Andrew Quanbeck

Which elements of improvement collaboratives are most effective? A cluster-randomized trial

AIMS 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. DESIGN 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. SETTING Out-patient addiction treatment clinics in the United States. PARTICIPANTS Two hundred and one clinics in five states. MEASUREMENTS 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. FINDINGS 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

Disseminating quality improvement: study protocol for a large cluster-randomized trial

2878 for coaching versus

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

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. CONCLUSIONS 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.

A business case for quality improvement in addiction treatment: Evidence from the NIATx collaborative

BackgroundDissemination is a critical facet of implementing quality improvement in organizations. As a field, addiction treatment has produced effective interventions but disseminated them slowly and reached only a fraction of people needing treatment. This study investigates four methods of disseminating quality improvement (QI) to addiction treatment programs in the U.S. It is, to our knowledge, the largest study of organizational change ever conducted in healthcare. The trial seeks to determine the most cost-effective method of disseminating quality improvement in addiction treatment.MethodsThe study is evaluating the costs and effectiveness of different QI approaches by randomizing 201 addiction-treatment programs to four interventions. Each intervention used a web-based learning kit plus monthly phone calls, coaching, face-to-face meetings, or the combination of all three. Effectiveness is defined as reducing waiting time (days between first contact and treatment), increasing program admissions, and increasing continuation in treatment. Opportunity costs will be estimated for the resources associated with providing the services.OutcomesThe study has three primary outcomes: waiting time, annual program admissions, and continuation in treatment. Secondary outcomes include: voluntary employee turnover, treatment completion, and operating margin. We are also seeking to understand the role of mediators, moderators, and other factors related to an organizations success in making changes.AnalysisWe are fitting a mixed-effect regression model to each programs average monthly waiting time and continuation rates (based on aggregated client records), including terms to isolate state and intervention effects. Admissions to treatment are aggregated to a yearly level to compensate for seasonality. We will order the interventions by cost to compare them pair-wise to the lowest cost intervention (monthly phone calls). All randomized sites with outcome data will be included in the analysis, following the intent-to-treat principle. Organizational covariates in the analysis include program size, management score, and state.DiscussionThe study offers seven recommendations for conducting a large-scale cluster-randomized trial: provide valuable services, have aims that are clear and important, seek powerful allies, understand the recruiting challenge, cultivate commitment, address turnover, and encourage rigor and flexibility.Trial RegistrationClinicalTrials. govNCT00934141

Implementing an mHealth system for substance use disorders in primary care: a mixed methods study of clinicians’ initial expectations and first year experiences

BackgroundHealthcare 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/DesignOur 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.DiscussionIf 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 registrationClinicalTrials.gov (NCT01963234).

Implementing buprenorphine in addiction treatment: payer and provider perspectives in Ohio.

The Network for the Improvement of Addiction Treatment (NIATx) promotes treatment access and retention through a customer-focused quality improvement model. This paper explores the issue of the “business case” for quality improvement in addiction treatment from the provider’s perspective. The business case model developed in this paper is based on case examples of early NIATx participants coupled with a review of the literature. Process inefficiencies indicated by long waiting times, high no-show rates, and low continuation rates cause underutilization of capacity and prevent optimal financial performance. By adopting customer-focused practices aimed at removing barriers to treatment access and retention, providers may be able to improve financial performance, increase staff retention, and gain long-term strategic advantage.

Improving substance abuse data systems to measure ‘waiting time to treatment’: Lessons learned from a quality improvement initiative

BackgroundMillions of Americans need but don’t receive treatment for substance use, and evidence suggests that addiction-focused interventions on smart phones could support their recovery. There is little research on implementation of addiction-related interventions in primary care, particularly in Federally Qualified Health Centers (FQHCs) that provide primary care to underserved populations. We used mixed methods to examine three FQHCs’ implementation of Seva, a smart-phone app that offers patients online support/discussion, health-tracking, and tools for coping with cravings, and offers clinicians information about patients’ health tracking and relapses. We examined (a) clinicians’ initial perspectives about implementing Seva, and (b) the first year of implementation at Site 1.MethodsPrior to staggered implementation at three FQHCs (Midwest city in WI vs. rural town in MT vs. metropolitan NY), interviews, meetings, and focus groups were conducted with 53 clinicians to identify core themes of initial expectations about implementation. One year into implementation at Site 1, clinicians there were re-interviewed. Their reports were supplemented by quantitative data on clinician and patient use of Seva.ResultsClinicians anticipated that Seva could help patients and make behavioral health appointments more efficient, but they were skeptical that physicians would engage with Seva (given high caseloads), and they were uncertain whether patients would use Seva. They were concerned about legal obligations for monitoring patients’ interactions online, including possible “cries for help” or inappropriate interactions. One year later at Site 1, behavioral health care providers, rather than physicians, had incorporated Seva into patient care, primarily by discussing it during appointments. Given workflow/load concerns, only a few key clinicians monitored health tracking/relapses and prompted outreach when needed; two researchers monitored the discussion board and alerted the clinic as needed. Clinician turnover/leave complicated this approach. Contrary to clinicians’ initial concerns, patients showed sustained, mutually supportive use of Seva, with few instances of misuse.ConclusionsResults suggest the value of (a) focusing implementation on behavioral health care providers rather than physicians, (b) assigning a few individuals (not necessarily clinicians) to monitor health tracking, relapses, and the discussion board, (c) anticipating turnover/leave and having designated replacements. Patients showed sustained, positive use of Seva.Trial registrationClinicalTrials.gov (NCT01963234).

Treatment seeking as a mechanism of change in a randomized controlled trial of a mobile health intervention to support recovery from alcohol use disorders

BackgroundBuprenorphine is under-utilized in treating opioid addiction. Payers and providers both have substantial influence over the adoption and use of this medication to enhance recovery. Their views could provide insights into the barriers and facilitators in buprenorphine adoption.MethodsWe conducted individual interviews with 18 Ohio county Alcohol, Drug Addiction, and Mental Health Services (ADAMHS) Boards (payers) and 36 addiction treatment centers (providers) to examine barriers and facilitators to buprenorphine use. Transcripts were reviewed, coded, and qualitatively analyzed. First, we examined reasons that county boards supported buprenorphine use. A second analysis compared county boards and addiction treatment providers on perceived barriers and facilitators to buprenorphine use. The final analysis compared county boards with low and high use of buprenorphine to determine how facilitators and barriers differed between those settings.ResultsCounty boards (payers) promoted buprenorphine use to improve clinical care, reduce opioid overdose deaths, and prepare providers for participation in integrated models of health care delivery with primary care clinics and hospitals. Providers and payers shared many of the same perceptions of facilitators and barriers to buprenorphine use. Common facilitators identified were knowledge of buprenorphine benefits, funds allocated to purchase buprenorphine, and support from the criminal justice system. Common barriers were negative attitudes toward use of agonist pharmacotherapy, payment environment, and physician prescribing capacity. County boards with low buprenorphine use rates cited negative attitudes toward use of agonist medication as a primary barrier. County boards with high rates of buprenorphine use dedicated funds to purchase buprenorphine in spite of concerns about limited physician prescribing capacity.ConclusionsThis qualitative analysis found that attitudes toward use of medication and medication funding environment play important roles in an organization’s decision to begin buprenorphine use and that physician availability influences an organization’s ability to expand buprenorphine use over time.Additional education, reimbursement support, and policy changes are needed to support buprenorphine adoption and use, along with a greater understanding of the roles payers, providers, and regulators play in the adoption of targeted practices.

Systems consultation: protocol for a novel implementation strategy designed to promote evidence-based practice in primary care

Robust data measurement systems assess health care performance and monitor population-level treatment trends. A key challenge in the assessment of substance abuse treatment is the development of systems to accurately monitor service delivery indicators. Wait time to treatment, as defined by the days between first request for service and first treatment, is an important measure of organizational process and delivery of care. The Network for the Improvement of Addiction Treatment emphasizes wait time as a primary outcome in their study of 201 addiction treatment agencies in the USA. This article describes the changes made in five state data systems to monitor wait times and outlines lessons learned that could be applied to other health data tracking systems.

Implementing a Mobile Health System to Integrate the Treatment of Addiction Into Primary Care: A Hybrid Implementation-Effectiveness Study

BACKGROUND We estimated the efficacy of the Addiction-Comprehensive Health Enhancement Support System (A-CHESS) in increasing the use of services for addiction and examined the extent to which this use of services mediated the effects of A-CHESS on risky drinking days and abstinence from drinking. METHODS We conducted secondary data analyses of the A-CHESS randomized controlled trial. Recruitment occurred in five residential treatment programs operated by two addiction treatment organizations. Participants were 349 adults with alcohol use disorders recruited two weeks before discharge from residential treatment. We provided intervention arm participants with a smartphone, the A-CHESS application, and an 8-month service plan. Control arm participants received treatment as usual. Telephone interviews at 4, 8, and 12-month follow-ups assessed past-month risky drinking days, past-month abstinence, and post-discharge service utilization (past-month outpatient addiction treatment and past-week mutual help including Alcoholics Anonymous and Narcotics Anonymous). Using mixed effects latent variable models, we estimated the indirect effects of A-CHESS on drinking outcomes, as mediated by post-discharge service utilization. RESULTS Approximately 50.5% of participants reported outpatient addiction treatment and 75.5% reported mutual help at any follow-up interview in the year following randomization. Assignment to the A-CHESS intervention was associated with an increased odds of outpatient addiction treatment across follow-ups, but not mutual help. This use of outpatient addiction treatment mediated the effect of A-CHESS on risky drinking days, but not abstinence. The effect of A-CHESS through outpatient addiction treatment appeared to reduce the expected number of risky drinking days across follow-ups by 11%. CONCLUSIONS The mobile health (mHealth) intervention promoted the use of outpatient addiction treatment, which appeared to contribute to its efficacy in reducing risky drinking. Future research should investigate how mHealth interventions could link patients to needed treatment services and promote the sustained use of these services.