Evidence map›Paper›PMID 21524303›Full record

ArticleImplementation science : IS2011

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

Andrew R Quanbeck, David H Gustafson, James H Ford, Alice Pulvermacher, Michael T French, K John McConnell, Dennis McCarty

Registry-linked trialAbstract readMulticenter Study
In one paragraph

Article in Implementation science : IS, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00934141 (Randomized Control Trial), which is not on this map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT00934141 phase3completednot on this map

Randomized Control Trial (RCT) Evaluating Improvement Strategies in Addiction Treatment

TypeinterventionalSponsorUniversity of Wisconsin, MadisonRan2006 to 2011Enrolled201ConditionsAddictionArmsLearning Session, Interest Circle Calls, Coaching, Website
3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Interventions to reduce waiting times for elective procedures.The Cochrane database of systematic reviews · 2015
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  19. What Influences Participation in QI? A Randomized Trial of Addiction Treatment Organizations.Journal for healthcare quality : official publication of the National Association for Healthcare Quality
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Andrew R QuanbeckCenter for Health Enhancement Systems Studies, Industrial and Systems Engineering Department, University of Wisconsin-Madison, Madison, WI 53706, USA. andrew.quanbeck@chess.wisc.edu
David H Gustafson
James H Ford
Alice Pulvermacher
Michael T French
K John McConnell
Dennis McCarty

Funding

RCT Evaluating Improvement Strategies for Addiction TxR01DA020832 · NIDA · UNIVERSITY OF WISCONSIN-MADISON · PI GUSTAFSON, DAVID H · 2006 to 2010
$7.3M
NIDA NIH HHS R01 DA020832
6 · The paper itself

Abstract

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. OUTCOMES: The 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 organization's success in making changes. ANALYSIS: We are fitting a mixed-effect regression model to each program's 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. DISCUSSION: The 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.

Indexed as

Organizational InnovationOutcome and Process Assessment, Health CareQuality ImprovementRandomized Controlled Trials as TopicResearch DesignCluster AnalysisCost-Benefit AnalysisCounselingHumansIntention to Treat AnalysisInternetRegression AnalysisSocial SupportSubstance-Related DisordersTelephoneUnited States

Identifiers

PMID21524303
PMCPMC3108336

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.