Addiction Clinical Trial
— NIATx 200Official title:
Randomized Control Trial (RCT) Evaluating Improvement Strategies in Addiction Treatment
Verified date | July 2013 |
Source | University of Wisconsin, Madison |
Contact | n/a |
Is FDA regulated | No |
Health authority | United States: Institutional Review Board |
Study type | Interventional |
Addiction treatment is often characterized by long delays between first contact and treatment as well as high no-show and drop out rates leading to unused capacity in apparently full agencies. Patients do not get needed care and agency financial stability is threatened. The Network for Improvement of Addiction Treatment (NIATx) began as a high-intensity improvement collaborative of 39 addiction treatment agencies distributed across 25 states. NIATx substantially improved time to treatment and continuation in treatment by making improvements to organizational processes (such as first contact, intake and assessment, engagement, level of care transitions, paperwork, social support, outreach, and scheduling) in preliminary studies. While the results are very encouraging, they have, by intent, been obtained from a select group of agencies using a high-cost combination of services. A more practical diffusion model is needed to spread process improvements across the spectrum of treatment agencies. This study is a cluster-randomized trial to test the effectiveness and cost of less expensive combinations of the services that make up the NIATx collaborative (interest circles, coach calls, coach visits and learning sessions).
Status | Completed |
Enrollment | 201 |
Est. completion date | January 2011 |
Est. primary completion date | July 2009 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | Both |
Age group | N/A and older |
Eligibility |
Inclusion Criteria: - at least 60 admissions/year - provide outpatient and intensive outpatient levels of care (as defined by ASAM) - provide or use detox services provided by others - have tax-exempt or government status or rely on public funding (e.g., block grants, Medicare, Medicaid, local government, private philanthropy) for at least 50% of their budget - have adopted no more than two of the planned interventions Exclusion Criteria: - are current NIATx members |
Allocation: Randomized, Endpoint Classification: Efficacy Study, Intervention Model: Parallel Assignment, Masking: Open Label, Primary Purpose: Health Services Research
Country | Name | City | State |
---|---|---|---|
United States | University of Wisonsin-Madison | Madison | Wisconsin |
Lead Sponsor | Collaborator |
---|---|
University of Wisconsin, Madison | National Institute on Drug Abuse (NIDA), Oregon Health and Science University |
United States,
Choi D, Hoffman KA, Kim MO, McCarty D. A high-resolution analysis of process improvement: use of quantile regression for wait time. Health Serv Res. 2013 Feb;48(1):333-47. doi: 10.1111/j.1475-6773.2012.01436.x. Epub 2012 Jun 20. — View Citation
Gustafson DH, Quanbeck AR, Robinson JM, Ford JH 2nd, Pulvermacher A, French MT, McConnell KJ, Batalden PB, Hoffman KA, McCarty D. Which elements of improvement collaboratives are most effective? A cluster-randomized trial. Addiction. 2013 Jun;108(6):1145- — View Citation
Gustafson DH. Essential Ingredients for Successful Redesign of Addiction Treatment. Bridge (Kans City). 2012;2(2). pii: v2i2_article01. — View Citation
Hoffman KA, Quanbeck A, Ford JH 2nd, Wrede F, Wright D, Lambert-Wacey D, Chvojka P, Hanchett A, McCarty D. Improving substance abuse data systems to measure 'waiting time to treatment': lessons learned from a quality improvement initiative. Health Informa — View Citation
McCarty D, Chandler RK. Understanding the importance of organizational and system variables on addiction treatment services within criminal justice settings. Drug Alcohol Depend. 2009 Aug 1;103 Suppl 1:S91-3. doi: 10.1016/j.drugalcdep.2009.03.001. Epub 20 — View Citation
McCarty D, Gustafson D, Capoccia VA, Cotter F. Improving care for the treatment of alcohol and drug disorders. J Behav Health Serv Res. 2009 Jan;36(1):52-60. doi: 10.1007/s11414-008-9108-4. Epub 2008 Feb 8. — View Citation
McCarty D, Roman PM, Sorensen J, Weisner C. Health Services Research for Drug and Alcohol Treatment and Prevention. J Drug Issues. 2009 Jan;39(1):197-208. — View Citation
McConnell KJ, Hoffman KA, Quanbeck A, McCarty D. Management practices in substance abuse treatment programs. J Subst Abuse Treat. 2009 Jul;37(1):79-89. doi: 10.1016/j.jsat.2008.11.002. Epub 2009 Feb 4. — View Citation
Quanbeck A, Lang K, Enami K, Brown RL. A cost-benefit analysis of Wisconsin's screening, brief intervention, and referral to treatment program: adding the employer's perspective. WMJ. 2010 Feb;109(1):9-14. — View Citation
Quanbeck A, Wheelock A, Ford JH 2nd, Pulvermacher A, Capoccia V, Gustafson D. Examining access to addiction treatment: scheduling processes and barriers. J Subst Abuse Treat. 2013 Mar;44(3):343-8. doi: 10.1016/j.jsat.2012.08.017. Epub 2012 Sep 27. — View Citation
Quanbeck AR, Gustafson DH, Ford JH 2nd, Pulvermacher A, French MT, McConnell KJ, McCarty D. Disseminating quality improvement: study protocol for a large cluster-randomized trial. Implement Sci. 2011 Apr 27;6:44. doi: 10.1186/1748-5908-6-44. — View Citation
Quanbeck AR, Madden L, Edmundson E, Ford JH 2nd, McConnell KJ, McCarty D, Gustafson DH. A business case for quality improvement in addiction treatment: evidence from the NIATx collaborative. J Behav Health Serv Res. 2012 Jan;39(1):91-100. doi: 10.1007/s11 — View Citation
Roosa M, Scripa JS, Zastowny TR, Ford JH 2nd. Using a NIATx based local learning collaborative for performance improvement. Eval Program Plann. 2011 Nov;34(4):390-8. doi: 10.1016/j.evalprogplan.2011.02.006. Epub 2011 Mar 2. — View Citation
* Note: There are 13 references in all — Click here to view all references
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Change in Average Waiting Time From First Contact to Treatment | The average length of time in days it takes from when a patient first calls for help to the time a patient was able to meet a clinician. In this quality improvement study, changes in this measure over time are reported. Estimates of improvement show the average days of improvement per month based on a best linear unbiased predictor estimate for each site. Note: this study has three primary outcomes. The number of participants analyzed varies for each outcome. The (higher) number of clinics shown in the flow diagram results because clinics may have been analyzed on a subset of the three primary outcomes (e.g., analyzed for waiting time and continuation, but not for annual number of new patients). To be considered "analyzed" in the flow diagram, a clinic must have been included in at least one primary outcomes analysis. |
Baseline and 15 months | No |
Primary | Change in Annual Number of Patient Admissions | We aimed to increase clinics' treatment capacity in this quality improvement study. Capacity was measured by counting clinics' annual number of patient admissions. We monitored changes in admission counts, per clinic, in a pre-post analysis. Changes in the natural logarithm of annual admissions are presented, which approximates the average percentage change (year-to-year) in the number of new patient admissions per clinic. Note: this study has three primary outcomes. The number of participants analyzed varies for each outcome. The (higher) number of clinics shown in the flow diagram results because clinics may have been analyzed on a subset of the three primary outcomes (e.g., analyzed for waiting time and continuation, but not for annual number of new patients). To be considered "analyzed" in the flow diagram, a clinic must have been included in at least one primary outcomes analysis. |
48 months (2 year baseline period and 2 year post-intervention period) | No |
Primary | Change in Average Continuation Rate Through the Fourth Treatment Session | This outcome represents change in the rate at which a clinic's patients continue in treatment. Continuation rate is defined as the percentage of patients that make at least 4 visits to the clinic, on different days, before being discharged. Estimates of improvement show the average percentage points of improvement per month based on a best linear unbiased predictor estimate for each site. Note: this study has three primary outcomes. The number of participants analyzed varies for each outcome. The (higher) number of clinics shown in the flow diagram results because clinics may have been analyzed on a subset of the three primary outcomes (e.g., analyzed for waiting time and continuation, but not for annual number of new patients). To be considered "analyzed" in the flow diagram, a clinic must have been included in at least one primary outcomes analysis. |
Baseline and 21 months | No |
Secondary | Cost of Group | The goal of the economic analysis was to estimate costs of each group for governmental authorities who might organize improvement collaboratives. We collected the cost of personnel (state employees, NIATx employees, coaches and consultants), data management, buildings and facilities, lodging, travel, telephone calls and miscellaneous costs. Costs were categorized as group specific (such as hotel costs for the learning sessions group) or non-group-specific, which included state-incurred costs for outreach, data management and infrastructure, encouraging participation and administration. Cost data were collected three times during the study period and aggregated to create a total cost estimate. Figures reported below represent costs at the arm/group level (costs were not assessed at the organizational level). Measure type is "Number." | Baseline and 18 months | No |
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