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According to the most recent National Healthcare Disparities Report, in the last three years more than 60% of disparities in quality of care have stayed the same or worsened for Blacks, Asians, Hispanics and poor populations. The causes of such disparities are multi-factorial, most often relate to quality, and are influenced by factors such as provider–patient relationships, provider bias and discrimination, and by patient variables such as mistrust of the health care system and refusal of treatment. System-level barriers such as health care financing, access to high quality services, and availability of high quality providers also play a role.
According to the authors, one of the most significant challenges and barriers health systems face in reducing disparities and improving equity is systematically identifying vulnerable populations. Assessments of race/ethnicity and language of patient populations can enable health care systems to:
A Data-Based Approach
The first step toward reducing disparities and improving equity requires systematically collecting race, ethnicity, and primary language data for all patients and then linking the data to clinical and patient satisfaction measures of quality. Such linkage can bring systems administrators closer to understanding where disparities exist. In turn, this can lead to understanding why disparities exist and to identifying some of the causal factors. Finally, targeted interventions aimed at reducing disparities can be developed and implemented and their success can be measured.
Overcoming Barriers to Data Collection
Valid and reliable data are the fundamental building blocks for identifying differences in care and developing interventions to improve the quality of care delivered to specific populations. Despite the importance of collecting and using reliable data to reduce disparities in care, most health care organizations do not systematically collect such information. Through fieldwork and research, the authors identified barriers and facilitators to data collection. For instance, barriers to collecting race, ethnicity and primary language data included discomfort of staff, concern about the legality of asking about racial/ethnic backgrounds, and system-level barriers such as a lack of consistent categories. Staff members were identified as the best facilitators when they understood the importance of collecting the data and effectively communicated that message to patients and families. The authors also offer tools and techniques for data collection.
Data as a Basis for Action
To transform collected data to actionable information, race, ethnicity, and language data must be linked to utilization and process measures. Observed differences in utilization may reflect differential need or may demonstrate significant inequalities in how hospitals meet the needs of different patient groups. Process of care measures provide information about which groups of patients received recommended services, when services were received, and whether there were gaps in services for specific populations. When data reflect such gaps, analysis is needed to determine why the gaps exist and to target interventions appropriately. Outcome measures—including mortality, morbidity, and measures related to patient safety and sentinel safety events—can also be monitored in terms of race, ethnicity, and language. If data show that health outcomes differ for groups of patients from different racial, ethnic, language, or socioeconomic groups, further investigation can determine the causes and direct subsequent actions.
Quality Measures for Quality Care
Equity and patient-centered care are two key indicators of high quality health care. Thus, measures of health care disparities are also measures of quality of care, and the language and tools of the quality movement can also address issues of inequity. However, bringing health care disparities into the mainstream quality-of-care arena requires an important shift in organizational culture: viewing equity in the context of a quality-improvement framework rather than treating it as a separate—and too often marginalized—undertaking. Ultimately, a quality-focused, data-driven approach will help ensure health care quality and equity for all populations.
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