Treat-to-target in RA: Collaboration To Improve adOption and adhereNce

Overview

The purpose of this study is to determine if a Learning Collaborative is an effective tool to improve adoption and adherence to a Treat-to-Target (TTT) treatment strategy in U.S. rheumatology practices for the treatment of Rheumatoid Arthritis (RA). The TTT strategy has been embraced by the American College of Rheumatology through the RA Treatment Guidelines and by EULAR,however several lines of evidence suggest that TTT is not practiced consistently across rheumatology settings.Participating practices will be randomized to receive the Learning Collaborative intervention in one of two phases. Coaching consistent with Learning Collaborative practices will be used to promote adherence to TTT. Chart abstraction will be used to determine if the Learning Collaborative was an effective tool to increase TTT adherence.

Twelve rheumatology practices around the U.S. will be recruited to participate in a Learning Collaborative (LC). Eligible practices will have at least 50 rheumatoid arthritis patients, at least 2 rheumatologists, and an electronic medical record or typed notes. Each practice will be randomized to receive the intervention in either phase 1 (first 9 months) or phase 2 (second 9 months). The collaborative will consist of a series of 9 Learning Sessions (1 conducted face-to-face, the remaining conducted via webinar) as well as regular coaching sessions and collaborative sessions. The structure of the collaborative will be such that each learning session will focus on resources, expertise, and best practices that address common barriers to Treat-to-Target (TTT) adoption. The follow-up coaching and collaborative sessions will give providers the opportunity to reflect on what they have learned from their own practices and from each other. The first Learning Session was a one-day face-to-face meeting that consisted of orienting the teams to the Model for Improvement, describing the Change Package and its content, conducting team building activities focused on developing ideas for plan-do-study-act (PDSA) cycles, and cross-team learning activities. (The PDSA cycles refer to tests of change, using four stages, that are performed as part of a quality improvement process.) The day primarily consisted of discussion sessions, and several lectures on TTT, disease activity measures, and shared decision making helped orient teams to the Change Package contents. There was ample time for teams to get feedback from expert faculty on their proposed tests of change and results to date. Subsequent Learning Sessions were conducted via webinar. We developed a web-based collaborative tool for the Learning Collaborative. It helped manage contents being shared across teams (i.e., key resources, PDSAs), displayed monthly improvement metrics, and provided a discussion board with conversation "threads." The tool was used in all Learning Sessions. Specific patient data will never be identified in the sessions of the Collaborative, and no specific patient data will be shared between the participating providers. Providers will be able to interact with each other to share general practice patterns and behaviors, but sensitive patient information will not be shared. The intervention will be conducted in 2 phases. In phase 1, a group of 5practices \[Cohort 1\] will initiate the collaborative, which will last 9 months. After 9 months, phase 2 will begin with the remaining practices \[Cohort 2\] initiating the second collaborative of the same content as cohort 1, and the second collaborative will also take 9 months to complete. The cohort 1 and cohort 2 collaborative groups will not have any interaction with each other, but cohort 2 will serve as a concurrent control to cohort 1 in the first 9 months of the intervention. De-identified data will be collected from participating sites to compare TTT practices prior to and following the Learning Collaborative intervention using a chart review abstraction tool. The tool has four items: 1) documentation of a treatment target; 2) documentation of shared decision making; 3) documentation of a disease activity measure; and 4) evidence that this information guided treatment decisions. This tool will be used to grade the visit in the two months immediately prior to the start of Phase 1 and the visit in the two months immediately prior to the end of Phase 1; a change score between baseline to follow-up will be calculated. The range of change in implementation of TTT can vary from -4 (worsening from 4 at baseline to 0 at follow-up) to +4 (improvement from 0 at baseline to 4 at follow-up). Thus, the range of change scores will be from -4 to +4, a 9-point ordinal scale. The baseline visit will be considered the patient visit within two months before the start of Phase 1 (January 2015). If there are multiple visits in this time-frame, then the note for the visit most proximal to January 2015 (start of the study) will be assessed. The end of Phase 1 visit will be considered the visit within two months before November 1, 2015. Again, if there are multiple visits in this time-frame, then the note for the visit most proximal to October 1, 2015 will be assessed. When assessing the performance at each site, we will randomly sample the medical records of patients with RA who have visits documented within these two time-frames. Surveys will also be collected from patients and providers to assess satisfaction with the patient-provider interaction and shared decision-making. RA patients were randomly selected at each site to complete a questionnaire rating their satisfaction with the shared decision-making process using the three item collaboRATE scale. This was carried out at the start of Phase I and will be re-assessed at the end of Phase 1. Similarly, we asked providers involved in the Learning Collaborative from all sites in both groups to complete a modified version of this questionnaire; this will also be re-assessed at the end of Phase 1. During Phase 2, the only outcomes we plan to assess are the primary outcome of TTT implementation as well as the patient collaboRATE scale. The primary analysis will compare the primary outcome among the Learning Collaborative sites with the control sites. The mean change in implementation of TTT for the Learning Collaborative arm will be compared with implementation of TTT for the control arm after accounting for intra-cluster correlation using linear mixed models. Although the normality assumption may be violated when the outcome variable is ordinal, linear mixed models should still be valid for the proposed sample size. Treatment arm will be the exposure of interest. Covariates included in the model will include provider-level characteristics (such as age, gender, training), patient-level characteristics (age, gender, baseline disease activity, baseline RA drugs), and other covariates found to be unbalanced at baseline. While these characteristics should be balanced given the random assignment to treatment arm, the small number of centers in each arm opens the possibility of baseline differences and thus the rationale for adjustment. Similarly, for the secondary outcomes (dichotomous variables), we will use generalized linear mixed models for binary outcomes. The trial has been powered based on the primary outcome - the estimated difference in change in TTT implementation between the Learning Collaborative intervention and the control sites. Several other assumptions underpin the sample size estimation. First, the control group would have no or only small change (0-5%) change in implementation of TTT compared with a change in the intervention group of 20-40%, an improvement level observed in a similar prior trial using a Learning Collaborative. Second, we will include 5 sites in the intervention group, and 6 in the control group. We assume that average number of providers in each practice is 5, and expect there would be substantial intra-cluster correlation (ICC) among patients within a given provider. We conservatively assume a range of ICC is 0.1-0.3 based on prior work. Third, the significant level (alpha) would be two-sided 0.05, and the goal power would be 80%. Based on these assumptions, we estimated sample sizes for the proposed trial. The required number of patients per provider needed to detected meaningful differences was calculated for each set of assumptions. Based on these estimates, we will review a random 6 patients per provider with eligible visits to ensure an adequate sample size to achieve 80% power.