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      Facilitators of and Barriers to Integrating Digital Mental Health Into County Mental Health Services: Qualitative Interview Analyses

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          Abstract

          Background

          Digital mental health interventions (DMHIs) represent a promising solution to address the growing unmet mental health needs and increase access to care. Integrating DMHIs into clinical and community settings is challenging and complex. Frameworks that explore a wide range of factors, such as the Exploration, Preparation, Implementation, Sustainment (EPIS) framework, can be useful for examining multilevel factors related to DMHI implementation efforts.

          Objective

          This paper aimed to identify the barriers to, facilitators of, and best practice recommendations for implementing DMHIs across similar organizational settings, according to the EPIS domains of inner context, outer context, innovation factors, and bridging factors.

          Methods

          This study stems from a large state-funded project in which 6 county behavioral health departments in California explored the use of DMHIs as part of county mental health services. Our team conducted interviews with clinical staff, peer support specialists, county leaders, project leaders, and clinic leaders using a semistructured interview guide. The development of the semistructured interview guide was informed by expert input regarding relevant inner context, outer context, innovation factors, and bridging factors in the exploration, preparation, and implementation phases of the EPIS framework. We followed a recursive 6-step process to conduct qualitative analyses using inductive and deductive components guided by the EPIS framework.

          Results

          On the basis of 69 interviews, we identified 3 main themes that aligned with the EPIS framework: readiness of individuals, readiness of innovations, and readiness of organizations and systems. Individual-level readiness referred to the extent to which clients had the necessary technological tools (eg, smartphones) and knowledge (digital literacy) to support the DMHI. Innovation-level readiness pertained to the accessibility, usefulness, safety, and fit of the DMHI. Organization- and system-level readiness concerned the extent to which providers and leadership collectively held positive views about DMHIs as well as the extent to which infrastructure (eg, staffing and payment model) was appropriate.

          Conclusions

          The successful implementation of DMHIs requires readiness at the individual, innovation, and organization and system levels. To improve individual-level readiness, we recommend equitable device distribution and digital literacy training. To improve innovation readiness, we recommend making DMHIs easier to use and introduce, clinically useful, and safe and adapting them to fit into the existing client needs and clinical workflow. To improve organization- and system-level readiness, we recommend supporting providers and local behavioral health departments with adequate technology and training and exploring potential system transformations (eg, integrated care model). Conceptualizing DMHIs as services allows the consideration of both the innovation characteristics of DMHIs (eg, efficacy, safety, and clinical usefulness) and the ecosystem around DMHIs, such as individual and organizational characteristics (inner context), purveyors and intermediaries (bridging factor), client characteristics (outer context), as well as the fit between the innovation and implementation settings (innovation factor).

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          Most cited references48

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          Using thematic analysis in psychology

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            Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies

            Background Many promising technological innovations in health and social care are characterized by nonadoption or abandonment by individuals or by failed attempts to scale up locally, spread distantly, or sustain the innovation long term at the organization or system level. Objective Our objective was to produce an evidence-based, theory-informed, and pragmatic framework to help predict and evaluate the success of a technology-supported health or social care program. Methods The study had 2 parallel components: (1) secondary research (hermeneutic systematic review) to identify key domains, and (2) empirical case studies of technology implementation to explore, test, and refine these domains. We studied 6 technology-supported programs—video outpatient consultations, global positioning system tracking for cognitive impairment, pendant alarm services, remote biomarker monitoring for heart failure, care organizing software, and integrated case management via data sharing—using longitudinal ethnography and action research for up to 3 years across more than 20 organizations. Data were collected at micro level (individual technology users), meso level (organizational processes and systems), and macro level (national policy and wider context). Analysis and synthesis was aided by sociotechnically informed theories of individual, organizational, and system change. The draft framework was shared with colleagues who were introducing or evaluating other technology-supported health or care programs and refined in response to feedback. Results The literature review identified 28 previous technology implementation frameworks, of which 14 had taken a dynamic systems approach (including 2 integrative reviews of previous work). Our empirical dataset consisted of over 400 hours of ethnographic observation, 165 semistructured interviews, and 200 documents. The final nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework included questions in 7 domains: the condition or illness, the technology, the value proposition, the adopter system (comprising professional staff, patient, and lay caregivers), the organization(s), the wider (institutional and societal) context, and the interaction and mutual adaptation between all these domains over time. Our empirical case studies raised a variety of challenges across all 7 domains, each classified as simple (straightforward, predictable, few components), complicated (multiple interacting components or issues), or complex (dynamic, unpredictable, not easily disaggregated into constituent components). Programs characterized by complicatedness proved difficult but not impossible to implement. Those characterized by complexity in multiple NASSS domains rarely, if ever, became mainstreamed. The framework showed promise when applied (both prospectively and retrospectively) to other programs. Conclusions Subject to further empirical testing, NASSS could be applied across a range of technological innovations in health and social care. It has several potential uses: (1) to inform the design of a new technology; (2) to identify technological solutions that (perhaps despite policy or industry enthusiasm) have a limited chance of achieving large-scale, sustained adoption; (3) to plan the implementation, scale-up, or rollout of a technology program; and (4) to explain and learn from program failures.
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              Advancing a Conceptual Model of Evidence-Based Practice Implementation in Public Service Sectors

              Implementation science is a quickly growing discipline. Lessons learned from business and medical settings are being applied but it is unclear how well they translate to settings with different historical origins and customs (e.g., public mental health, social service, alcohol/drug sectors). The purpose of this paper is to propose a multi-level, four phase model of the implementation process (i.e., Exploration, Adoption/Preparation, Implementation, Sustainment), derived from extant literature, and apply it to public sector services. We highlight features of the model likely to be particularly important in each phase, while considering the outer and inner contexts (i.e., levels) of public sector service systems.
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                Author and article information

                Contributors
                Journal
                JMIR Form Res
                JMIR Form Res
                JFR
                JMIR Formative Research
                JMIR Publications (Toronto, Canada )
                2561-326X
                2023
                16 May 2023
                : 7
                : e45718
                Affiliations
                [1 ] Department of Medicine University of California, Irvine Irvine, CA United States
                [2 ] Department of Psychiatry University of California, San Diego La Jolla, CA United States
                [3 ] Altman Clinical and Translational Research Institute Dissemination and Implementation Science Center University of California, San Diego La Jolla, CA United States
                [4 ] Child and Adolescent Services Research Center San Diego, CA United States
                [5 ] Department of Psychological Science University of California, Irvine Irvine, CA United States
                [6 ] Herbert Wertheim School of Public Health and Human Longevity Science University of California, San Diego La Jolla, CA United States
                [7 ] The Design Lab University of California, San Diego La Jolla, CA United States
                [8 ] Department of Public Health University of California, Irvine Irvine, CA United States
                [9 ] Department of Informatics University of California, Irvine Irvine, CA United States
                Author notes
                Corresponding Author: Xin Zhao zhaox44@ 123456hs.uci.edu
                Author information
                https://orcid.org/0000-0001-8455-9757
                https://orcid.org/0000-0001-6520-2920
                https://orcid.org/0009-0002-3446-7410
                https://orcid.org/0009-0002-7992-6983
                https://orcid.org/0009-0001-9744-127X
                https://orcid.org/0000-0003-2980-1592
                https://orcid.org/0000-0002-3099-8081
                https://orcid.org/0000-0002-8314-0732
                https://orcid.org/0000-0003-4121-4948
                https://orcid.org/0000-0003-4147-5785
                https://orcid.org/0000-0002-1003-0399
                https://orcid.org/0000-0003-0742-9240
                Article
                v7i1e45718
                10.2196/45718
                10230355
                37191975
                d2ba3eb0-fbda-4ea5-9d13-fde4625be57a
                ©Xin Zhao, Nicole A Stadnick, Eduardo Ceballos-Corro, Jorge Castro Jr, Kera Mallard-Swanson, Kristina J Palomares, Elizabeth Eikey, Margaret Schneider, Kai Zheng, Dana B Mukamel, Stephen M Schueller, Dara H Sorkin. Originally published in JMIR Formative Research (https://formative.jmir.org), 16.05.2023.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

                History
                : 19 January 2023
                : 14 February 2023
                : 7 March 2023
                : 22 March 2023
                Categories
                Original Paper
                Original Paper

                digital mental health,mobile health,mhealth,implementation readiness,implementation science,qualitative analyses,mobile phone

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