Automatic classification of communication logs into implementation stages via text analysis

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention's generalizability and interoperability with existing systems, as well as the inner settings' data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting.

High-Fidelity Agent-Based Modeling to Support Prevention Decision-Making: an Open Science Approach

Preventing adverse health outcomes is complex due to the multi-level contexts and social systems in which these phenomena occur. To capture both the systemic effects, local determinants, and individual-level risks and protective factors simultaneously, the prevention field has called for adoption of system science methods in general and agent-based models (ABMs) specifically. While these models can provide unique and timely insight into the potential of prevention strategies, an ABM's ability to do so depends strongly on its accuracy in capturing the phenomenon. Furthermore, for ABMs to be useful, they need to be accepted by and available to decision-makers and other stakeholders. These two attributes of accuracy and acceptability are key components of open science. To ensure the creation of high-fidelity models and reliability in their outcomes and consequent model-based decision-making, we present a set of recommendations for adopting and using this novel method. We recommend ways to include stakeholders throughout the modeling process, as well as ways to conduct model verification, validation, and replication. Examples from HIV and overdose prevention work illustrate how these recommendations can be applied.

Sustainability planning in the US response to the opioid crisis: An examination using expert and text mining approaches

Between January 2016 and June 2020, the Substance Abuse and Mental Health Services Administration rapidly distributed $7.5 billion in response to the U.S. opioid crisis. These funds are designed to increase access to medications for addiction treatment, reduce unmet treatment need, reduce overdose death rates, and provide and sustain effective prevention, treatment and recovery activities. It is unclear whether or not the services developed using these funds will be sustained beyond the start-up period. Based on 34 (64%) State Opioid Response (SOR) applications, we assessed the states' sustainability plans focusing on potential funding sources, policies, and quality monitoring. We found variable commitment to sustainability across response plans with less than half the states adequately describing sustainability plans. States with higher proportions of opioid prescribing, opioid misuse, and poverty had somewhat higher scores on sustainment. A text mining/machine learning approach automatically rated sustainability in SOR applications with an 82% accuracy compared to human ratings. Because life saving evidence-based programs and services may be lost, intentional commitment to sustainment beyond the bolus of start-up funding is essential.

Implementation strategies for digital mental health interventions in health care settings

U.S. health care systems are tasked with alleviating the burden of mental health, but are frequently underprepared and lack workforce and resource capacity to deliver services to all in need. Digital mental health interventions (DMHIs) can increase access to evidence-based mental health care. However, DMHIs commonly do not fit into the day-to-day activities of the people who engage with them, resulting in a research-to-practice gap for DMHI implementation. For health care settings, differences between digital and traditional mental health services make alignment and integration challenging. Specialized attention is needed to improve the implementation of DMHIs in health care settings so that these services yield high uptake, engagement, and sustainment. The purpose of this article is to enhance efforts to integrate DMHIs in health care settings by proposing implementation strategies, selected and operationalized based on the discrete strategies established in the Expert Recommendations for Implementing Change project, that align to DMHI-specific barriers in these settings. Guidance is offered in how these strategies can be applied to DMHI implementation across four phases commonly distinguished in implementation science using the Exploration, Preparation, Implementation, Sustainment Framework. Next steps to advance research in this area and improve the research-to-practice gap for implementing DMHIs are recommended. Applying implementation strategies to DMHI implementation will enable psychologists to systematically evaluate this process, which can yield an enhanced understanding of the factors that facilitate implementation success and improve the translation of DMHIs from controlled trials to real-world settings. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Measurement of sustainment of prevention programs and initiatives: the sustainment measurement system scale

Background

Enhancing the sustainability of evidence-based prevention programs for mental and behavioral health requires tools for measuring both sustainability determinants and sustainment outcomes. The aim of this study was to develop the Sustainment Measurement System Scale (SMSS) and to assess its reliability and construct validity for measuring both determinants and outcomes of efforts to sustain prevention programs and initiatives.

Methods

A 42-item scale comprised of items identified from qualitative data collected from 45 representatives of 10 programs and 8 SAMHSA program officers was administered to 186 representatives of 145 programs funded by 7 SAMHSA prevention grant initiatives. Cronbach’s alphas were used to determine inter-item reliability. Convergent validity was assessed by comparisons of a global measure of sustainment with current SAMHSA-funding status and continued operation in the same form. Discriminant validity was assessed by comparisons of sustainability determinants with whether or not the program had undergone adaptations.

Results

Confirmatory factor analysis provided support for a 35-item model fit to the data. Cronbach’s alpha was .84 for the sustainment outcome construct and ranged from .70 to .93 for the sustainability determinant constructs. All of the determinant constructs were significantly associated with sustainment outcome individual and global measures for the entire sample (p < 0.01 to 0.001) and for community-based programs and programs with a substance abuse focus (p < 0.05 to 0.001). Convergent validity was supported by significant associations between the global sustainment measure and current SAMHSA funding status and continued operation in the same form (p < 0.001). Four of the sustainability determinant constructs (responsive to community needs; coalitions, partnerships, and networks; organizational staff capability; and evaluation, feedback, and program outcomes) were also significantly associated with current SAMHSA funding status (p < 0.5 to 0.01). With the exception of organizational staff capability, all sustainability determinants were unrelated to program adaptation as predicted.

Conclusions

The SMSS demonstrated good reliability and convergent and discriminant validity in assessing likelihood of sustainment of SAMHSA funded prevention programs and initiatives. The measure demonstrates potential in identifying predictors of program sustainment and as a tool for enhancing the likelihood of successful sustainment through ongoing evaluation and feedback.

Three Flavorings for a Soup to Cure what Ails Mental Health Services

With new tools from artificial intelligence and new perspectives on personalizing interventions, we could revolutionize the way mental health services are delivered and achieve major gains in improving the public's mental health. We examine Dr. Bickman's vision around these technological and paradigm changes that would usher in major scientific, workforce training, and societal cultural changes. We argue that additional efforts in research evaluations in implementation have the potential to scale up and adapt existing interventions and scale them out to diverse populations and service systems. The next stage of this work involves testing the effectiveness of personalized interventions that are preferred by the public and integrating these choices into sustainable service systems. We note cautions on the delivery of these programs as automated algorithmic recommendations are heretofore foreign to humans.

A Longitudinal Mixed Method Approach for Assessing Implementation Context and Process Factors: Comparison of Three Sites from a Housing First Implementation Strategy Pilot

BACKGROUND

Implementation science's focus on establishing implementation strategy effectiveness has overshadowed the need to understand differential performance of such strategies under various conditions. Methods allowing for assessment between implementation context and process can help address this gap. This paper provides a detailed description of a mixed method procedure for assessing factors related to the implementation context and process intersection, which was developed as part of the pilot study of the Housing First Technical Assistance and Training (HFTAT) Program, a multifaceted strategy designed to support Housing First model implementation.

METHODS

The HFTAT was pilot tested among a sample of three organizations. Our mixed method approach combines two tools often used in implementation research-the Stages of Implementation Completion and the Consolidated Framework for Implementation Research-in a novel way. Several stages to analysis were completed, starting with a separate analysis of data pertaining to each measure and then two levels of mixed method analysis.

RESULTS

The approach provided a better understanding of the issues that impacted the implementation guided by the HFTAT, suggesting: (1) individual determinants seemed to have a bigger impact based on the number of SIC phases they affected, (2) implementation context and process were connected through climate-related factors in the inner setting that made the sites more or less responsive to addressing identified barriers, and (3) there is a need to better assess context factors to identify areas where implementation drivers should be better targeted to facilitate change, and this is supported by prior research.

CONCLUSIONS

Understanding the underlying factors impacting a setting's performance related to a specific implementation strategy has potential to improve decision-making and optimize future implementation efforts. The approach likely be as successful combining the SIC with other determinant frameworks and should be utilized at the onset of an implementation project to maximize its usefulness.

Quantitative approaches for the evaluation of implementation research studies

Implementation research necessitates a shift from clinical trial methods in both the conduct of the study and in the way that it is evaluated given the focus on the impact of implementation strategies. That is, the methods or techniques to support the adoption and delivery of a clinical or preventive intervention, program, or policy. As strategies target one or more levels within the service delivery system, evaluating their impact needs to follow suit. This article discusses the methods and practices involved in quantitative evaluations of implementation research studies. We focus on evaluation methods that characterize and quantify the overall impacts of an implementation strategy on various outcomes. This article discusses available measurement methods for common quantitative implementation outcomes involved in such an evaluation-adoption, fidelity, implementation cost, reach, and sustainment-and the sources of such data for these metrics using established taxonomies and frameworks. Last, we present an example of a quantitative evaluation from an ongoing randomized rollout implementation trial of the Collaborative Care Model for depression management in a large primary healthcare system.

Reducing Cardiovascular Disparities Through Community-Engaged Implementation Research: A National Heart, Lung, and Blood Institute Workshop Report

Cardiovascular disparities remain pervasive in the United States. Unequal disease burden is evident among population groups based on sex, race, ethnicity, socioeconomic status, educational attainment, nativity, or geography. Despite the significant declines in cardiovascular disease mortality rates in all demographic groups during the last 50 years, large disparities remain by sex, race, ethnicity, and geography. Recent data from modeling studies, linked micromap plots, and small-area analyses also demonstrate prominent variation in cardiovascular disease mortality rates across states and counties, with an especially high disease burden in the southeastern United States and Appalachia. Despite these continued disparities, few large-scale intervention studies have been conducted in these high-burden populations to examine the feasibility of reducing or eliminating cardiovascular disparities. To address this challenge, on June 22 and 23, 2017, the National Heart, Lung, and Blood Institute convened experts from a broad range of biomedical, behavioral, environmental, implementation, and social science backgrounds to summarize the current state of knowledge of cardiovascular disease disparities and propose intervention strategies aligned with the National Heart, Lung, and Blood Institute mission. This report presents the themes, challenges, opportunities, available resources, and recommended actions discussed at the workshop.

An individually tailored family-centered intervention for pediatric obesity in primary care: study protocol of a randomized type II hybrid effectiveness-implementation trial (Raising Healthy Children study)

BACKGROUND

Pediatric obesity is a multi-faceted public health concern that can lead to cardiovascular diseases, cancers, and early mortality. Small changes in diet, physical activity, or BMI can significantly reduce the possibility of developing cardiometabolic risk factors. Family-based behavioral interventions are an underutilized, evidence-based approach that have been found to significantly prevent excess weight gain and obesity in children and adolescents. Poor program availability, low participation rates, and non-adherence are noted barriers to positive outcomes. Effective interventions for pediatric obesity in primary care are hampered by low family functioning, motivation, and adherence to recommendations.

METHODS

This (type II) hybrid effectiveness-implementation randomized trial tests the Family Check-Up 4 Health (FCU4Health) program, which was designed to target health behavior change in children by improving family management practices and parenting skills, with the goal of preventing obesity and excess weight gain. The FCU4Health is assessment driven to tailor services and increase parent motivation. A sample of 350 families with children aged 6 to 12 years who are identified as overweight or obese (BMI ≥ 85th percentile for age and gender) will be enrolled at three primary care clinics [two Federally Qualified Healthcare Centers (FQHCs) and a children's hospital]. All clinics serve predominantly Medicaid patients and a large ethnic minority population, including Latinos, African Americans, and American Indians who face disparities in obesity, cardiometabolic risk, and access to care. The FCU4Health will be coordinated with usual care, using two different delivery strategies: an embedded approach for the two FQHCs and a referral model for the hospital-based clinic. To assess program effectiveness (BMI, body composition, child health behaviors, parenting, and utilization of support services) and implementation outcomes (such outcomes as acceptability, adoption, feasibility, appropriateness, fidelity, and cost), we use a multi-method and multi-informant assessment strategy including electronic health record data, behavioral observation, questionnaires, interviews, and cost capture methods.

DISCUSSION

This study has the potential to prevent excess weight gain, obesity, and health disparities in children by establishing the effectiveness of the FCU4Health and collecting information critical for healthcare decision makers to support sustainable implementation of family-based programs in primary care.

TRIAL REGISTRATION

NCT03013309 ClinicalTrials.gov.

"Scaling-out" evidence-based interventions to new populations or new health care delivery systems

BACKGROUND

Implementing treatments and interventions with demonstrated effectiveness is critical for improving patient health outcomes at a reduced cost. When an evidence-based intervention (EBI) is implemented with fidelity in a setting that is very similar to the setting wherein it was previously found to be effective, it is reasonable to anticipate similar benefits of that EBI. However, one goal of implementation science is to expand the use of EBIs as broadly as is feasible and appropriate in order to foster the greatest public health impact. When implementing an EBI in a novel setting, or targeting novel populations, one must consider whether there is sufficient justification that the EBI would have similar benefits to those found in earlier trials.

DISCUSSION

In this paper, we introduce a new concept for implementation called "scaling-out" when EBIs are adapted either to new populations or new delivery systems, or both. Using existing external validity theories and multilevel mediation modeling, we provide a logical framework for determining what new empirical evidence is required for an intervention to retain its evidence-based standard in this new context. The motivating questions are whether scale-out can reasonably be expected to produce population-level effectiveness as found in previous studies, and what additional empirical evaluations would be necessary to test for this short of an entirely new effectiveness trial. We present evaluation options for assessing whether scaling-out results in the ultimate health outcome of interest.

CONCLUSION

In scaling to health or service delivery systems or population/community contexts that are different from the setting where the EBI was originally tested, there are situations where a shorter timeframe of translation is possible. We argue that implementation of an EBI in a moderately different setting or with a different population can sometimes "borrow strength" from evidence of impact in a prior effectiveness trial. The collection of additional empirical data is deemed necessary by the nature and degree of adaptations to the EBI and the context. Our argument in this paper is conceptual, and we propose formal empirical tests of mediational equivalence in a follow-up paper.