practitioners Implementing Behavioral Lifestyle Interventions: Protocol for 3 Pilot Intervention Studies

BACKGROUND

Motivational interviewing (MI) is an evidence-based, patient-centered communication method shown to be effective in helping persons with serious mental illness (SMI) to improve health behaviors. In clinical trials where study staff conducted lifestyle interventions incorporating an MI approach, cardiovascular disease (CVD) risk profiles of participants with SMI showed improvement. Given the disproportionate burden of CVD in this population, practitioners who provide somatic and mental health care to persons with SMI are ideally positioned to deliver patient-centered CVD risk reduction interventions. However, the time for MI training (traditionally 16-24 hours), follow-up feedback, and the coaching required to develop and maintain patient-centered skills are significant barriers to incorporating MI when scaling up these evidence-based practices.

OBJECTIVE

We describe the design and development of the following 2 scalable MI training approaches for community mental health practitioners: real-time brief workshops and follow-up asynchronous avatar training. These approaches are being used in 3 different pilot implementation research projects that address weight loss, smoking cessation, and CVD risk reduction in people with SMI who are a part of ALACRITY Center, a research-to-practice translation center funded by the National Institute of Mental Health.

METHODS

Clinicians and staff in community mental health clinics across Maryland were trained to deliver 3 distinct evidence-based physical health lifestyle interventions using an MI approach to persons with SMI. The real-time brief MI workshop training for ACHIEVE-D weight loss coaches was 4 hours; IMPACT smoking cessation counselors received 2-hour workshops and prescribers received 1-hour workshops; and RHYTHM CVD risk reduction program staff received 4 hours of MI. All workshop trainings occurred over videoconference. The asynchronous avatar training includes 1 common didactic instructional module for the 3 projects and 1 conversation simulation unique to each study's target behavior. Avatar training is accessible on a commercial website. We plan to assess practitioners' attitudes and beliefs about MI and evaluate the impact of the 2 MI training approaches on their MI skills 3, 6, and 12 months after training using the MI Treatment Integrity 4.2.1 coding tool and the data generated by the avatar-automated scoring system.

RESULTS

The ALACRITY Center was funded in August 2018. We have implemented the MI training for 126 practitioners who are currently delivering the 3 implementation projects. We expect the studies to be complete in May 2023.

CONCLUSIONS

This study will contribute to knowledge about the effect of brief real-time training augmented with avatar skills practice on clinician MI skills. If MI Treatment Integrity scoring shows it to be effective, brief videoconference trainings supplemented with avatar skills practice could be used to train busy community mental health practitioners to use an MI approach when implementing physical health interventions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/44830.

Development of the SPREAD framework to support the scaling of de-implementation strategies: a mixed-methods study

OBJECTIVE

We aimed to increase the understanding of the scaling of de-implementation strategies by identifying the determinants of the process and developing a determinant framework.

DESIGN AND METHODS

This study has a mixed-methods design. First, we performed an integrative review to build a literature-based framework describing the determinants of the scaling of healthcare innovations and interventions. PubMed and EMBASE were searched for relevant studies from 1995 to December 2020. We systematically extracted the determinants of the scaling of interventions and developed a literature-based framework. Subsequently, this framework was discussed in four focus groups with national and international de-implementation experts. The literature-based framework was complemented by the findings of the focus group meetings and adapted for the scaling of de-implementation strategies.

RESULTS

The literature search resulted in 42 articles that discussed the determinants of the scaling of innovations and interventions. No articles described determinants specifically for de-implementation strategies. During the focus groups, all participants agreed on the relevance of the extracted determinants for the scaling of de-implementation strategies. The experts emphasised that while the determinants are relevant for various countries, the implications differ due to different contexts, cultures and histories. The analyses of the focus groups resulted in additional topics and determinants, namely, medical training, professional networks, interests of stakeholders, clinical guidelines and patients' perspectives. The results of the focus group meetings were combined with the literature framework, which together formed the supporting the scaling of de-implementation strategies (SPREAD) framework. The SPREAD framework includes determinants from four domains: (1) scaling plan, (2) external context, (3) de-implementation strategy and (4) adopters.

CONCLUSIONS

The SPREAD framework describes the determinants of the scaling of de-implementation strategies. These determinants are potential targets for various parties to facilitate the scaling of de-implementation strategies. Future research should validate these determinants of the scaling of de-implementation strategies.

Scaling Evidence-Based Interventions to Improve the Cardiovascular Health of People With Serious Mental Illness

People with serious mental illnesses (SMIs) experience excess mortality, driven in large part by high rates of cardiovascular disease (CVD), with all cardiovascular disease risk factors elevated. Interventions designed to improve the cardiovascular health of people with SMI have been shown to lead to clinically significant improvements in clinical trials; however, the uptake of these interventions into real-life clinical settings remains limited. Implementation strategies, which constitute the "how to" component of changing healthcare practice, are critical to supporting the scale-up of evidence-based interventions that can improve the cardiovascular health of people with SMI. And yet, implementation strategies are often poorly described and rarely justified theoretically in the literature, limiting the ability of researchers and practitioners to tease apart why, what, how, and when implementation strategies lead to improvement. In this Perspective, we describe the implementation strategies that the Johns Hopkins ALACRITY Center for Health and Longevity in Mental Illness is using to scale-up three evidenced-based interventions related to: (1) weight loss; (2) tobacco smoking cessation treatment; and (3) hypertension, dyslipidemia, and diabetes care for people with SMI. Building on concepts from the literature on complex health interventions, we focus on considerations related to the core function of an intervention (i.e., or basic purposes of the change process that the health intervention seeks to facilitate) vs. the form (i.e., implementation strategies or specific activities taken to carry out core functions that are customized to local contexts). By clearly delineating how implementation strategies are operationalized to support the interventions' core functions across these three studies, we aim to build and improve the future evidence base of how to adapt, implement, and evaluate interventions to improve the cardiovascular health of people with SMI.

Agent-Based Modeling for Implementation Research: An Application to Tobacco Smoking Cessation for Persons with Serious Mental Illness

Background

Implementation researchers have sought ways to use simulations to support the core components of implementation, which typically include assessing the need for change, designing implementation strategies, executing the strategies, and evaluating outcomes. The goal of this paper is to explain how agent-based modeling could fulfill this role.

Methods

We describe agent-based modeling with respect to other simulation methods that have been used in implementation science, using non-technical language that is broadly accessible. We then provide a stepwise procedure for developing agent-based models of implementation processes. We use, as a case study to illustrate the procedure, the implementation of evidence-based smoking cessation practices for persons with serious mental illness (SMI) in community mental health clinics.

Results

For our case study, we present descriptions of the motivating research questions, specific models used to answer these questions, and a summary of the insights that can be obtained from the models. In the first example, we use a simple form of agent-based modeling to simulate the observed smoking behaviors of persons with SMI in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with SMI). In the second example, we illustrate how a more complex agent-based approach that includes interactions between patients, providers and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland.

Conclusion

In this paper we explain how agent-based models can be used to address implementation science research questions and provide a procedure for setting up simulation models. Through our examples, we show how what-if scenarios can be examined in the implementation process, which are particularly useful in implementation frameworks with adaptive components.