After investigating the health disparities makeup of the model output in Part 1 of this series, as well as uncovering the “kinder coding” bias in Part 2, we sought to investigate how clinicians code for social determinants of health and what disparities may emerge.
Social determinants of health (SDoH) relate to how individuals are born, live, age, learn, work, worship and play, which affects their functioning, quality of life, health and health risks. The CMS Framework for Health Equity includes collecting, analyzing and reporting social determinants of health for patients. There has also been an increasing push for increased coding of social determinants of health for patients, including payments for SDoH risk assessments through CMS in 2024.
Looking at the data.
Social determinants of health are found in Z codes with the following encompassing most of the categories:
- Z55 Problems related to education and literacy
- Z56 Problems related to employment and unemployment
- Z57 Occupational exposure to risk factors
- Z58 Problems related to physical environment
- Z59 Problems related to housing and economic circumstances
- Z60 Problems related to social environment
- Z62 Problems related to upbringing
- Z63 Other problems related to primary support group, including
family circumstances - Z64 Problems related to certain psychosocial circumstances
- Z65 Problems related to other psychosocial circumstances
Social determinants of health coding can offer a valuable insight into the components of a patient’s life that may be affecting them and their health. If providers take time to document these factors in a patient’s medical record, there is a better opportunity to address their SDoH needs, including case management or offering resources for housing, food, employment, and more. Social determinants of health disparities disproportionately affect BIPOC patients through less access to infrastructural support and health resources. In our research, we aimed to understand if our data reflected these SDoH disparities, and draw further conclusions about biases evident in coding practices.
We encourage every health system and health plan to consider incorporating social determinants of health screenings as part of intake processes, and including additional reimbursement for those screenings. There have been many tools published, including the Accountable Health Communities (AHC) Health-Related Social Needs (HRSN) Screening Tool from CMS.
We created the following chart based on US Census race categories, with the highlighted numbers illustrating the percentages of a specific race from the total patient population with the corresponding ICD-10 or CPT code.
Z codes overrepresented in Black patients
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Upon reviewing and analyzing the codes by percentage breakdown based on race, clear patterns start emerging. Similarly to how health disparities became evident in our analyses, another clear trend was that social determinants of health-related issues were disproportionately affecting BIPOC, and more specifically, Black patients. The findings showed that Black patients disproportionately made up common Z codes for SDoH needs, compared to the average percentage of population based on the US Census race categories. The top SDoH codes included education and literacy, employment, food, housing and life transitions and psychosocial circumstances.
These findings are congruent with the current understanding of how SDoH needs are unevenly distributed across communities. Racism itself can be regarded as a critical social determinant of health, which affects every aspect of the lives of Black patients. Structural racism, along with systemic barriers, serve to limit patient access to care and negatively affect wellbeing and safety. It is unsurprising to see evidence that Black patients within these datasets have more SDoH needs, and the understanding that systems to address those needs have not been built into our health care system.
Lastly, we investigated biased coding for social determinants of health and found two sets of odd, and likely biased practices: white patients were repeatedly coded for employment needs while Black patients were given codes like “low levels of literacy” or “education problems.”
When filtering by for codes that white patients make up the largest percentage of, the most frequent code is “Other problems related to employment” (Z5689). Going back to our kinder coding idea, we can hypothesize that providers are practicing the same coding style for white patients regarding this specific SDoH category—by coding for problems relating to employment, providers are offering more context to the patient’s issues. This places the blame on issues that are external to the patient, essentially giving them the benefit of the doubt.
Meanwhile, we identified that Black patients were disproportionately given codes about low literacy and lack of education. Using the same investigative lens we used in Part 1, we can draw the conclusion that Black patients are being given these codes because of clinician bias. If a Black patient is experiencing problems related to their employment, would they be given the same Z code as a white patient? Or would the onus be put on the individual, rather than the social determinants of health that affect Black communities?
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We’re not saying that all of these clinicians are being openly racist when they walk into the exam room in these coding instances. Biases can also emerge when clinicians hold preconceived notions they may not even be aware of—not taking time to understand the type of communication a patient uses (e.g., non-native English speakers, AAVE, etc.) or acknowledging that medical settings are stressful for patients who have been repeatedly mistreated, and as a result, they may have difficulty communicating. Conversely, taking more time to learn more about patient details and putting additional effort into documenting more thoroughly may have racial disparities, as one study showed, Black and Hispanic patients had 25% less facetime with clinicians over a year compared with white patients. In our investigation we identified that many clinicians do document SDoH needs for Black patients, but that bias within that documentation may still occur.
Our investigation into SDoH coding practices highlights the existing disparities present within communities and within the health care system, particularly those that impact BIPOC patients. Prevalent SDoH codes among Black patients underscore systemic barriers rooted in structural racism and limited resources. Biased coding practices depending on a patient’s race further exacerbate these disparities. Putting a patient’s health into the wider context of their SDoH is crucial—as well as addressing implicit biases around a patient's circumstances (e.g., employment status, level of education, etc.). It’s an important step towards dismantling systemic inequalities and ensuring equitable access to care and resources for all individuals.
This is part three of a three-part series of thought leadership from Violet.
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