Primary Care and Disease Prevention: Examining Health Equity through Informatics

One of the main quality measures for our healthcare system and public health programs is equity. Public health informatics is vital for distributing programs and information that keep people healthy, as well as evaluating the equity of our health system. Most of the racial inequities in healthcare outcomes are due to disparities in preventive measures. These disparities can be caused by differences access to care and affluence. At the root of these disparities are policies reinforcing systems that prevent social mobility for communities of color.

Healthcare Access in the Digital Age

In the age of the coronavirus pandemic, telehealth could be a double-edged sword in terms of equity. Overall, it should have a positive impact on equity, as some barriers to care are reduced through virtual appointments and some clinics offer free or reduced-charged visits online, as compared to in-person. Through telehealth, access opens up from a physical standpoint as well. Patients are not bound in their provider searches by transportation access – and can therefore broaden their choices of provider.

However, all of these gains are dependent on equitable access to smartphones or computers in order to join the visits. While racial disparities in ownership of smartphones is minute, access to computers and overall comfort with technology do have significant differences by race. Furthermore, ownership of a smartphone is more likely for US-born individuals. For example, 87% of U.S.-born Hispanics own a smartphone, compared to 69% of Hispanics born abroad.1 If smartphones can be provided equitably and healthcare cost barriers can be reduced, telehealth can have a great impact in improving accessibility to primary care. This would improve overall health in vulnerable communities through more effective disease prevention.

Informatics Research Approach: Racial Equity in Primary Care Access

Access to primary care is important for staying in a “wellness” cycle – as the saying goes, “Prevention is the best cure.” Racial equity in primary care will consequentially drive equity within overall population health outcomes. An informatics approach to studying racial equity in primary care access would entail evaluating the level of insurance coverage and primary care utilization across racial groups.

The dataset for this study would need to include EMR data with sociodemographic information, CPT codes, and claims data. Insurance data will also be needed. Health insurance plans should be broken down into multiple continuous variables, including deductible amount, primary care visit co-pay amount, monthly premium, ER visit co-pay, and number of in-network primary care providers. These coverage variables should be analyzed by sociodemographic variables, including race and income level, as well as EMR data on frequency of primary care appointments. Outputs of this analysis could drive support for policies supporting the expansion of medical coverage, as well as showcasing the need for free services to increase access to primary care.

One challenge with this approach is that data will need to collected for uninsured patients and patients who do not use primary care. A surveillance mechanism will need to be in place to collect data on these groups in order to identify racial disparities in insurance coverage and its impacts on primary care utilization. Survey-based approaches or CDC datasets on insurance coverage could be used.

What does the U.S. government say about racial equity in healthcare?

The 2010 CMS Meaningful Use guidelines2 include multiple measures related to health disparities, such as documenting sociodemographic data in a structured way and maintaining disease lists by demographic group to identify inequities. Racism and implicit bias are not specifically included in the metrics for reducing health disparities. As of yet, these factors are not standardized in documentation formats or data collection methods.

While CMS does not directly address racial justice in health outcomes, the Department of Health and Human Services has created the “HHS Action Plan to Reduce Racial and Ethnic Health Disparities.”3 The first-ever HHS Disparities Action Plan, this plan is part of the Federal Health IT Strategic Plan4, which emphasizes the need for improved equity in its objectives for Goal 3: Strengthen Health Care Delivery. The HHS Disparities Action Plan states its vision: “A nation free of disparities in health and health care.” The creation of the plan is long-overdue but a good sign that research and investment will increase in promoting health equity.

Surveillance will play a large part in providing datasets for disease occurrence and health disparities across races and socioeconomic groups. As showcased by the documented “Cooked,” the existing inequalities in social determinants of health put Black, Brown, and low-income communities at severe disadvantages in every area of health – and historically, federal, state, and local governments have done have resisted enacting policies to remedy these major forces of health inequity. To make real strides in health equity, programs will need to focus on disease prevention and addressing long-existing racial discrimination and resulting socioeconomic vulnerabilities.

References:

  1. Perrin, A., & Turner, E. (2019, August 20). Smartphones help blacks, Hispanics bridge some – but not all – digital gaps with whites. Pew Research Center. Retrieved October 14, 2020, from https://www.pewresearch.org/fact-tank/2019/08/20/smartphones-help-blacks-hispanics-bridge-some-but-not-all-digital-gaps-with-whites/
  1. Medicare & Medicaid EHR Incentive Program Meaningful Use Stage 1 Requirements Overview [PDF (2010). https://www.cms.gov/Regulations-andGuidance/Legislation/EHRIncentivePrograms/downloads/mu_stage1_reqoverview.pdf

3. HHS. (n.d.). HHS Action Plan to Reduce Racial and Ethnic Health Disparities [PDF].

  1. The Office of the National Coordinator for Health Information Technology (ONC), Federal Health IT

     Strategic Plan 2014, Exec. Doc. (2014).

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