Data-driven strategies and actionable insights can help healthcare close the gap on one of the most critical issues of our time – health equity. It’s a significant challenge confronting many healthcare organizations in terms of value-based reimbursement models and achieving care parity, one that requires expanding their view of health equity and social determinants of health (SDoH).
The prevalent view focuses on organizational initiatives related to patient experience and patient relations. However, this fails to consider how actionable data comes through as a natural occurrence of a patient’s interaction with the health system staff and care team – including those charged with managing the revenue cycle.
For example, the W.K. Kellogg Foundation estimates that health disparities contribute $93 billion in excess costs for medical care. By eliminating those inequities by 2050, we can reduce the need for more than $150 billion in medical care costs. In addition, healthcare professionals can better inform health equity strategies using the data with which coders and revenue cycle management professionals interact daily.
Providers, payers, and regulatory agencies are all seeking ways to identify and close disparities across sex, race, ethnicity, socioeconomic status, and other factors. New quality-based care models further impact this trend, requiring the creation of a health equity plan, collection of beneficiary-reported demographic and SDoH, beneficiary-level risk adjustments, and all pertinent information related to language, race, ethnicity, gender identity, and sexual orientation.
SDoH Data Tells a Story
Every healthcare organization has access to an immense amount of information that can help identify and better understand inequities in care delivery and help to inform initiatives to improve health equity across communities. That data is often hidden in revenue cycle management processes, data that can bring an action to life. Healthcare providers can address "catch-and-patch" healthcare by finding opportunities to improve care in underserved communities.
Analyze any multiple populations, as well, to help in clarifying health disparities within the United States. For example, teenage pregnancy, smoking, obesity, and lack of education are often concentrated within vulnerable communities. Disadvantaged groups such as those with mental health issues, substance abuse disorders, incarceration, homelessness, and the LGBTQ+ community show additional patterns of health inequities. Higher hospitalization rates and lengthy stays are tied to SDoH, as are the need for post-acute care in skilled nursing facilities and long-term rehabilitation centers.
These metrics are essential to a patient's SDoH story when physicians and other care team members take the time to enter comprehensive documentation in the patient's chart. In addition, documentation allows coders to assign appropriate codes for tracking and trending SDoH patterns, enabling healthcare organizations to understand their patient populations better and intervene as appropriate.
Addressing issues such as food insecurity by linking patients to programs such as Supplemental Nutrition Assistance Program, or SNAP, Meals on Wheels, or food pantries can reduce malnutrition rates and improve health outcomes. For example, patients who receive SNAP – 42 million low-income Americans – report excellent or very good health compared to non-participants. Improved birth outcomes and long-term health issues in adults are seen among those receiving SNAP, too, so early access is key.
The Role of RCM
Coders and RCM professionals interact daily with data invaluable to inform health equity strategies – particularly since the transition to the ICD-10 code set, which created an environment rich with data detailing SDoH. Code sets have expanded over the years to capture SDoH information at a level impossible with other data sources. For example, ICD-10-CM Z codes can record highly detailed SDoH directly into the patient's record, providing deeper insights into health factors.
There is a solid case for RCM in any comprehensive SDoH/health equity strategy. Eliminating care inequities by 2050 can lead to a drop in the $93 billion in excess Medicare care costs are the $42 billion in lost annual productivity.
Reimbursement is closely linked to quality, adding to the evidence that supports RCM. The use of outcome measures by CMS determines a hospital's overall quality, including patient experience, mortality, effectiveness and safety of care, readmission rate, and excess stay.
The greatest challenge for many provider organizations is sifting through tons of data to discover health equity insights. This requires a governance committee to design policies and procedures to link patients with community services. Integrating SDoH with clinical data and care plans fully, it also requires addressing four major data concepts:
- Screening tools: Used to identify patients with social risk factors. Numerous existing tools can be leveraged, including some embedded within EHR systems.
- Diagnosis/Identified need(s): Reporting SDoH issues identified by screening tools.
- Interventions: Actions must be taken to address the specific SDoH needs.
- Goals: What is expected to be achieved to resolve the patient’s identified need(s).
It’s also important to conduct audits on documentation quality and coding accuracy and provide appropriate feedback. Finally, success requires data interoperability, a standard set of values, and the capability to harness data for analytical purposes.
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