HDG #018: Z-codes aren’t working—but maybe they could?
Read time: 5 minutes
Greetings, Gurus! Today we're diving deep into Z-codes, those broad (and sometimes absurd) diagnosis codes that doctors can encode onto a claim to (in theory) track, understand, and document a patient's social, familial, and other external factors.
First things first, what are Z-codes?
The premise of Z-codes is ambitious—they provide a potential path towards greater health equity by allowing us to measure and track social determinants of health, hopefully leading us towards greater health equity.
Z-codes tie back to the concept of social determinants of health (SDOH)—those non-medical factors that have a massive impact on our health. And that's precisely where Z-codes were supposed to come into play. As part of the ICD-10 diagnosis code system, they were specifically designed to capture these social determinants of health (SDOH) onto a medical claim to help inform care and other programs.
Well over 1,000 Z-codes are in existence. Some are pretty standard and make a lot of sense, like Z59.0 for homelessness, or Z91.14 for a patient's intentional underdosing of medication regimen due to financial hardship. But then you have some that just make you go, "Wait, what?" Like Z63.1 for problems in the relationship with in-laws (yeah, that's real… although I can see how that could impact safety, mental health, and family life).
FY2024 has seen some new Z-codes ranging from a family history of adenomatous polyps (Z84.89), a child in the custody of a non-relative guardian (Z62.24), to a code for a child welfare exam (Z02.84). We even got codes for carriers of specific bacterial diseases (Z22.34-) and for identifying resistance to carbapenem (Z16.13). New codes also popped up for carriers of other specific bacterial diseases (Z22.34-) and for identifying resistance to carbapenem (Z16.13). You can see the codes are not all specific or limited to SDOH—many of them are classified broadly as “factors influencing health,” which can be many things.
But here's the twist: they're not being used as much as we'd like, if at all.
Despite the growing recognition of the role of social determinants in health outcomes, Z-codes aren't being used as much as we had hoped. A 2019 study by CMS showed that only 1.59% of 33.1 million continuously enrolled Medicare FFS beneficiaries had claims with Z-codes. It's a modest rise from 1.31% in 2016, but still a tiny drop of water in the ocean.
CMS continues to bang the Z-code drum, regularly releasing helpful information about how to best use them and why, like this:
https://www.cms.gov/files/document/zcodes-infographic.pdf
Reasons for underuse are many.
Time constraints for providers, the optional nature of the codes, their breadth yet limited scope in accurately capturing a patient's "truth", and frankly, the inertia of old habits.
Based on my experience, I've noticed certain trends:
Time Constraints: Healthcare providers often face time constraints in their practice, making it challenging to thoroughly document social determinants of health. The urgency to provide quality care can result in the unintentional oversight of Z-codes.
Optional Usage: Z-codes are not mandatory, and as a result, their inclusion in claims may not be prioritized. This voluntary nature can lead to underutilization, limiting our comprehensive understanding of patients' overall health.
Broad Yet Limited Scope: Although Z-codes offer broad categories, they may not capture the intricate nuances of a patient's circumstances. This limited scope can impede the accuracy of data and hinder our ability to address health disparities effectively.
Low Utilization: Analyzing claim data has often revealed minimal usage of Z-codes. This trend highlights the need to explore alternative approaches, such as interoperable and exchangeable standards, to capture and exchange information about patients' social determinants of health and external risk factors.
. . .
While Z-codes have the potential to drive positive change, it's essential to acknowledge their limitations. While Z-codes can provide an incredible depth of information, they're not the silver bullet for capturing social determinants of health and propelling health equity.
We're going to need other interoperable and exchangeable standards to fully grasp, document, and exchange info about a patient's external risk factors. This is where SDOH screening and interoperable data standards for SDOH data elements (like those the Gravity Project are working on), are so important and represent a huge piece of the future.
Finding a way to map or harvest these Z-codes in a hybrid approach might be preferable, as the Z-codes are robust if used fully and are by nature standardized. Conversely, there are numerous different SDOH screening tools out there, no standard exists, and there is no universal definition of how they might map to specific SDOH indicators.
Progress is being made, but I don’t think Z-codes were given a shot and have more potential than they’ve yielded because there is a lot of potentially good information to be had if more claims had this kind of data on them.
If you don’t believe me, just check out these Z-codes from AHRQ’s CCS-R Diagnosis Grouper
(And here’s a downloadable spreadsheet for you to pull into Excel):
https://docs.google.com/spreadsheets/d/e/2PACX-1vQ2-s2Yag0ActXt5g4ivl8RQ356zP47TnQEi4uKl8GnF3_MRqoAr06dFeD1lwZtbguVh-plL0MOI1av/pubhtml?gid=0&single=true
. . .
Actionable Idea of the Week:
This is a call not to dismiss Z-codes because they are underused, and to start exploring them in your analyses or while coding claims. Consider them as one of many tools in your toolbox for understanding and addressing SDOH. And remember, even a small percentage use of Z-codes can unveil significant insights when used in combination with other data points, which can be used to shape more effective health policies or interventions.
Consider the broader potential of Z-codes to create patient-centric care. Healthcare providers can use Z-codes to better understand their patients' social contexts, which could influence treatment plans or intervention strategies. Beyond the medical sphere, payers and health systems can utilize the Z-codes to understand the community-level factors affecting their patient population. This can guide targeted investment in community resources, addressing the SDOH at a systemic level.
Some organizations have even thought about incentivizing their use and incorporating them into certain measures. While this opens more logistical challenges, it is something that should be weighed carefully.
I’m confident some happy medium exists, we will just need to give it more thought, balancing administrative burden with benefit and most importantly—taking action.
What do you think?
How do you see Z-codes and other similar tools shaping the future of health equity? How do you imagine we can evolve beyond them? Hit reply and let me know your thoughts or how you’ve seen other organizations tackle this.
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See you next week!
-Stefany
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