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Equity Data Monitoring, Display, and QI Initiatives

Learning Objectives:
1. Identify wins, opportunities, and barriers/challenges around using health equity data for patient quality and experience at your institution.
2. Identify and understand a health equity framework and how to implement it with a governance structure to impact care at your institution.
3. Describe an example of how to utilize health equity data to identify inequities and provide targeted interventions.
0.25 Credit CME

This video is an excerpt from the AMA Advancing Equity through Quality and Safety Peer Network session on Data Monitoring, Display, and QI Initiatives. This section describes the framework and process by which Brigham and Women's Hospital collected equity data to inform quality improvement initiatives at their institution.

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Video Transcript

Esteban Gershanik: [00:29] As we know from our framing, about two decades ago, we had medicinal pillars of quality, which included the safe, effective, patient centered, timely and efficient pillars, and the often-forgotten equity—or equitable—pillar.

[00:42] About a decade later, through the Affordable Care Act, we had the ACA requirements to establish priority neighborhoods. Every three years you do a Community Health Needs Assessment for your community. We did this at our institution this past year. Most recently, this year and last year, we have the US News & World Report health equity scores coming out. At our institution, we saw that we were behind another institution in our hospital service area.

[01:14] To address our healthcare equity domain, we use the framework that Pam, Karthik, and others wrote. Pam, would you like to explain the work and the tiers?

Pam: [01:25] Karthik and I, a few years ago, wrote up a framework to establish measures that will advance equity. There are four tiers here, and this is the measurement framework that Brigham and Women's uses and the framework that Esteban will be going over.

[01:43] In short, there are four areas within which metrics should be established, which includes access transition quality, and socioeconomic/environmental impact. The data that we usually look at, we focus on this third level here, focused on clinical quality measures.

Pam: [02:05] Then we started thinking: You could have the best outcomes, readmissions, or other downstream quality measures, but if we're only serving a wealthy community, is our health system equitable? For example, a more affluent hospital may look like they're providing higher quality care using these clinical measures, but they might be creating barriers to access for marginalized patients.

[02:32] When we think about equity as a framework, we want to look a little further upstream and first look at access. Can historically disadvantaged patients even get into our health system? And if they get kicked in at the second level, are they getting equitable treatment?

[02:51] You heard Michelle Morse and Bram Wispelwey talk about inequitable access between general medicine versus specialty care in cardiology. That's really around transition.

[03:04] Then we look at clinical measure. Lastly, we want to look at neighborhoods and how the health system is impacting the socioeconomic and environmental factors in their community. Those are the four levels that we look at when we think about measures. I'll pass it back to Esteban and you can go through the specifics in terms of how the Brigham uses this framework.

Gershanik: [03:28] Thanks, Pam. We'll go through the slides quickly. We created a health care equity domain team where our purpose was to advance racial justice and equity in healthcare delivery through high performance quality and safety practices. This domain team was integrated with the hospital strategy and goals. That article that you saw before, that was also coauthored not just by Karthik and Pam, but also our Chief Quality Officer at the time, Andrew Resnick, who's one of our faculty, and Sonny Ethan, who was our CMO and president at the time.

[03:55] We incorporated and integrated the aspect of our equity domain team into service-, science-, and unit-based teams in order to improve our clinical outcomes. And, as some of you discussed in the chat, it's aggregating your data to sort of see how that works.

[04:08] If you go to the next slide, you can see an evaluation of our governance structure, where we wanted to identify evidence-based practices into our standard operating procedures. For each of these domain teams, we would go up to our quality operations government, and then our hospital governance with senior management with our CEO, CMO, and our board of directors that we would report some of these findings to. Next slide.

[04:33] We have 40 service lines that we would address. I know each of our different hospitals have different service lines. We were incorporating or integrating this equity domain team into each of these service lines with—next slide—an aim that we establish a charter together and collaboratively with members of each service line, experts of our equity initiatives around the hospital and community, along with senior leadership and sponsorship.

[04:59] We aim to advance racial justice and equity in our healthcare delivery at Brigham Health. Our main goals at this time were to inform strategies, establish near- and long-term priorities, and set targets and goals that we could execute each of these unit-based teams or service lines to achieve improved and equitable clinical outcomes. We started to say, "Hey, what we what would be our first initial year-one priorities?" And these were some that we established:

[05:25] First, understanding who do we serve—that access piece that Pam discussed? And measuring some of the basic demographics that we could establish for our inpatient and outpatient populations.

[05:35] Establish additional standardized equity measures, whether it's insurance status, poverty status, or ability. Then, we make sure for each of these service lines that we could incorporate who they are seeing inpatient and outpatient, disaggregate their data, and identify a quality measure for each of these service lines where they can disaggregate this.

Gershanik (continued): [05:55] Also through our joint commission work, we recognize—from a nursing standpoint— that there are four core measures that we had to assess: critical test results reporting, pain assessment and reassessment, suicide prevention, and restraint use. We broke that down demographically—along with articles that were done and work that was done—to look at disparities and inequities that we had in all four of these measures. I think we highlighted some of the restraint newsfeeds.

[06:21] One way that we wanted to look at this is: what is the neighborhood that we should be serving? First, from the community health needs assessment, we looked at our priority neighborhoods for our community health needs assessments. We also looked at some of the definitions around the U.S. News & World Report for the hospital service area, along with the hospital referral region.

[06:40] As an institution that sees people across the country and internationally, we want to know: Who is our population, and who are we serving? We've tried... we've delineated this and wanted to identify this, not just for us as an entire population, but also for our service lines and our outpatient/inpatient and emergency department locations.

[07:01] To go through our demographics for us, this was just a first step. It was a bigger step than I anticipated, but it's a good understanding of who we serve. One of the things that we recognize, we looked at unique patients— not how many times they came through our hospital, but unique ID percentages—and we compared it to our priority neighborhoods, our hospital service area, and our hospital referral region.

[07:23] What we found for the patients who were coming through our hospital, our ambulatory clinics, and ED, that they're primarily patients that were more white and more English speaking than our hospital service area. Our priority neighborhoods—as can be highlighted and seen here—are Black, Asian, Latinx, Hispanic, as well as limited-English-proficiency patients were underrepresented in the patient population.

[07:44] That was just a first step. If you just look at what we're capturing in the data, we are at the bottom around language compared to what our priority neighborhoods are. It's a huge difference. Who should we be serving and providing access to, and who do we serve? Next slide.

[07:59] The next question we asked was, "Who's coming through our emergency department? Who are we seeing in the ambulatory space? And who are we seeing inpatient?"

[08:08] Similarly, we saw disparities here. The majority of patients that are seen in our ambulatory care clinics were white and commercially insured, whereas the majority of patients being seen in our emergency department, we're not white or commercially insured. Their access to care was utilizing the emergency department.

[08:24] Not only that, but we also wanted to capture how much of the other unknown rates were collected. We saw that this is always high at all of our locations. There's a lot that's not filled in. We recognize that that number was higher when it came to our emergency department, and some of our inpatient space versus the outpatient space. We wanted to recognize where we had deficiencies in capturing some of that data.

[08:45] Then the other aspect is: "Who's seen in your ambulatory clinic versus who's getting the referrals for some of that specialty care?" We know there's disparities around referrals and some of this. Similarly, we saw that, in our primary ambulatory care clinics, we had more Latino, non-English-speaking, and Medicaid patients than we had in our specialty referral clinics.

[09:04] They sometimes see that as a structural or institutional barrier with payers and so forth and so on. In our subspecialty clinics we had more white, English-speaking, and commercially insured patients versus our ambulatory space. In breaking this down, just as a baseline, it identified to us that there are differences between who we should be serving, who we want to serve, and who we actually are serving and creating some of those disparities and understanding those things.

[09:29] We also then created a dashboard to look at our ambulatory care access. We could see in real time—or if we want to look over a year—what was the race, age, ethnicity, language, and payer of these patients... We also broke this down comparing virtual versus in-person, knowing that during the pandemic, we had a high increase in our virtual care visits. We wanted to see how that affected the impact around the inequities of access.

[09:56] Furthermore—next slide—we also were able to take a look at this through different aspects of those virtual visits that we talked about and whether it was by phone, by video, or other circumstances. We could capture that, while understanding what the modality was that best serves these communities.

[10:16] Lastly—I know we talked about this briefly before, but because in the next slide, we also looked at and added recently—the dismissals from our different practices and service lines based on race, ethnicity, age, and seeing how that's changed from fiscal year 2021 to 2022.

[10:32] Things that ask that question around access: Are we being equitable in the access of care we provide? Are we dismissing folks at an equal or equitable rate and why or why not? When you go to each of these locations, you can see there's a difference. But we can also see what the end—or what the number of patients are—as well, to understand what the dismissal looks at based on the number of people we're also seeing. Then we see if there's an issue that correlates with that.

[10:58] In summary, just to capture that access piece, we recognize that our patient populations are more white, more English speaking than our hospital service area and our priority neighborhoods that we designated through our Community Health Needs Assessment.

Gershanik (continued): [11:09] Many Black, Asian, Latinx, and limited-English patients were underrepresented. The majority of patients seen in our ambulatory care were white and commercially insured, along with our subspecialty clinics, whereas our Emergency Department was more not-white or commercially insured. Also, we best collected the other unknown piece in the ambulatory setting versus the inpatient emergency department setting, and that there were some demographic differences in our practices.

[11:34] With the next year, of course, what we have is transitions, there's a nice little highlight. One of the things that we looked for when it came to transitions of care—I know that Bram and Reagan talked previously about our heart failure work—seeing that once people came into the emergency department, they were being transitioned to our general medicine service instead of to our cardiology service, and there were disparities in equities around that.

[11:58] We're looking at our institution and are capturing the numbers based on race, ethnicity, payer, and other dynamics as to: Who is being transitioned to what services? Who's in the hallways versus in rooms in our emergency departments? Also, we're now looking at: When we discharge people from our inpatient services, where are they going to and there are there any disparities between whether they're going home, whether they're going home with services, whether they're going to Eltechs, or snaps.

[12:24] On the bottom, you have some of the work that we're doing right now. Looking at some of those differences because, again, if we don't measure it, we don't value it. And if we don't measure it, we can't capture it and understand what's going on either.

[12:37] Furthermore, when it comes to the next year of quality of care—there's the highlight... I feel like we need background music for that—we're also looking at all of our quality measures and breaking it down demographically. This is just one example of mortality that we're breaking down by race, gender, ethnicity, and language. And we see disparity.

[12:56] We're also doing this for quality CLASBIs, and happiness. Knowing that... we're reporting all these measures. And knowing that often when it comes to happies, the traditional education that goes into assessing them is for white skin versus darker skins, and adding the educational piece on that.

[13:09] Furthermore, we're also— as mentioned previously in our March meeting—we're having each service line come with a specific quality initiative. For the lactation services, they are looking at breastfeeding. They originally were reporting intent and exclusivity of breastfeeding from our OB service, lactation service. At first it was one number, but we said, "Hey, let's break this down to demographically."

[13:34] What we saw that for our white women it was 55%, Black was 35%, English-speaking the Hispanic: 30%, and non-English-speaking: 15%. We said, "Hey, there's a disparity here. There's an inequity there." Not only that, but there's folks who were intending to but couldn't.

[13:51] Just breaking down the information demographically added our understanding of the inequity that was for this quality measure. We're doing this for all our service lines. Next slide.

[14:02] What we did is we said, "Hey, let's establish a Mama Sana program where we ended up hiring a Spanish-speaking Latina woman as our lactation consultant, and adding a service to help direct Spanish-speaking initiatives around this. We built partnerships with outpatient community resources like Stronger Generations, Baby Care, and the Boston Breastfeeding Village, because the care we provide for our patients is before they come into hospital, when they come into our hospital, and when they go out. Bringing that connectivity even closer has helped build a stronger initiative into addressing some of these inequities, we saw this quality measure.

[14:34] Furthermore, this is the New England Journal Catalyst that came out this past year, this is an experience at Inner Mountain that showed their dashboard that they utilize through Vizient—that I know many of us are partners with—and breaking down some of their quality and disaggregated data with clinical outcomes that they do and I'm happy to share that after the conversation.

[14:55] Lastly, the last piece is that socioeconomic environmental impact that we can discus. Now with this, we're working with our Vizient partners and also other identities of indices are looking at the Area Deprivation Index and other indices. Next slide.

[15:10] These are a variety of socioeconomic and environmental factors that we can look at different indices and as an equity domain decide, "Hey, which one of these captures the social determinants of health that Dr Akinbola talked about, that impact some of the access and other issues in that tiered framework.

[15:27] In addition to that patient experience that Nadia is leading with Amir, Ahmed, and other folks in our institution, has also sort of been a highlight of how we break down this data as well. Next slide.

[15:44] I know many of us have adopted the NRC dashboard for our patient experience. You can disaggregate the data by age, language, marital status, and race. Next slide.

[15:59] We wanted to know, "Hey, what are the demographics of reporting that goes through our NRC dashboard, and what goes through the people who call our Patient Family Relations Department for certain complaints for a year?" We wanted to break this down, again, by age, gender, the neighborhood that people live, the language, and the race.

[16:19] Just one of the things we saw was that over 330 000 responses—which is only 20% of the response rate when we send out NRC—came through. Through our patient family relations, we had up to 8600 concerns by 4600 patients.

[16:34] Who are these people that are responding to our patient experience? They're primarily white and English-speaking. So, our non-white and non-English speaking patients are underrepresented, and some of the initiatives that Amir, Abed, Nadia, and our group is doing is now creating workgroups to understand how we can better reach communities that we don't hear from.

[16:54] Then the question is: Us hearing from them... what do we hear? One of the things that we did with our initial NRC data was we created that Tableau dashboard, to better see how this works around service line, going into the specific questions, whether we're at baseline below or above... Next slide.

Gershanik (continued): [17:13] We also broke this down by race. Seeing that for our institution, the responses here are in the light blue. The blue shows the volume of responses by white people versus non-white, which is the orange. What we saw was that the rates were higher for our white patients. And for our Black patients, when it came to satisfaction scores... Our white patients were having a better NRC score than baseline or national standards, whereas our Black patients were below.

[17:40] We also said, "Hey, what would it look like based on language?" On the next slide, we broke it down by language, again, disparity seen there, their responses were less.

[17:50] Lastly, we had specific units looking at this for each specific question that you can get from NRC. On next slide, we look specifically in our Medicine Unit: "Hey, what was the difference between our Spanish-speaking patients versus our English-speaking patients? For each of the questions that are asked, we saw that English speaking patients rate their experience more positively than our Spanish speaking patients by a long margin when it comes to came to our medicine units. Particularly, around the areas of whether they knew what to do after their hospitalizations, courtesy and respect, and feeling safe.

[18:23] Furthermore, we saw that for our priority neighborhoods that were meaning to serve that they had worse NRC scores. Next slide.

[18:31 ]Then when we asked the question... how about folks with disabilities system-wide? We did a survey to look at patients who are greater than 65, who is hearing, visual, physical, or mobility disabilities. They also had a worse experience. Patients of color with disabilities had an even worse experience.

[18:53 Lastly, but not least, with the patient family relations piece, we also broke this down by white versus non-white, English- versus non-English speaking, and saw that patient-family relations were often engaged with patients of color, or non-English speakers when they dealt with communities, customer service, belongings, or behavioral patient management versus serious reportable events.

[19:13] Whereas our white patients engage more with our patient family relations, and English speaking patients did as well when it came to building issues, responsiveness issues, or giving a compliment. Great disparities there.

[19:25] Overall, what I'm trying to show here—and what I'd love to discuss with everyone, and again, this is a comprehensive effort through the work that Reagan, Nadia, and I, along with Karen and Karthik and Pam establishing that framework—is that... We incorporate a scene in March that equity-informed high-reliability framework to capture discrimination in bias, seeing that once you capture it and increase and some of the themes that we saw once we captured this.

[19:50] From quality, knowing that we have differences around access to care, transitions in care and around quality measures when we once we break this down demographically. From an experience standpoint, different patients have different experience, and we're hearing from different patients.

[20:04] To have a standard around how we disaggregate this data to where it lines up with those requirements federally, and for our institution is key along with putting it in the hands of people so that something can happen, and it can be acted upon.

[20:19] Furthermore, there are new quality measures coming out. Many of you know this from the U.S. News & World Report, they just came out with their most recent efforts around equity scores. The Loan Institute's CMS, World Health Organization, Fortune, IBM, Watson Health, top 100 hospitals.

[20:33] On the next slide, this is to cover some of these national measures, CMS in April reported out their proposed policies that advance health equity and maternal health and support hospitals, the CMS framework for health equity that included... that show those five priority areas where CMS is looking to design, implement, and operationalize policies and programs to support health for all people served by their programs.

[20:55] The CMS Innovation Center has five strategic objectives for advancing system transformation. Primarily, they're highlighting health equity, and a framework where again, similar to the work that we've done, that's breaking down a lot of these quality measures demographically to identify some of these inequities.

[21:11] And so with that... this is a huge problem that we're all addressing here, but where there's problems and issues, there's opportunities for us to improve and that's the goal of what we're doing here. If you're networking with one another, and learning from one another, and if we're able to do this together, we can make a real difference in the patients we're trying to serve.

Video Information

CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships.

If applicable, all relevant financial relationships have been mitigated.

AMA CME Accreditation Information

Credit Designation Statement: The American Medical Association designates this Enduring Material activity for a maximum of 0.25  AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to:

  • 0.25 Medical Knowledge MOC points in the American Board of Internal Medicine's (ABIM) Maintenance of Certification (MOC) program;;
  • 0.25 Self-Assessment points in the American Board of Otolaryngology – Head and Neck Surgery’s (ABOHNS) Continuing Certification program;
  • 0.25 MOC points in the American Board of Pediatrics’ (ABP) Maintenance of Certification (MOC) program;
  • 0.25 Lifelong Learning points in the American Board of Pathology’s (ABPath) Continuing Certification program; and
  • 0.25 credit toward the CME [and Self-Assessment requirements] of the American Board of Surgery’s Continuous Certification program

It is the CME activity provider's responsibility to submit participant completion information to ACCME for the purpose of granting MOC credit.


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