Must-haves to identify cohorts for a health equity-focused quality improvement initiative
As a physician, QI practitioner, and health equity consultant, I have seen that a rising tide does not necessarily lift all boats. A BMJ piece confirms this, “There can be three possible outcomes of QI on equity: improvement for all but maintenance of the equity gap (equality in improvement); improvement more in the disadvantaged population (decreasing the gap); or improvement more in the advantaged population (widening the gap).”
The path to successfully addressing health inequities has two components. First, measure and report on QI outcomes before and after a health equity intervention or initiative. Second, pre-design strategies and implement programs by creating a QI data strategy through a health equity lens. Here are seven considerations for creating a comprehensive QI data strategy that achieves health equity objectives.
Align and coordinate health equity data strategies
Start by looking at both internal and external partners and sources of data as you begin your focus on the QI data strategy. It’s vital to align and coordinate health equity data strategies across all lines of business, for payers; and across multiple clinics and points of care delivery, for providers. Include external partners like business process management vendors, operational and care supports, care management services, as well as community and service organizations as they either act on health equity initiatives and/or provide data that is used as part of program design and management.
Set annual REaLS data collection goals
The COVID-19 pandemic highlighted the need for, and current lack of, reliable race and ethnicity data. The CDC reported that data have been missing for nearly 40% of people vaccinated. This makes it challenging to create programs that can reach historically marginalized people and geographies.
Set data collection targets for annual race, ethnicity, language (REaL), sexual orientation, and gender identity (REaLS). Standardize to one collection instrument and/or align to the existing SNOMED CT and SDoH-related ICD-10 Z codes from the Office of the National Coordinator for Health Information Technology (ONC) and the National Institutes of Health (NIH).
The American Hospital Association’s (AHA) guide, “A Framework for Stratifying Race, Ethnicity and Language Data,” provides the following questions to ensure high-quality REaL data.
- Accuracy: Self-identified, correctly recorded, consistent categorization?
- Completeness: REaL data captured across all services? Percentage unknown, other, or declined tracked and evaluated?
- Uniqueness: Are individual patients represented only once?
- Timeliness: Are data updated regularly?
- Consistency: Are data internally consistent? Reflect the patient population served?
Stratify and start small
When building your health equity data strategy, stratify high-priority performance measures by REaLS data and start small by selecting just one or two conditions to address first. The initial activities could focus on where local quality improvement is most needed, heavily-weighted HEDIS measures, or priority Quality Incentive programs. Conditions to focus on could include controlling high blood pressure (HEDIS measure CBP) or Hemoglobin A1C control for patients living with diabetes (HEDIS measure HBD).
Identify cross patterns of inequity
Single-source or single-element data limits the analysis and potential insights possible. Using additional data filters or sorts can identify cross patterns of inequity, which could include sexual orientation, gender identity, and social needs. Also, plan data disaggregation early in the program to ensure there is an analysis of changes among the groups and subgroups by income, race, and geography. This is helpful in understanding the hidden factors as part of root cause analysis.
Layer in geographic indices
Identify distribution and burden of health inequities by geography, using geocode member-level data and stratification. Analyze healthcare data according to geographic indices, such as RTI Rarity™, which is our proprietary tool that forms a composite local social inequity index using over 200 data sources at a granular level.
Consistent data analysis
Quality Improvement approaches prioritize the regular analysis of results to uncover new opportunities. For multi-level health equity QI data strategy efforts, consistent reporting and reviews against goals provide discipline towards improving identified inequities in key measures. Sustained results come from an ongoing systems approach and not just a one-time data analysis. Also, consistent data analysis keeps results in perspective. Context relies heavily on data, and how interventions are implemented can dramatically change their impact on health equity.
PDSA cycles and begin again
The beauty of integrating health equity as a foundational part of quality improvement is that QI professionals are seasoned in using standardized and proven tools and techniques. The Plan, Do, Study, Act (PDSA) model ensures consistent cycles where teams can test interventions and iterate towards more significant improvement. The IHI’s Improving Health Equity: Build Infrastructure to Support Health Equity guide offers examples of how organizations have built their data infrastructure to improve health equity.
Master QI data strategies that achieve better health equity
A sophisticated and tailored QI data strategy provides the required data collection, stratification, and analysis that reveals inequities, supports priority setting, and extend improvement activities. Our team provides quality improvement, health equity data strategy guidance, and research approaches and analytics tools to plan and realize objectives that improve health equitably.
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