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      Integrating addiction treatment into primary care using mobile health technology: protocol for an implementation research study

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          Abstract

          Background

          Healthcare reform in the United States is encouraging Federally Qualified Health Centers and other primary-care practices to integrate treatment for addiction and other behavioral health conditions into their practices. The potential of mobile health technologies to manage addiction and comorbidities such as HIV in these settings is substantial but largely untested. This paper describes a protocol to evaluate the implementation of an E-Health integrated communication technology delivered via mobile phones, called Seva, into primary-care settings. Seva is an evidence-based system of addiction treatment and recovery support for patients and real-time caseload monitoring for clinicians.

          Methods/Design

          Our implementation strategy uses three models of organizational change: the Program Planning Model to promote acceptance and sustainability, the NIATx quality improvement model to create a welcoming environment for change, and Rogers’s diffusion of innovations research, which facilitates adaptations of innovations to maximize their adoption potential. We will implement Seva and conduct an intensive, mixed-methods assessment at three diverse Federally Qualified Healthcare Centers in the United States. Our non-concurrent multiple-baseline design includes three periods — pretest (ending in four months of implementation preparation), active Seva implementation, and maintenance — with implementation staggered at six-month intervals across sites. The first site will serve as a pilot clinic. We will track the timing of intervention elements and assess study outcomes within each dimension of the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework, including effects on clinicians, patients, and practices. Our mixed-methods approach will include quantitative ( e.g., interrupted time-series analysis of treatment attendance, with clinics as the unit of analysis) and qualitative ( e.g., staff interviews regarding adaptations to implementation protocol) methods, and assessment of implementation costs.

          Discussion

          If implementation is successful, the field will have a proven technology that helps Federally Qualified Health Centers and affiliated organizations provide addiction treatment and recovery support, as well as a proven strategy for implementing the technology. Seva also has the potential to improve core elements of addiction treatment, such as referral and treatment processes. A mobile technology for addiction treatment and accompanying implementation model could provide a cost-effective means to improve the lives of patients with drug and alcohol problems.

          Trial registration

          ClinicalTrials.gov ( NCT01963234).

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          Most cited references34

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          Relapse Prevention

          Relapse prevention (RP) is an important component of alcoholism treatment. The RP model proposed by Marlatt and Gordon suggests that both immediate determinants (e.g., high-risk situations, coping skills, outcome expectancies, and the abstinence violation effect) and covert antecedents (e.g., lifestyle factors and urges and cravings) can contribute to relapse. The RP model also incorporates numerous specific and global intervention strategies that allow therapist and client to address each step of the relapse process. Specific interventions include identifying specific high-risk situations for each client and enhancing the client’s skills for coping with those situations, increasing the client’s self-efficacy, eliminating myths regarding alcohol’s effects, managing lapses, and restructuring the client’s perceptions of the relapse process. Global strategies comprise balancing the client’s lifestyle and helping him or her develop positive addictions, employing stimulus control techniques and urge-management techniques, and developing relapse road maps. Several studies have provided theoretical and practical support for the RP model.
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            Which elements of improvement collaboratives are most effective? A cluster-randomized trial.

            Improvement collaboratives consisting of various components are used throughout health care to improve quality, but no study has identified which components work best. This study tested the effectiveness of different components in addiction treatment services, hypothesizing that a combination of all components would be most effective. An unblinded cluster-randomized trial assigned clinics to one of four groups: interest circle calls (group teleconferences), clinic-level coaching, learning sessions (large face-to-face meetings) and a combination of all three. Interest circle calls functioned as a minimal intervention comparison group. Out-patient addiction treatment clinics in the United States. Two hundred and one clinics in five states. Clinic data managers submitted data on three primary outcomes: waiting-time (mean days between first contact and first treatment), retention (percentage of patients retained from first to fourth treatment session) and annual number of new patients. State and group costs were collected for a cost-effectiveness analysis. Waiting-time declined significantly for three groups: coaching (an average of 4.6 days/clinic, P = 0.001), learning sessions (3.5 days/clinic, P = 0.012) and the combination (4.7 days/clinic, P = 0.001). The coaching and combination groups increased significantly the number of new patients (19.5%, P = 0.028; 8.9%, P = 0.029; respectively). Interest circle calls showed no significant effect on outcomes. None of the groups improved retention significantly. The estimated cost per clinic was $2878 for coaching versus $7930 for the combination. Coaching and the combination of collaborative components were about equally effective in achieving study aims, but coaching was substantially more cost-effective. When trying to improve the effectiveness of addiction treatment services, clinic-level coaching appears to help improve waiting-time and number of new patients while other components of improvement collaboratives (interest circles calls and learning sessions) do not seem to add further value. © 2013 The Authors, Addiction © 2013 Society for the Study of Addiction.
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              Computerized behavior therapy for opioid-dependent outpatients: a randomized controlled trial.

              The authors evaluated the efficacy of an interactive, computer-based behavioral therapy intervention, grounded in the community reinforcement approach (CRA) plus voucher-based contingency management model of behavior therapy. Our randomized, controlled trial was conducted at a university-based research clinic. Participants comprised 135 volunteer adult outpatients who met DSM-IV criteria for opioid dependence. All participants received maintenance treatment with buprenorphine and were randomly assigned to one of three treatments: (a) therapist-delivered CRA treatment with vouchers, (b) computer-assisted CRA treatment with vouchers, or (c) standard treatment. The therapist-delivered and computer-assisted CRA plus vouchers interventions produced comparable weeks of continuous opioid and cocaine abstinence (M = 7.98 and 7.78, respectively) and significantly greater weeks of abstinence than the standard intervention (M = 4.69; p < .05), yet participants in the computer-assisted CRA condition had over 80% of their intervention delivered by an interactive computer program. The comparable efficacy obtained with computer-assisted and therapist-delivered therapy may enable more widespread dissemination of the evidence-based CRA plus vouchers intervention in a manner that is cost-effective and ensures treatment fidelity.
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                Author and article information

                Contributors
                Journal
                Implement Sci
                Implement Sci
                Implementation Science : IS
                BioMed Central
                1748-5908
                2014
                29 May 2014
                : 9
                : 65
                Affiliations
                [1 ]Center for Health Enhancement Systems Studies, Industrial and Systems Engineering Department, University of Wisconsin – Madison, Madison, WI 53706, USA
                [2 ]Center for Technology and Behavioral Health, Dartmouth Psychiatric Research Center, Dartmouth College, Lebanon, NH 03766, USA
                [3 ]Center for Addictive Disorders, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI 53713, USA
                [4 ]Communication Arts Department, University of Wisconsin – Madison, Madison, WI 53706, USA
                [5 ]School of Social Work, University of Wisconsin – Madison, Madison, WI 53706, USA
                Article
                1748-5908-9-65
                10.1186/1748-5908-9-65
                4072605
                24884976
                db7f9a61-016c-4964-bdd4-94b6399dee23
                Copyright © 2014 Quanbeck et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 17 February 2014
                : 17 May 2014
                Categories
                Study Protocol

                Medicine
                primary care integration,systems change,e-health,re-aim framework
                Medicine
                primary care integration, systems change, e-health, re-aim framework

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