Lauren Hovey NORC at the University of Chicago

1. Supporting Disparities Research by Increasing the Collection of Social Determinants of Health Data through EHRs S24 Presentations Lauren Hovey NORC at the University of Chicago

2. Disclosure • I and my spouse/partner have no relevant relationships with commercial interests to disclose.

3. Learning Objectives • After participating in this session the learner should be better able to: • Discuss the importance of using EHRs to collect social determinants of health • Race, ethnicity, and language preference (REaL) • Sexual orientation, gender identity (SOGI) • Understand the federal standards and relevant policies for REaL and SOGI data collection • Describe the challenges providers face in EHR-based data collection

4. Context • The 2003 National Academies of Medicine report, Unequal Treatment: Confronting Racial and Ethnic Disparities in Healthcare • Complicated factors contribute to social risk • Health and human service agencies, researchers, and clinicians are all key players • Standardized data collection within Electronic Health Records (EHRs) represents a tool and opportunity • Facilitating services for populations at risk for disparities • Enhancing analytics to support knowledge generation

5. EHR Adoption by Office-Based Physicians • 87% adoption; 78% adopted a certified EHR; 54% adopted a Basic EHR Source: Jamoom E, Yang N. Table of Electronic Health Record Adoption and Use among Office-based Physicians in the U.S., by State: 2015 National Electronic Health Records Survey. 2016

6. Objectives • Explore the intersection of: • The provider role and challenges in data collection in the clinical setting • Current EHR standards for collecting REaLand SOGI • Policies governing REaL and SOGI data collection • Goal: to inform and assist providers in recognizing the need to improve EHR-based capture and use of REaL/SOGI to reduce health care disparities

7. Methods • Review of existing standards for REaL/SOGI collection • Office of Management and Budget (OMB) • Department of Health and Human Services (HHS) • Centers for Disease Control and Prevention (CDC) • Review of relevant policies • Meaningful Use and the EHR Incentive Programs (now “Promoting Interoperability” Programs) • Medicare Access and CHIP Reauthorization Act of 2015 (MACRA)/Merit-Based Incentive Payment System (MIPS) • Review of grey and white literature • PubMed, federal and industry reports, etc.

8. Review of Standards: REaL Data

9. Review of Standards: SOGI Data

10. Relevant Policies and Requirements Interoperability Programs

11. Provider-Level Challenges • Workflow: • Lack of best practices for collecting data during the patient/provider encounter • Data collection at registration/admitting • One-on-one during the patient/provider visit • Patient’s self-identifying with more than one racial group • Lack of knowledge of EHR product functionality • Locating fields within the patient record to record relevant demographic data • Understanding the downsides of information captured as free text rather than structured data

12. Provider-Level Challenges • Provider education and competency: • Understanding how to use the data for practice-level quality improvement • Generating patient panel reports stratified by REAL data • Addressing health disparities in treatment decisions • Understanding the relevancy of REAL and LGBTQ data for treatment decision and appropriate preventative screenings • Skills to ask demographic questions and to explain the utility of the data to patients • Comfort and knowledge of discussing LGBTQ health issues with patients • Using correct terminology to discuss and collect SOGI data

13. Provider-Level Challenges • Privacy and confidentiality: • Patients’ negative past provider interaction experiences related to discrimination • Pushback from patients and staff not wanting to provide or ask about demographic information • Patients’ general concerns with respect to data breaches • Individual patient disclosure preferences • Patient knowledge regarding the importance of discussion sexual orientation and gender identity data with their providers • Confidentiality issues related to collecting SOGI from minors

14. Provider-Level Challenges • Vendor product capabilities: • Products that restrict or prepopulate fields related to gender • Presence and correct use of EHR standards and fields to record REaL/SOGI data • Presence and use of clinical decision support triggers to promote data capture

15. Conclusions • Standards for REaL/SOGI data collection are available for use • Accurate data must be available for analysis and EHRs are the tool • Provider-level barriers complicate its collection but are surmountable • Policy levers are also available in the form of value-based purchasing

16. Practical Application of this Session • This session identifies: • The standards relevant to the collection of REaL and SOGI data • The current policies and requirements that govern REaL and SOGI data collection and exchange • Challenges providers face at point-of-care in the understanding, discussion, and collection of REaLand SOGI data • A path forward for greater EHR-based capture in the form of disparity reduction and value-based purchasing

17. Question #1 • To facilitate the standardized collection of REaL data, which of the federal minimum data sets provides an optimal level of granularity? • OMB minimum standard • HHS race and ethnicity standards • CDC Race and Ethnicity Code Set Version 1.0 • The optimal data standards are those that are fit for purpose and appropriate for the context in which the data are collected.

18. Answer • OMB minimum standard • HHS race and ethnicity standards • CDC Race and Ethnicity Code Set Version 1.0 • The optimal data standards are those that are fit for purpose and the context in which the data are collected. • Explanation: (D) Providers must balance a number of clinical and administrative responsibilities in collecting REaL and SOGI data. This includes managing workflow and time constraints, patient relationships, and navigating the EHR product itself. Most EHRs and health IT products are capable of demographic data collection; therefore, “the best fit” for granularity of demographic data capture involves a triangulation of the providers’ priorities for data capture and use, patient needs, and available standards and products.

19. Question #2 • Even when providers and/or health systems are motivated to collect and use REaL/SOGI data for clinical purposes, their vendor products may not be optimized for the task. Conversely, vendors may offer robust functionalities that are unintuitive or that providers are not well versed in using. For EHR products designed to facilitate REaL/SOGI collection and use, which solution reduces provider burden and facilitates data capture and use? • Pre-populated fields related to gender/sex-specific history • Structured fields for REaL/SOGI that occur in a consistent place in the EHR • Free text fields that allow providers to enter relevant information • Data capture in a pre-assessment form (e.g., via portal or registration questionnaire)

20. Answer • Pre-populated fields related to gender/sex-specific history • Structured fields for REaL/SOGI that occur in a consistent place in the EHR • Free text fields that allow providers to enter relevant information • Data capture in a pre-assessment form (e.g., via portal or registration questionnaire) • Explanation: (B) Ideally, vendors support provider training in how to use structured REaL/SOGI fields within the patient record. (A) Products that restrict or pre-populate sex-specific history, exam, or ordering templates may limit the clinician’s ability to record and update the patient record (e.g., a transgendered patients’ medical transition history and current anatomy). (C) Free text fields are useful for difficult-to-code information, but are difficult to access for future care. (D) The ideal mode and timing of demographic data collection is an open question as far as how best to encourage patient disclosure; however, the utility of this information depends on its electronic captured and how effectively it is incorporated into the EHR.

21. Thank you!

22. AMIA is the professional home for more than 5,400 informatics professionals, representing frontline clinicians, researchers, public health experts and educators who bring meaning to data, manage information and generate new knowledge across the research and healthcare enterprise. AMIA 2018 Clinical Informatics Conference | amia.org