Data Quality and Reporting Guide

Delivering quality care demands that we address the needs of subpopulations with attentiveness to their specific care needs and structural barriers to health. Even in organizations where performance on HEDIS and other quality measures is high, significant racial and other sociodemographic disparities often persist.

Strengthening population health requires going beyond aggregated HEDIS measures and identifying subpopulations that have lower rates of receiving certain types of care, and may require different forms of support. The process to get robust patient-level data is complicated and doesn’t have to be perfect to advance the work. Even imperfect data on race, ethnicity and language (REAL), sexual orientation and gender identity (SOGI), and social needs provide valuable insights to guide care delivery and coordination.

Practices can contribute to improving health and reducing disparities by considering the following approaches to strengthen data quality:

The initial step in addressing disparities is to collect high-quality data that fosters a comprehensive understanding of each patient. This entails incorporating REAL data, demographic data (age, gender) and social needs data. By leveraging this information, healthcare practices can gain valuable insights into disparities in access, continuity and health outcomes.

• This guide, developed by the American Hospital Association, provides care systems with guidance about collecting and using REAL data in meaningful ways to understand and address healthcare disparities: Reducing Health Care Disparities: Collection and Use of Race, Ethnicity and Language Data.
• This white paper from the Commission to End Health Care Disparities provides context and recommendations for collecting and using REAL data in ambulatory settings: Collecting and using race, ethnicity and language data in ambulatory settings.
• The Fenway Institute shares methods for gathering sexual orientation and gender identity (SOGI) data from patients in clinical settings: How to Gather Data on Sexual Orientation and Gender Identity in Clinical Settings.
• This training module by the American Medical Association (AMA) describes why collecting REAL data is important, and how to use this data to improve quality outcomes: Collecting Patient Data: Improving Health Equity in Your Practice.
• This presentation by the Community Health Care Association of New York outlines the importance of accurate and standardized REAL data and how to routinize data workflows: Race, Ethnicity and Language Data Collection.

As your data quality continues to improve, you can further tailor efforts to enhance healthcare specifically for subpopulations experiencing disparities.

• This brief by the American Hospital Association Center for Health Innovation provides guidance on how to use data to identify, investigate and intervene to address health disparities: Using Data to Reduce Health Disparities and Improve Health Equity.
• In this case study, Advancing Health Equity highlights how a health system in Rhode Island developed a phone-based care management intervention to address depression in Latinos, who typically receive less treatment and have poorer outcomes: Telephone-Based Depression Care Management.
• This framework developed by the American Hospital Association provides guidance for how healthcare systems can stratify REAL data for the use of identifying healthcare disparities: A framework for stratifying race, ethnicity and language data.
• In this publication, the American Heart Association highlights a pharmacist delivered care model for hypertension in barbershops to address disparities in blood pressure in Black men: Barbershop-Based Care Dramatically Trims Blood Pressure.

As you engage in the work, partnering with individuals with lived expertise in data sharing ensures the efforts are more responsive to the needs of community members, and the insights provide valuable context to inform data sharing. You may also find it helpful to partner with community organizations outside the healthcare practices to strengthen and broaden the data being utilized to guide clinical and programmatic decisions to address disparities.

• This brief from the Center for Health Care Strategies shares key lessons and insights to incorporate how the community perspective can improve data sharing efforts: A Community-Centered Approach to Data Sharing and Policy Change: Lessons for Advancing Health Equity.
• This practical guide from the California Health Care Foundation serves as a tool for communities considering cross-sector data sharing between health and homeless systems of care. It provides practical tips for some of the challenging components of cross-sector data sharing: How to Share Data: A Practical Guide for Health and Homeless Systems of Care.