Replication and Sustainability of Improved Access and Retention within the Network for the Improvement of Addiction Treatment

Department of Public Health and Preventive Medicine, CB669, Oregon Health & Science University, CB669, 3181 SW Sam Jackson Park Road, Portland, OR 97239, United States.
Drug and Alcohol Dependence (Impact Factor: 3.42). 07/2008; 98(1-2):63-9. DOI: 10.1016/j.drugalcdep.2008.04.016
Source: PubMed


The Network for the Improvement of Addiction Treatment (NIATx) applies process improvement strategies to enhance the quality of care for the treatment of alcohol and drug disorders. A prior analysis reported significant reductions in days to treatment and significant increases in retention in care [McCarty, D., Gustafson, D. H., Wisdom, J. P., Ford, J., Choi, D., Molfenter, T., Capoccia, V., Cotter, F. 2007. The Network for the Improvement of Addiction Treatment (NIATx): enhancing access and retention. Drug Alcohol Depend. 88, 138-145]. A second cohort of outpatient (n=10) and intensive outpatient (n=4) treatment centers tested the replicability of the NIATx model. An additional 20 months of data from the original cohort (7 outpatient, 4 intensive outpatient, and 4 residential treatment centers) assessed long-term sustainability. The replication analysis found a 38% reduction in days to treatment (30.7 to 19.4 days) during an 18-month intervention. Retention in care improved 13% from the first to second session of care (from 75.4% to 85.0%), 12% between the first and third session of care (69.2-77.7%), and 18% between the first and fourth session of care (57.1-67.5%). The sustainability analysis suggested that treatment centers maintained the reductions in days to treatment and the enhanced retention in care. Replication of the NIATx improvements in a second cohort of treatment centers increases confidence in the application of process improvements to treatment for alcohol and drug disorders. The ability to sustain the gains after project awards were exhausted suggests that participating programs institutionalized the organizational changes that led to the enhanced performance.

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Available from: Dongseok Choi
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    • "By focusing on socio-political aspects of change in community-based organizations, use of the PPM has led to greater community acceptance than seen in comparison groups [38,39]. The NIATx model, an extensively evaluated and widely adopted organizational change model in addiction treatment, provides an evidence-based approach to implementation by optimizing processes that create a welcoming environment for change [40,41]. Rogers’s diffusion of innovations research identified characteristics of innovations associated with successful implementation and sustainability [42]. "
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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 (NCT01963234).
    Full-text · Article · May 2014 · Implementation Science
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    • "Placing thousands of phone calls to clinics revealed a picture of what it is like for patients to access treatment. The undertaking left the research team with the distinct impression that the quality of clinics' phone scheduling processes varied, an observation consistent with anecdotal reports made in other evaluations (Capoccia, et al., 2007; Ford et al., 2007; McCarty et al., 2007; Hoffman et al., 2008). Quality is a difficult concept to define in healthcare and often a matter of subjective perception. "
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    ABSTRACT: This paper reports on the phone scheduling systems that patients encounter when seeking addiction treatment. Researchers made a series of 28 monthly calls to 192 addiction treatment clinics to inquire about the clinics' first available appointment for an assessment. Each month, the date of each clinic's first available appointment and the date the appointment was made were recorded. During a 4-month baseline data collection period, the average waiting time from contact with the clinic to the first available appointment was 7.2days. Clinics engaged in a 15-month quality improvement intervention in which average waiting time was reduced to 5.8days. During the course of the study, researchers noted difficulty in contacting clinics and began recording the date of each additional attempt required to secure an appointment. On average, 0.47 callbacks were required to establish contact with clinics and schedule an appointment. Based on these findings, aspects of quality in phone scheduling processes are discussed. Most people with addiction seek help by calling a local addiction treatment clinic, and the reception they get matters. The results highlight variation in access to addiction treatment and suggest opportunities to improve phone scheduling processes.
    Full-text · Article · Sep 2012 · Journal of substance abuse treatment
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    • "Retention in care also improved; the proportion of clients who completed a first session of care and returned for a second and third session of care increased 18% between the first and second session (72% to 85%) and 17% between the first and third session of care (62% to 73%) (McCarty et al. 2007). A subsequent analysis of 14 outpatient and intensive outpatient treatment programs within the second NIATx cohort replicated the reduction in wait time and the improvement in retention and noted that the first cohort sustained the gains during a 20 month follow-up (Hoffman et al. 2008). "
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    ABSTRACT: Apply quantile regression for a high-resolution analysis of changes in wait time to treatment and assess its applicability to quality improvement data compared with least-squares regression. Addiction treatment programs participating in the Network for the Improvement of Addiction Treatment. We used quantile regression to estimate wait time changes at 5, 50, and 95 percent and compared the results with mean trends by least-squares regression. Quantile regression analysis found statistically significant changes in the 5 and 95 percent quantiles of wait time that were not identified using least-squares regression. Quantile regression enabled estimating changes specific to different percentiles of the wait time distribution. It provided a high-resolution analysis that was more sensitive to changes in quantiles of the wait time distributions.
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