In Part 1 of this thought leadership series on coding discrepancies, we looked at health inequities across patient populations and the coding practices overrepresented in Black and AAPI patients. Some of the most glaring discrepancies we found included: Black patients accounting for 32% of the code, “bizarre personal appearance” (nearly twice the 16.1% patient population), and 28% of the code for “need for continuous supervision.” For AAPI patients, we saw the code for “encounter for childbirth instruction” was nearly triple the population size, and the coding for “worries” was more than double the population size of AAPI patients. 

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In Part 2, we’re looking at the flip side of this coding bias, into something I call kinder coding—the ways in which white patients are given the benefit of the doubt in clinical encounters. 

What is kinder coding?

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If you think of the most common reasons a person would present in a doctor’s office, you most likely wouldn’t think about causes like technology, lacrosse, or dog-walking. Yet these are some of the top ICD-10 codes that visits with white patients are disproportionately coded for. 

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According to the US census, 61.2% of the population is white. However, white patients make up 80.7% of the “Activity, other involving computer technology and electronic devices” code (Y93C9), 79.7% of the “Garden or yard of unspecified non-institutional (private) residence as the place of occurrence of the external cause” (Y92007) code, and 79.4% of the “Activity, lacrosse and field hockey” (Y9365) codes.

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Looking at the data.

We created this 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.

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When reviewing racial demographic breakdown per codes, we noticed some curious and alarming patterns emerge. We reviewed the racial health disparities we found in Part 1 of our coding series.  And while we were expecting those, and unfortunately were even able to use those as an informal validation of our model, this other type of coding bias that emerged with white patients was not one we foresaw, nor have we seen a lot of information about.

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I’ve termed this racial coding bias “kinder coding" because it became clear as I reviewed the data that clinicians were giving white patients the benefit of the doubt by taking more time to understand and give more context to their injuries and ailments.

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Why do I say this? It’s because most of the top codes that white patients are disproportionately coded with ICD-10 W and Y codes. W codes provide information about the circumstances of the injury or ailment (e.g. falls, bites, explosions, etc.) The code even gets specific enough to differentiate between Fell from chair, initial encounter (W07.XXXA) and Fall from chair, subsequent (W07.XXXD). Y codes also reference external causes, such as locations for the injury or ailment, and also have a high level of specificity for them. Y codes include details such as Activities involving caregiving (Y93.F) and Nursing home as the place of occurrence of the external cause (Y92.12).

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What becomes apparent from this data is that clinicians are taking time enough in their sessions to get a high level of specific information from white patients, while getting considerably less details from patients of other races. It’s true that these highly specific codes are only included in a small minority of patients, but in order to use these codes, clinicians have to spend a lot of time understanding why the patient is coming in.

Average physician facetime per year for white patients: 70.0 min

Average physician facetime per year for Black patients: 52.4 min

Average physician facetime per year for Hispanic patients: 53.0 min

‍_{Reference: Gaffney, Adam et al. “Trends and Disparities in the Distribution of Outpatient Physicians' Annual Face Time with Patients, 1979-2018.” Journal of general internal medicine vol. 38,2 (2023): 434-441. doi:10.1007/s11606-022-07688-x}

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What is so “kind” about this way of coding? The level of specificity can dampen the perceived fault of the patient in their injury and ailment—something that is an insidious part of systemic racism in health care for BIPOC patients. When a clinician uses codes to specify that an injury was caused due to “Activity, food preparation and clean up” in “Unspecified place in single-family (private) house” it increases the possibility that the provider takes the patient’s story at face value and puts the blame towards external circumstances. Juxtapose that with fault-bearing codes such as “Inappropriate diet and eating habits” (Z724) and “Acculturation difficulty” (Z603), which BIPOC patients disproportionately make up, and the implications these coding discrepancies may have for care are quite alarming. 

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Here’s an example of how this could play out in a doctor’s office:

• A teenage white patient presents for an injury to their knee. Through a series of detailed questions, the clinician learns that the patient was having a BBQ with friends and their football game escalated into an impromptu wrestling match. The patient’s friend accidentally hit their knee, causing the injury. The patient encounter is coded with “Activity, rough housing and horseplay” (Y9383).
• A teenage Black patient presents for the same injury to their knee. The clinician does not get nearly as many details about the context of the patient’s injury, and shows biases towards the patient, so instead of a more innocuous code, this patient could end up with a code like “Assault by unspecified means” (Y09), or “Assault by unarmed brawl or fight, initial encounter” (T7802XA).

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In this example, we see a disparity in both time spent with patients as well as trust towards the patients. It is well documented that patients of different races and ethnicities are treated differently when going for medical care, especially in a health care system built to prioritize white patients. Health disparities stem from inequitable care provided to BIPOC patients, and there has been a focus in recent years in addressing the roots of these issues. What I have not seen as a prominent part of the conversation is how coding practices can lead to and exacerbate the negative experiences and negative health outcomes of patients. 

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However, as more research is released, these issues are starting to come to light. A recent article in Health Affairs reviewed over 40k notes from EHRs and found that Black patients had over 2.5 times the odds of having at least one negative descriptor in the history and physical notes compared to white patients. Another recent journal article found that Black patients were being coded for higher rates of noncompliance with Z-codes. 

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As we have more access to aggregated data, such as the data set we created breaking down provider patient panels by race, these larger-scale coding discrepancies and biases will continue to emerge. And with these discrepancies—I would have to guess—we will continue to see patterns emerge of the privileging of white patients in documentation. “Kinder coding” should be a standard practice across all communities, with clinicians providing consistent facetime, having consistent discussions with patients, and consistent documentation practices as part of inclusive, quality care.

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This is part two of a three-part series of thought leadership from Violet. 

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