Social Determinants of Health

Social determinants of health (SDOH) are the conditions in which people are born, grow, work, live, and age, and the wider set of factors that influence their health. These include: income and social status, education, employment, social support networks, neighborhoods and built environment, racism and discrimination, gender, sexual orientation, age, and religion.

While some people may be more vulnerable to certain SDOH than others, it's important to note that everyone is affected by them. SDOH is one of the most important predictors of a person's health. Poor SDOH can lead to increased rates of illness and death. They can also contribute to health disparities, which are differences in health that are closely linked with economic, social, and/or environmental disadvantages.

The Growing Use of SDOH Data in Healthcare

The use of SDOH data is growing in the healthcare industry as providers seek to improve population health outcomes. By understanding the social and environmental factors that contribute to poor health, providers can develop targeted interventions and programs that address the root causes of illness.

There are several reasons why SDOH data is becoming more important in healthcare. First, there is increasing recognition of the role that social factors play in health. Second, new data-collection methods are making it easier to collect information on social determinants. Finally, there is a growing body of research linking social factors to health outcomes.

The healthcare industry is under increasing pressure to consider the social determinants of health (SDOH) when making decisions about patient care. Additionally, with SDOH data becoming increasingly available, awareness of SDOH among healthcare professionals and integrating SDOH into healthcare decision-making can ensure its use effectively.

Benefits 

There are many benefits to using SDOH data in healthcare. For example, SDOH data can help providers identify patients who may be at risk for certain diseases or conditions and target interventions accordingly. 

Public health and healthcare professionals rely on accurate and up-to-date SDOH data to identify and address disparities in communities. SDOH data can help identify groups of people who are at risk for poor health outcomes and guide interventions to improve health outcomes. For example, poverty can lead to poor nutrition and increased stress, which can in turn lead to chronic diseases like heart disease and diabetes. 

Additionally, SDOH data can help providers improve their care coordination efforts, track the impact of interventions, and measure progress towards public health goals. By understanding the social and environmental factors that affect health, providers can better address the needs of their patients.

Collecting SDOH Data 

There are several ways to collect SDOH data. Some methods, like surveys, require input from individuals or organizations. Other methods, like mapping or reviewing public records, can be done using existing data sources. No matter which method is used, it’s important to ensure that the data is accurate and up-to-date. 

Challenges 

There are several challenges associated with using SDOH data in healthcare. One challenge is that the data can be difficult to collect. For example, some data may be collected by government agencies, while other data may need to be collected through surveys or interviews. 

Another challenge is that SDOH data can be hard to interpret. This is because the data can be complex and may not always be easy to understand. There’s no one-size-fits-all definition of what these factors are. What might be a social determinant of health for one person may not be for another. Additionally, SDOH can vary greatly from one community to another. This variability can make it difficult to develop interventions and policies that address SDOH. For example, a policy that aims to improve food security in a low-income neighborhood may not be effective in a high-income neighborhood. It's important to take into account the unique context of each community when designing interventions and policies related to SDOH.

It can be challenging to use SDOH data to improve health outcomes. This is because the data may not always be accurate. Accuracy is a major concern, and it can be difficult to ensure the data is complete. This can lead to inaccurate conclusions being drawn about individuals or groups, which can result in unfair treatment.

Other factors, such as poverty or racism, can contribute to poor health outcomes, and these factors are often not reflected in traditional healthcare data. SDOH data can help provide a more complete picture of a community's health and can help identify hidden disparities that may not be evident from other data sources.

There are a number of ethical considerations around collecting and using SDOH data. One of the main concerns is privacy. SDOH data often contains sensitive information about an individual’s social and economic circumstances. This information can be used to discriminate against individuals or groups if it is not handled properly.

Finally, there is the issue of consent. When is it appropriate to collect SDOH data without the consent of the individual? How can we ensure that individuals are aware of how their data will be used? These are important questions that need to be considered when collecting and using SDOH data.

SDOH Data and HCC Risk Adjustment 

Risk adjustment is a process that uses SDOH data to estimate the expected number of cases of a particular disease in a population. This information can be used to compare the actual number of disease cases in the population to the expected number and can be used to target prevention and early detection efforts. It can also be used to design and implement interventions to reduce health disparities. 

There is overwhelming evidence that suggests that SDOH are important drivers of health and health care costs. However, currently most Hierarchical Condition Categories (HCC) risk adjustment models do not consider SDOH. This can result in inaccurate measures of health care spending and quality. In particular, SDOH such as poverty, food insecurity, and lack of transportation can make it difficult for patients to access needed care. As a result, patients with more social needs are more likely to experience poorer health outcomes and higher health care costs. This also means that individuals who are at higher risk due to their social circumstances are not being accurately identified and accounted for.

Health care providers are always looking for ways to improve the accuracy of their medical risk adjustment and SDOH data can be a valuable tool in this effort. Inclusion of SDOH in HCC risk adjustment models would improve the accuracy of these models and allow for more targeted and effective interventions. There is already some evidence that this approach can be effective, and we must continue to invest in research in this area.

In the most recent Advanced Notice for Medicare Advantage (MA) payments, the Centers for Medicare & Medicaid Services (CMS) made known that it is asking for comments to determine if advancements can be made to the MA risk adjustment model to take into account the impacts of SDOH data on the health status of MA beneficiaries. CMS is extremely interested in suggestions that take into account factors that adjust for SDOH into the risk adjustment model that forecasts the respective costs of MA beneficiaries.

Conclusion

There is no doubt that social determinants of health (SDOH) have a major impact on our health and wellbeing. Despite this, healthcare systems around the world have been slow to address SDOH. However, there is growing awareness of the importance of SDOH and its impact on health outcomes. This is leading to more attention being paid to SDOH by policy-makers, healthcare professionals and the general public. If opportunities such as using technology to interpret data on SDOH and many more are seized, then it is possible to make progress on addressing SDOH. This would have a major impact on our health and wellbeing and would help to reduce inequalities in our healthcare system.

Now more than ever, accurate and evidence-based clinical documentation matters. The level of reimbursement and the healthcare resources available depends on the accuracy and specificity of documentation by physicians. Learn how you can capture every appropriate HCC code and get the reimbursements you deserve with A.I powered risk adjustment software from ForeSee Medical.