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Supplements The Aligning Forces for Quality Initiative: Early Lessons From Efforts to Improve Healthcare Quality
Creating and Sustaining Change: Early Insights From Aligning Forces
Claire B. Gibbons, PhD, MPH; and Anne F. Weiss, MPP
Getting the Structure Right for Communitywide Healthcare Improvement
Gordon Mosser, MD
Lessons for Reducing Disparities in Regional Quality Improvement Efforts
Scott C. Cook, PhD; Anna P. Goddu, MSc; Amanda R. Clarke, MPH; Robert S. Nocon, MHS; Kevin W. McCullough, MJ; and Marshall H. Chin, MD, MPH
The Imperative to Promote Collaborative Consumer Engagement: Lessons From the Aligning Forces for Quality Initiative
Debra L. Ness, MS
That Was Then, This Is Now
Lisa A. Simpson, MB, BCh, MPH, FAAP
Regional Health Improvement Collaboratives Needed Now More Than Ever: Program Directors' Perspectives
Randall D. Cebul, MD; Susanne E. Dade, MPA; Lisa M. Letourneau, MD, MPH; and Alan Glaseroff, MD, ABFM
The Aligning Forces for Quality Initiative: Background and Evolution From 2005 to 2012
Dennis P. Scanlon, PhD; Jeff Beich, PhD; Jeffrey A. Alexander, PhD; Jon B. Christianson, PhD; Romana Hasnain-Wynia, PhD; Megan C. McHugh, PhD; and Jessica N. Mittler, PhD
Barriers and Strategies to Align Stakeholders in Healthcare Alliances
Larry R. Hearld, PhD; Jeffrey A. Alexander, PhD; Jeff Beich, PhD; Jessica N. Mittler, PhD; and Jennifer L. O’Hora, BA
The Aligning Forces for Quality Initiative: Background and Evolution From 2005 to 2012 - eAppendix
Midterm Observations and Recommendations From the Evaluation of the AF4Q Initiative
Jeffrey A. Alexander, PhD; Dennis P. Scanlon, PhD; Megan C. McHugh, PhD; Jon B. Christianson, PhD; Jessica N. Mittler, PhD; Romana Hasnain-Wynia, PhD; and Jeff Beich, PhD
Producing Public Reports of Physician Quality at the Community Level: The Aligning Forces for Quality Initiative Experience
Jon B. Christianson, PhD; Karen M. Volmar, JD, MPH; Bethany W. Shaw, MHA; and Dennis P. Scanlon, PhD
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Community-Level Interventions to Collect Race/Ethnicity and Language Data to Reduce Disparities
Romana Hasnain-Wynia, PhD; Deidre M. Weber, BA; Julie C. Yonek, MPH; Javiera Pumarino, BA; and Jessica N. Mittler, PhD
Evaluating a Community-Based Program to Improve Healthcare Quality: Research Design for the Aligning Forces for Quality Initiative
Dennis P. Scanlon, PhD; Jeffrey A. Alexander, PhD; Jeff Beich, PhD; Jon B. Christianson, PhD; Romana Hasnain-Wynia, PhD; Megan C. McHugh, PhD; Jessica N. Mittler, PhD; Yunfeng Shi, PhD; and Laura J. B
Using Websites to Engage Consumers in Managing Their Health and Healthcare
Jessica N. Mittler, PhD; Karen M. Volmar, JD, MPH; Bethany W. Shaw, MHA; Jon B. Christianson, PhD; and Dennis P. Scanlon, PhD
Participating Faculty: The Aligning Forces for Quality Initiative: Early Lessons From Efforts to Improve Healthcare Quality at the Community Level
Letter From the Guest Editor
David Blumenthal, MD, MPP
Samuel O. Thier Professor of Medicine and Professor of Health Care Policy Massachusetts General Hospital/Partners HealthCare System and Harvard Medical School, Boston

Community-Level Interventions to Collect Race/Ethnicity and Language Data to Reduce Disparities

Romana Hasnain-Wynia, PhD; Deidre M. Weber, BA; Julie C. Yonek, MPH; Javiera Pumarino, BA; and Jessica N. Mittler, PhD
For alliances with racially and ethnically diverse populations, targeting disparities was generally supported by their partners and the broader community, but getting buy-in to specifically get providers to collect REL data as a starting point was still often a hard sell to community organizations representing minorities. In part, these community organizations saw REL data collection as myopic or misguided, since they felt REL disparities in health outcomes stemmed from sources other than differences in physician and hospital care. As an alliance disparities staffer summarized:

“We’re tending to focus on the provider end, but there is a whole community component that’s going on around disparities and we need to look at that. If we started to collect REL data without the larger community understanding why—the leaders of the black community, the church leaders, the politicians—that could be misunderstood. We have to lay groundwork of ‘disparities exist and there are some things we need to do (ie, REL data collection) to get a handle on them.’”

Some AF4Q alliances were able to build constructive partnerships with community organizations on the topic of disparities, and then use this to leverage buy-in on REL data collection activities:

“We [the alliance] are working with a community-based African American organization. They provide an outside lens for the REL work. They were really helpful in dispelling myths about HIPAA [Health Insurance Portability and Accountability Act] violations for collecting data and laying the groundwork preparing the community that this is coming.”

Theme 3: Hospitals Can Be an Entry Point for Collecting REL Data

Alliance staff have struggled with how to address individual providers’ perceptions that disparities are not an issue for them. They report that these views pose a barrier in securing commitment from provider organizations to collect REL data. However, among providers, the majority of alliance directors and staff found hospitals to be willing partners in collecting REL data: at the time of the interviews, 10 of the 14 alliances had actively engaged with the hospital community around data collection and the other 4 had started a dialogue.

Alliance directors and staff generally viewed hospitals as an easier sell on this topic compared with physician practices. Some thought that this may be because hospitals have an infrastructure for collecting data and engaging in quality improvement. One alliance leader said:

“Hospitals were much easier to work with than ambulatory sites because of resource issues. Even if the will and desire is there [for practices], the resources are not necessarily sufficient.”

A small number of AF4Q alliances were using some “early adopter” hospitals to propel data collection activities on both the inpatient and ambulatory side:

“The strategy we (the alliance) created was to bring hospitals on board by focusing on early adopters—those hospitals that are interested in health disparities. We had a number of fairly high-level people say that we want to do this as a community. The early adopters were used as advocates. We had the support of the board chair.”

One reason why hospitals may be promising early adopters has been the work of hospital learning collaboratives in AF4Q communities, such as the Equity Quality Improvement Collaborative (EQIC)16 and the Hospital Quality Network (HQN).17 These programs have provided a degree of momentum and opportunities for learning about systematically collecting REL data within hospitals and expanding this knowledge into the community. All alliances with hospitals that participated in collaboratives mentioned leveraging the lessons learned:

“The EQIC grant made use of an effective resource in terms of training hospital registration folks on collecting data and presenting materials to educate folks on the hospital side. We’re making headway because we have 11 hospitals in [the] HQN, which requires REL data collection. Most of the hospitals in the HQN are in the area of the state with disparities. So we think we’re going to get a lot accomplished through the HQN work.”

“We have our local initiative which builds off an existing model for collecting REL data [that is part of the] Expecting Success [project].18 We have had significant communitywide training around the standardized collection of REL data. We are starting to look at the readmission data relative to REL. While we’re retraining on the inpatient side, we’re also beginning to do some training in the ambulatory setting.”

Although generally positive, some alliances have experienced tension working with hospitals in a learning collaborative. Alliance directors reported finding it difficult to engage these hospitals because they were already committed to learning collaborative activities which were not necessarily synchronous with the AF4Q alliance activities. As an alliance director stated:

“It has been a significant challenge in our HQN work. We have had significant barriers trying to get information about what’s going on with our hospitals in the HQN. We can’t get access to the data. We are responsible for keeping the hospitals engaged, but we are operating in the dark.”

Theme 4: Physician Practices Pose Unique Challenges

Alliance directors and staff expressed that it takes a lot of effort to engage physician practices around issues related to disparities, particularly data collection. This may be because practices have a variety of work cultures and use a wide range of data management systems. Many smaller practices also lack infrastructure (eg, electronic health records) that can serve as a tool to support data collection. Typical alliance leader comments included:

“Well, we (alliance) did a survey of physician practices and REL data are just not available. There’s more confusion and lack of consistency in collecting REL [data] in medical practices.”

Alliance staff found that physicians believed they knew their patients and treated them the same, and that they would not provide lower-quality care to their minority patients. Alliance staff worried that discussing disparities with physician practices seemed like an attack on their professionalism:

“When we talk to the medical groups, they don’t really understand the value in collecting REL [data]. We’re going to need resources to help them understand why it is important to collect. The conversation with physicians has to be about improving healthcare broadly. Physicians will become extremely defensive if the only conversation is around health disparities and health equity.”

There was also uncertainty about the utility of the data and whether there would be sufficient numbers for analytical purposes. Alliance staff conveyed that physician practices need to see models that use the data to implement measurable improvement and show a return on investment. An alliance director stated:

“I would bet that perhaps many physicians don’t even think that there’s a problem with equity. We need to provide them with whatever it is they need to improve their focus and improve their practice—whether it’s best practices that come from within the area or by connecting them to projects from outside the area.”

However, those alliances working with physicians in Federally Qualified Health Centers (FQHCs) found important opportunities for advancing the AF4Q initiative equity agenda. Alliances that were working with FQHCs found that these practices were successfully collecting REL data and using it to target quality improvement and disparities-reduction activities. One reason for this success could be that  alliances working with FQHCs did not struggle with physicians in these settings, and that physicians acknowledged that disparities may exist within their practices.

Theme 5: Health Plans Are Currently Not a Primary Source of REL Data

Very few alliances were working with health plans to capture REL data. In general, few health plans were thought to be collecting REL data. Alliance directors and staff found it difficult to obtain this information even if plans reported having it.

“We interviewed health plans and the only time they collected REL [data] was for special projects and for health risk assessment projects. They just don’t have the capacity to collect the information and store it where it is easy to retrieve.”

Some alliances primarily focused on Medicaid plans to obtain REL data because these plans were more likely to have this information. However, some alliance directors acknowledged that state budgetary constraints were making extraction of this information difficult.

Theme 6: Federal Initiatives Can Be Facilitators and Distracters

We asked alliance directors and staff about the impact of the federal meaningful use criteria and incentives for collecting REL data in electronic health record systems. Their perceptions varied in regard to the benefits of such drivers; many found them to be a positive force, but a few viewed them as negative. Comments included:

“We might have had to push harder to get hospitals and doctors to come along without the federal initiatives. We’re working through meaningful use with the doctors in the field so we are there where we can help and train them.”

“The AF4Q initiative is a good example that reform is local. We continue to look for ways that we can work collaboratively with the Regional Extension Center on the data collection side, and we’re (alliance) taking advantage of the situation to say we all know that REL collection is something that we’re going to have to do.”

A few alliance directors found the federal drivers to be distracters. A director captured this sentiment:

“Meaningful use has created a distraction and a lot of noise. It’s heightened some of the competitive tension for the hospital systems.”

Discussion

Our qualitative results suggest that working with many stakeholders to address local disparities is a challenging task. Getting them to rally around collecting REL data as a first step in reducing disparities in care, as described by Kilbourne et al, is even more challenging.7 Fortunately, the alliances’ experiences provide several helpful, early insights on how one might improve the strategy of building local systems to collect data to detect disparities.

First, an initial focus on collecting REL data can work if some early adopters are on board and can provide useful models with local credibility. The AF4Q alliances that have made substantial progress have facilitated communitywide dialogues, led by organizations with local experience, about the importance of having these data to detect, monitor, and reduce disparities in care.19 However, even in these communities, providers are often unsure about the utility of collecting REL data, and many do not believe that the most important disparities in their communities are race-based, reinforcing findings from other studies.2,20 These concerns cannot be dismissed, as evidence shows that disparities are multifaceted and driven by many factors.21 However, racial and ethnic disparities remain pervasive, even when accounting for factors such as socioeconomic status, necessitating systematic REL data collection.1

 
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