Year: 2025
Type: article
Abstract: Equity is 1 of 6 domains of health care quality defined by the Institute of Medicine.1 While incorporating equity into quality improvement (QI) is crucial,2 it introduces unique challenges. Using the Model for Improvement,3 we can ask 2 fundamental questions about equity in QI: (1) What are we trying to accomplish? In other words, what is the equitable state? and (2) How are we measuring an improvement in equity? In this issue of Hospital Pediatrics, Valderrama et al describe their work creating and operationalizing an equity dashboard in a pediatric emergency department (ED).4 Their work highlights these challenges and underscores why addressing them is complex yet essential to achieving high-quality equitable care.Dashboards are data feedback tools used to enhance quality and safety and highlight inequities in pediatric health care settings.5,6 The authors of this study emphasize one of the key strengths of dashboards: real-time visualization of data trends.5,7 Using the Institute for Healthcare Improvement’s framework for operationalizing equity,8 they examine “equity gaps” or differences in health outcomes by groups in the population. They detected several gaps in timeliness, including prolonged ED length of stay for patients who speak Spanish compared with patients who speak English.9 Identifying trends like these in real time can help clinical leaders prioritize QI efforts, plan interventions, and allocate resources.5,7However, this approach in defining the equity gap as the difference in outcomes between groups leads us back to the fundamental questions: What is the equity goal, and how are we measuring it? Valderrama et al defined the equity gap as the difference in a quality metric between patients who are non-Hispanic, white, and English speaking and others, and their goal was to reduce this gap. The rationale for centering this population as the reference standard could be intuitive for some comparisons. For communication-related metrics in the United States, where English is the default language for health care delivery and serves most patients, it may be logical to use English speakers as the referent population. However, when examining other measures of quality care, Valderrama et al used white, non-Hispanic as the referent group against which to measure disparities, likely because they make up the majority of the population. While this approach has been used historically by the Centers for Disease Control and Institute for Healthcare Improvement, it suffers from 2 pitfalls.8,10 First, centering the non-Hispanic, white population as the reference standard inadvertently suggests that the ideal state of health for a patient is to be a member of that group. Second, for certain outcomes, patients who are non-Hispanic and white receive higher rates of inappropriate care and, thus, may represent the wrong benchmark.11–13To address these complexities, we encourage teams to thoughtfully explore alternative measurement approaches while carefully examining the fundamental questions guiding their equity work. First, when evidence or consensus-based guidance exists to define the ideal patient outcome, use this to set the equity goal. For example, Natale et al investigated disparities in appropriate head imaging for minor head trauma as defined by the Pediatric Emergency Care Applied Research Network.13 In this case, the equitable state may be defined as adherence to the evidence-based pathway. Second, instead of choosing the highest prevalence group as the reference standard, consider using an estimate of central tendency for the whole population. For example, Hartford et al evaluated the use of laboratory or radiology investigations by race/ethnicity and language data compared with the overall mean.6 In another example, Gutman et al examined racial and ethnic disparities in the evaluation of low-risk febrile infants in the ED.14 Instead of using non-Hispanic, white infants as the reference standard in their regression model, they applied deviation coding (or “sum contrasts”) so that all comparisons were made to a “grand mean” (or “mean of means”). A limitation of this method is that the “grand mean” may not approximate the mean depending on subgroup sizes. Other forms of contrast coding, such as weighted effect coding may offer another approach.15 All such coding schemes require thoughtful application, as the reference standard may become a theoretical quantity. Third, use historical norms across an entire population served or peer institution data trends to determine goals. For example, a goal for unexpected revisits may be based on historical data for all patient arrivals rather than comparing subgroups to a majority referent population. Finally, we encourage the use of collaborative goal setting. Valderrama et al formed a multidisciplinary team to accomplish their goals, and the same multidisciplinary team could have partnered with family and community representatives to set goals for measures that are meaningful to patients in the local and clinical context.Defining and operationalizing equity in QI are rapidly advancing. Although significant work has focused on measuring and reducing disparities in health outcomes between population groups, future efforts are required to define and measure the dismantling of structural barriers to health equity. In a recent publication by a working group from the American College of Emergency Physicians, Khidir et al suggested numerous equity measures that highlight the following structural barriers: the lack of 24/7 language interpreter services, the use of stigmatizing language in the medical record, and the concordance between the ED patient population and the local community race, ethnicity, and payer mix. Measuring these goals and applying QI methodology when relevant could lead to even more impactful changes.While measuring equity presents inherent challenges, we commend Valderrama et al for their important contribution—perfection should not be the enemy of progress. As pediatric institutions increasingly engage in equity-focused QI work, we encourage them to harness informatics and real-time data extraction to develop dashboards that embed equity into routine quality and operations activities. In doing so, institutions should also anticipate the complexities inherent in defining health equity and selecting appropriate measurement approaches while drawing on the evolving tools and strategies developed by equity scientists to navigate these challenges effectively.
Source: Hospital Pediatrics
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4416262912