As part of the growing concerns with healthcare quality, organizations are focusing more on identifying and addressing disparities in the care they provide. Establishing an effective data management program to gather metrics to identify vulnerable populations, prioritize opportunities, and measure the effectiveness of mitigation efforts is essential in addressing the question, “Are we being equitable with our healthcare? How do we know, and do we have the data to validate our answer?”
Data provides validation on your identification and prioritization of at-risk groups and the health-related social needs that most affect your patient population. It guides your action planning and your measurement of the effectiveness of your plan’s activities. Ultimately, data will be key to helping you tell the story of how your patients are experiencing health care at your organization.
Identifying At-Risk Patient Populations
The initial steps in using data to address disparities will be to identify and prioritize patient populations, and for this, you’ll need to identify your data sources. Look to currently available sources where you may already be gathering the information you need to identify priorities. Utilizing available sources of patient data can support more efficient movement in your program, providing those data sources with the granularity of information necessary to identify opportunities. These data sources are often connected with your patient identification and payer information databases. You should review your data to determine that it provides information such as the patient's preferred language, race, age, gender, and home address that can support the identification of at-risk groups. Other sources for consideration besides the admission/assessment data mentioned before.
- Surveys, patient safety events, complaints. Preferably, it should be in a digitized format that can be filtered and manipulated to extract trends.
- Organizational quality data. This could include outcome and process data.
- Patient/Family Advisory teams, local groups, and social media. These may require a specialized tool to obtain data, but they would typically be relegated to more specific information gathering.
These sources may be an excellent source of information to readily identify at-risk populations for organizations that are just starting to consider healthcare disparity initiatives. As work progresses, there may be opportunities to identify other populations or sub-populations within larger groups with identified disparities or potential disparities that require intervention. It’s important for organizations to recognize that there may be a diverse group of populations and at-risk groups they may never have considered. Additionally, they should recognize that these groups may change, with new groups appearing with changes in services or other community factors.
Discovering and Prioritizing Healthcare Disparities Within the Organization
Organizations must first understand where disparities exist, the scale of the disparities, and why these disparities are occurring within their patient population. Examining disparities through data allows organizations to understand differences in how patients experience care and how they can improve care processes to ensure appropriate care for all patients. Organizations may have pre-existing ideas of how health conditions and outcomes vary in specific patient populations based on observations and anecdotal evidence; however, they often underestimate the extent of these disparities in their patient populations, and they may not be aware of the barriers patients face during the course of care activities or the factors outside of the healthcare system that may play a role in patients achieving their healthcare goals. Additionally, disparities may exist in different groups or conditions more than expected. It’s vital that data is closely examined by first stratifying it into various attributes such as sex, gender, gender identity, race, socioeconomic factors, etc., and comparing these with quality and health outcomes and process data to provide the most reliable way to reveal the type and magnitude of a disparity and either verify “hunches” or re-direct focus when assumptions with disparity are disproven.
After you have identified the specific patient groups for your organization, you'll want to start looking at the distribution within each group and answer the question “What's happening within each one” to identify the meaningful differences. Compare data and groups to determine how quality within one group differs from quality in another. Using a health equity lens when comparing groups defines health disparities not solely as healthcare quality or outcome differences, but their meaning within a context of differences that may arise from intentional or unintentional marginalization and likely reinforce the social disadvantage and vulnerability of the patient group.
This process will provide organizations with the breadth and depth of different health disparities within the populations they serve and help them better understand the needs and issues of the various patient groups to which they provide services.
Using Data in Planning Equity-Focused Initiatives and Measuring Impact
To help identify those disparities your organization should make priority improvement projects. You should stratify quality measures that reflect their organizational priorities, which would be most sensitive to disparities. These can include measures of access and care delivery (e.g., missed appointments or immunization rates), clinical outcomes, satisfaction, cost, or others. Organizations may also want to prioritize domains of care that are expected to differ the most across demographic groups. As organizations choose initiatives for reducing disparities, they should establish the process of using data in the evaluation plan as an integral part of implementation rather than an afterthought. This ensures that organizations have the data they need to support claims about the impact of the care transformation and track progress and challenges.
Organizations should take steps early as they plan their improvement actions by defining goals for improvement and identifying appropriate measures for success. Additionally, they should develop a process for reviewing data during the care transformation, including gathering a baseline before implementation to demonstrate if there is change through initiatives.
The National Quality Forum (NQF) provides four criteria to help healthcare organizations select meaningful measures that warrant a specific focus on health equity and health disparities. These criteria can be considered across various factors, including race or ethnicity, gender identity, sexual orientation, disability, socioeconomic status, and other social factors. These four criteria include:
- Prevalence: How prevalent is the disease or condition (targeted by the quality measure) in the disparate population?
- Size of Disparity: How large is the quality, access, and/or health outcome gap between the disparate population and the group with the highest quality for that measure?
- Strength of Evidence: How strong is the evidence linking improvement in performance on the measure to improved outcomes in the disparate population?
- Ease and feasibility of improvement (actionable): Is the measure actionable among the disparate population to be improved?
Organizations should start with the measures they used to identify disparities in the first place but also should choose other measures that will reflect the impact of the care transformation. Three types of measures are useful for successfully evaluating data: intervention process, health process, and outcome measures.
Process and outcome measures impact patients (positive or negative) and are usually the measures organizations stratify to find disparities in the first place. Process measures tend to improve faster than outcome measures since they focus on one part of care rather than the constellation of factors that influence outcome measures.
Intervention process measures evaluate whether the care transformation was successfully implemented as planned. For example, an organization may track no-show rates or the number of calls it takes to reach a patient in order to show the effort required for “successful” patient contact.
Health process measures refer to what is done to a patient. Optimally, organizations will use evidence-based process measures that have been demonstrated to improve patient outcomes (such as eye screening for patients with diabetes).
Outcome measures refer to the actual results for the patient. They can be disease-specific or general and include clinical indicators such as blood pressure control for patients with hypertension or hemoglobin A1C for patients with diabetes. Other outcome measures include results such as the number of emergency department visits or hospitalizations and survey measures of patient experience.
Other considerations for defining goals and measures of success can include each type of measure that organizations can define goals in terms of:
- The same population before and after the intervention (e.g., a 10 percent increase in LDL screening rates),
- A comparison to another group (e.g., equal rates between Hispanic/Latino patients and Asian-American patients) or
- A comparison to a benchmark outside of the organization (e.g., 80 percent of the national rate for this measure).
Organizations should consider the degree of change with data measures to establish their goals. Measures can show:
- Absolute improvement –a measure improved by 80%.
- Positive change in trends –year-over-year emergency department visit rates declined compared to increases in the two years’ pre-intervention.
- Flattening trends—year-over-year emergency department visit rates stopped climbing compared to increases in the two years’ pre-intervention.
Each of these examples may be an appropriate goal depending on the inner and outer contexts of the organization and the disparity being addressed.
Some organizations also may find it helpful to conduct “pilot testing” before the intervention begins. Pilot testing involves implementing change on a smaller scale before expanding the intervention to collect data suggesting future changes. Future changes may include: 1) the scale of the intervention (e.g., more patients or more practices), 2) the population or condition of focus, 3) the intervention itself, and 4) stakeholder involvement (who and how to engage). Organizations that lack the staff, time, or institutional resources to perform dedicated pilot testing should look for ways to improve their intervention efforts within the data they regularly review.
Communicating Your Story of Equitable Care
Organizations should not simply collect and monitor disparities data. As organizations work to reduce disparities, they can improve their success by sharing the intervention's results. Personal stories re-humanize the people behind the quantitative data and are an essential way to generate buy-in. Stories will keep staff and leadership more engaged throughout the intervention, and sharing the results of health equity efforts can encourage further action and highlight opportunities for improving implementation.
By sharing results within and outside of the organization, organizations can:
- Receive feedback and ideas for ways to improve health equity efforts.
- Celebrate progress (including “quick wins”) in order to maintain momentum.
- Understand why results came out as they did and provide a better understanding of the organization and the disparate population.
- Create a culture of transparency with patients and communities by goals and improvements.
- Lay the groundwork at the end for future partnerships and encourage action from people not previously involved (e.g., partnerships with additional health plans or community-based organizations); and
- Maintain health equity as a top priority by linking clear, compelling results to other high-priority programs in the organization (e.g., patient safety or care management).
Not all care transformations will successfully reduce disparities. Organizations may hesitate to share negative results, but even negative results can carry lessons for success. Organizations can learn important lessons from projects that fail to have an effect and can incorporate the lessons into future efforts. Unfavorable health equity initiative data can help “prove” the value of a project and make the case for resource allocation.
Health equity data can help organizations demonstrate their success to external entities, such as the Center for Medicaid & Medicare Services, and charitable foundations. These entities may have programs, partnerships, or grant opportunities that can support organizations’ efforts— financially or otherwise—to reduce racial and ethnic disparities and thus improve quality and strengthen the business case for equitable care.
Summary
As the business visionary Peter Drucker stated, “You can’t improve what you don’t measure,” by extension, you can’t measure without data. Actively reviewing and responding to data allows organizations to reduce disparities and engage patients and the community in ways not possible without data. Health equity data tells a compelling story that motivates healthcare stakeholders—patients, providers, payers, state officials, community patients, and others—to participate in achieving high-quality healthcare for all. Establishing effective and efficient data management processes to measure, analyze, initiate, and evaluate is key to mitigating and sustaining health equity initiatives.
For questions or to learn more contact the C&A team at 704-573-4535 or email us at info@courtemanche-assocs.com.
