SummaryThe FY 2022 update of the ICD-10-CM includes 19 new Z codes that relate to social determinants of health (SDOH). Z codes present an opportunity to standardize and improve patient SDOH data collection to assist stakeholders in addressing non-clinical needs that impact health outcomes and healthcare costs.
Addressing SDOH factors has been shown to be associated with both positive health outcomes and lower costs. Providers, payers, and manufacturers are continuously seeking to better understand the conditions that impact patients beyond the clinical setting. Examples include:
- Safe housing, transportation, and neighborhoods
- Racism, discrimination, and violence
- Education, job opportunities, and income
- Access to nutritious foods and physical activity opportunities
- Polluted air and water
- Language and literacy skills
Capturing SDOH factors for patients occurs through multiple channels, and challenges in standardization of SDOH data collection and usage have posed the need for a more streamlined data collection method. One tool that can be leveraged to capture and track non-clinical needs of patient populations is the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes. Chapter 21 of the ICD-10-CM, “Factors influencing health status and contact with health services,” includes a range of SDOH factors collectively referred to as Z codes. SDOH codes located primarily in the Z code category include outcome risks related to education and literacy, housing and economic circumstances, income and loneliness, and other factors related to primary support groups, including family circumstances.
The Centers for Medicare and Medicaid Services (CMS) have added and revised 169 ICD-10-CM Z codes over the last 6 years to document patient SDOH data. The FY 2022 update includes 19 new codes to help providers collect data on the non-clinical conditions impacting health outcomes for their patients. These new codes were implemented on October 1, 2022. Examples include:
- Z55.5: Less than High School Diploma
- Z58.6: Inadequate Drinking/Water Supply
- Z59.0: Homelessness
- Z59.4: Lack of Food
- Z59.8: Other Problems Related to Housing and Economic Circumstances
Underutilization of SDOH Z Codes
Despite the availability of the Z codes, they have not been widely utilized by providers. In a recently published report entitled “Utilization of Z Codes for Social Determinants of Health among Medicare Fee-for-Service Beneficiaries, 2019,” the CMS reported that among the 33.1 million continuously enrolled Medicare fee-for-service in 2019, 1.59% had claims with Z codes, as compared to 1.31% in 2016. Intake questionnaires report 88% of healthcare organizations are now completing some form of screening for social needs with their members, however, the data is inconsistently collected. The top 5 provider types representing the largest proportions for Z codes reporting are:
- Family practice physicians (15%)
- Internal medicine physicians (14%)
- Nurse practitioners (14%)
- Psychiatry physicians (13%)
- Licensed clinical social workers (12%)
The CMS report published in September 2021 highlights a slight increase in utilization but underscores the opportunity to further enhance consistent Z code utilization across all sites of care and all practice types. Underutilization may be due to the codes not being linked to a reimbursable service or procedure, lack of awareness of the codes, lack of clarity on who can document the codes, and when and why they should be used.
Inovalon’s MORE2 Registry® claims data can be leveraged to evaluate current Z code usage. This data can be used to understand which stakeholders are most often reporting Z codes as well as specific non-medical factors facing a specific patient population.
Value of SDOH Z Codes for Multiple Stakeholders
Studies have reported that 20% of health outcomes are related to clinical care or treatment received while as much as 80% of health outcomes are related to patients’ SDOH.1 Increased utilization of Z codes to identify patient SDOH factors is likely to provide value for providers, payers, and manufacturers as well as enable the provision of more targeted support. Analyzing Z code data can assist stakeholders in improving access and coordination and quality of care through understanding the detailed non-clinical factors impacting specific patient populations. Specifically, data analysis of Z codes offers opportunities for stakeholders to:
- Identify and address most prevalent social risk factors within a specific disease area or target patient population
- Understand and address issues related to adherence and lack of follow-up in receiving care
- Initiate and track referrals to necessary social services
As the trend toward value-based care continues to increase, Z codes can be leveraged to provide manufacturers and payers more robust insights into complex patient populations that are managed through these arrangements. For example, Z code data can be useful when risk adjusting a benchmark or target contract price in a value-based purchasing arrangement. Additionally, increased insight into the social risk factors of patient populations can also allow stakeholders to deploy targeted support services and identify population-level trends to improve health outcomes and quality of care while reducing healthcare costs.
Increasing Utilization: What Payers/Manufacturers Can Do
Avalere can leverage its data assets and expertise to help our clients understand social and environmental barriers to access, develop data-driven patient support and access services, identify the impact of SDOH on key health outcomes such as hospital readmission and medication adherence, and support health equity initiatives across therapeutic areas. More information about the importance of data collection and Social Determinants of Health can be found in the second segment of the Avalere Health Essential Voice podcast series.
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- Hood, C. M., K. P. Gennuso, G. R. Swain, and B. B. Catlin. “County health rankings: Relationships between determinant factors and health outcomes,” American Journal of Preventive Medicine 50.2 (2016): 129–135.
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