Conceptual framework using data collection in support of quality measures.? ?In a 2- to 3-, address the need for the use of the grouping of the domains?? and why it is impo
Conceptual framework using data collection in support of quality measures.
In a 2- to 3-, address the need for the use of the “grouping of the domains” and why it is important to include them.
Then give an example of a project to which these can be applied.
Include in your summary ways in which we can do a better job in educating those in healthcare about the need for QI.
Health Equity, Diversity & Inclusion Measures for Hospitals and Health System Dashboards
©2020 American Hospital Association | December 2020
Page 10 | www.aha.org
Introduction
Among the American Hospital Association’s top priorities are addressing equity, diversity and inclusion in health care. We believe that health inequities contribute to health disparities, a well-documented factor in both the cost of care and quality outcomes. We are proud to collaborate with
our members and other stakeholders to continue to support the shared goal of advancing the health and well-being of all individuals and communities. These are necessary actions to improve health and save lives.
Health equity is core to AHA’s vision of a society of healthy communities, where all individuals reach their highest potential for health. Health equity is not the same as health equality, in which everyone receives the
same opportunities for health. Rather, health equity requires
an interdisciplinary, team-based approach to ensure everyone can
Health Equity, Diversity & Inclusion Dashboard
Domains
How to Use this Document:
· An interdispciplinary team reporting to the C-suite executives is best positioned to utilize this document.
· The measures under each domain are intended to be easily integrated into existing dashboards.
· The “Operationalizing this Measure” column describes ways in which hospitals and health systems may engage stakeholders across the system.
· The supporting tools and resources feature briefs, guides and toolkits to help hospitals and health systems make progress toward achieving that measure.
achieve optimal health that is fair and just, especially for individuals who have the greatest need.
Hospitals and health systems are actively engaged in addressing inequities and reducing disparities in their communities through various strategies and mechanisms. A dashboard can provide health care leaders with the necessary information on their journey to advance health equity, diversity and inclusion. A basic level health equity, diversity and inclusion dashboard may include measures to include the following: race, ethnicity and language preference (REaL) data collection, stratification and use; cultural competency training; diversity and inclusion in governance and leadership; and community partnerships. As hospitals and health systems begin to tackle these areas of opportunity, dashboards may become more advanced to include measures related to supplier diversity, employee satisfaction and other areas of organizational importance.
Domain 1: Data Collection, Stratification and Use |
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Desired Outcomes/ Measures |
Intent of Measures |
Operationalizing the Measure |
Supporting Tools and Resources |
Increase the collection, stratification and use of race, ethnicity, language (REaL) preference data. Measure(s): · Percent of workforce (staff and clinicians) trained regarding collection of self- reported REaL data. · Percent of patient records with REaL data preference complete with opportunity for verification at multiple points of care, beyond just registration . |
Data collection, stratification and use are essential to developing initiatives to eliminate disparities in health outcomes. By collecting, stratifying and using REaL patient data along with other data points such as sexual orientation, gender identity, geographic location, veteran status and disability status, hospitals and health systems can better identify disparities in patient populations. |
REaL data can be collected at various points of care or within the community. For example, Henry Ford Health System, an AHA 2020 Carolyn Boone Lewis Equity of Care Award Honoree, collects REaL data for more than 90% of patients, as a result of their “ We Ask Because We Care” campaign. The data is stratified and used to implement programs and improve outcomes in maternal and infant health, diabetes management and prevention, and other areas. |
Addressing Health Care Disparities through Race, Ethnicity and Language (REaL) Data (2020) This brief contains multiple resources and case studies of how hospitals are using REaL data to better understand disparities in care. URL: https://ifdhe.aha.org/addressing-health-care-disparities- through-race-ethnicity-and-language-real-data Building an Organizational Response to Health Disparities (2020) This resource features a compilation of reports, guides, toolkits, training tools, webinars, books and articles regarding REaL data collection, stratification and use. URL: https://www.cms.gov/About-CMS/Agency-Information/ OMH/Downloads/Data-Collection-Resources.pdf Evaluation of the National Standards for Culturally and Linguistically Appropriate Services in Health and Health Care (National CLAS Standards) (2018) This toolkit describes the National CLAS Standards and provides meaningful and practical guidance on delivering culturally and linguistically appropriate services. URL: https://minorityhealth.hhs.gov/assets/PDF/ Evaluation_of_the_Natn_CLAS_Standards_Toolkit_PR3599_ final.508Compliant.pdf |
Framework for Stratifying Race, Ethnicity & Language Data (2014) This guide provides a framework that allows hospitals and health systems to stratify patient data to identify health care disparities. This framework consists of five steps. |
Domain 1: Data Collection, Stratification and Use |
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Desired Outcomes/ Measures |
Intent of Measures |
Operationalizing the Measure |
Supporting Tools and Resources |
Increase the collection, stratification and use of data (sexual orientation, gender identity and disability status) among broader culturally diverse populations. Measure(s): · Percent of patient records with sexual orientation, gender identity and disability status data complete with opportunity for verification at multiple points of care, beyond just registration. |
Atrium Health, AHA’s 2019 Carolyn Boone Lewis Equity of Care Award Honoree, developed a “ Demographic Data Wall,” which is used to identify disparities in population health measures and stratifies race, ethnicity, language, sexual orientation and gender identity data allowing clinical leaders to identify gaps in outcomes across populations. |
Do Ask, Do Tell: A Toolkit for Collecting Data on Sexual Orientation and Gender Identity in Clinical Settings (2020) This toolkit provides specific sexual orientation and gender identity (SOGI) questions recommended by national LGBTQ organizations. It also describes how to collect these data in electronic health record (EHR) systems, how to use these data to support clinical processes, and how to train clinical staff to interact with LGBTQ patients in ways that are affirming and welcoming. URL: https://doaskdotell.org/ehr/toolkit/ Ready, Set, Go! Guidelines and Tips For Collecting Patient Data on Sexual Orientation and Gender Identity (SOGI) (2018) This guide helps hospital and health systems begin to implement sexual orientation and gender identity (SOGI) data collection. URL: https://www.lgbtqiahealtheducation.org/wp-content/ uploads/2018/03/Ready-Set-Go_2018.pdf |
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· Percent of workforce trained regarding collection of sexual orientation, gender identity and disability status for patients. |
Washington Group Training on Disability Data Collection and Analysis (2015) This training module aims to help understand the approach and guiding principles of the Washington Group on Disability Statics and deepen knowledge of how to collect and analyze disability. URL: https://hilearngo.handicap-international.org/ workspaces/176/open/tool/home#/tab/-1 |
Domain 1: Data Collection, Stratification and Use |
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Desired Outcomes/ Measures |
Intent of Measures |
Operationalizing the Measure |
Supporting Tools and Resources |
Identify and monitor the collection and use of patient social needs such as: food insecurity, housing stability, transportation needs, education needs, social support, financial stability, employment, physical safety and other measures that are specific to your population’s needs. Measure(s): · Percent of patient records with social needs data complete with opportunity for verification at multiple points of care, beyond just registration. · Percent of workforce trained regarding collection of social needs areas for patients. |
Rush University Medical Center, an AHA 2019 Carolyn Boone Lewis Equity of Care Award Honoree, implemented social needs screening across the system and community settings to identify risk factors associated with social needs (food insecurity, housing instability and transportation). For example, Rush works to mitigate the social determinants of health (SDOH) through strategic partnerships that provide food delivery services to older adults. |
PRAPARE Implementation and Action Toolkit (2019) Toolkit with resources, best practices and lessons learned to guide implementation, data collection and responses to social determinant needs. Contains standardized patient risk assessment tool as well as a process and collection of resources to identify and act on the SDOH. URL: http://www.nachc.org/research-and-data/prapare/ toolkit/ Screening for Social Needs: Guiding Care Teams to Engage Patients (2019) This tool helps hospitals and health systems facilitate sensitive conversations with patients about their nonmedical needs that may be a barrier to good health. URL: https://www.aha.org/toolkitsmethodology/2019-06- 05-screening-social-needs-guiding-care-teams-engage- patients ICD-10-CM Coding for Social Determinants of Health (2018) This brief provides an overview on ICD-10 coding for SDOH. It also features a list of AHA tools and resources for hospitals, health systems and clinicians that address the social needs and the SDOH. URL: https://www.aha.org/system/files/2018-04/value- initiative-icd-10-code-social-determinants-of-health.pdf |
Domain 2: Cultural Competency Training |
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Desired Outcomes/ Measures |
Intent of Measures |
Operationalizing the Measure |
Supporting Tools and Resources |
Implement and/or monitor cultural competency training amongst all employees and clinicians to ensure culturally responsive care within strategic planning efforts, operations, yearly employee trainings, clinical care, social services and other areas specific to your organization. Measure(s): |
Cultural competency and unconscious/ implicit bias trainings increase health care professionals’ understanding of factors that are important to patients and play a key role in care decisions. These trainings also provide an opportunity for health care professionals to be mindful of unconscious and implicit biases that may occur when interacting with patients and team members. |
Chatham Hospital, a member of the University of North Carolina Health System and an AHA 2020 Carolyn Boone Lewis Equity of Care Honoree, administers trainings in communication, cultural competency and unconscious bias to employees. These trainings can be implemented yearly as part of continuing education for employees. |
Becoming a Culturally Competent Health Care Organization (2013) This guide provides a high-level overview for becoming a culturally competent health care organization and includes two case studies. URL: http://www.hpoe.org/Reports-HPOE/becoming_ culturally_competent_health_care_organization.PDF Building a Culturally Competent Organization: The Quest for Equity in Health Care (2011) This guide explores the case for cultural competency and provides guidance for health care leaders to build a culturally competent organization. URL: https://www.aha.org/ahahret-guides/2011-05-11- building-culturally-competent-organization |
· Percent of employees and clinicians who have completed cultural competency training. |
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· Percent of patient and family complaints related to cultural competency. |
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· Rate of patient satisfaction scores (HCAHPS, CG-CAHPS) pre- and post- cultural competency training stratified by race, ethnicity and language preference. |
Domain 2: Cultural Competency Training |
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Desired Outcomes/ Measures |
Intent of Measures |
Operationalizing the Measure |
Supporting Tools and Resources |
Increase unconscious and implicit bias training amongst all employees and clinicians to ensure that associations or attitudes that are reflexive do not alter perceptions, behaviors, interactions or decision-making. |
Health Care organizations utilizing the Implicit Association Test (IAT) (2019) This guide outlines four ways organizations can use the Implicit Association Test to improve health equity and quality of care. URL: https://www.aonl.org/system/files/media/file/2019/04/ ifd-implicit-association-0419.pdf |
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Measure(s): |
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