By ARJUN GOSAIN

In the United States alone, one in ten people live in poverty, 10.2% of households are food insecure, and more than half of people living below the poverty line are transportation insecure. These statistics represent social determinants of health (SDOH) measures that describe a patient’s experience outside hospital walls. 

Health.gov defines SDOH as “the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.” This definition argues that a patient’s experiences are just as crucial if not more telling than their biology.

And this makes sense as a person who is housing insecure may not have the same access to nutritional food, transportation, or social support. Additionally, some patients, in their efforts to maintain health, may experience discrimination based on their skin color or religious beliefs. 

Some studies have found SDOH can drive up to 80% of health outcomes. This means that the traditional healthcare model—hospitalization, healthcare delivery, and treatment—only affects a mere 20% of a person’s overall health. To tap into this 80%, healthcare professionals need data. However, SDOH data collection poses significant challenges.

SDOH Overview

Before we dive into data collection, let’s review the specific measures of SDOH and why they should take top priority among healthcare professionals. 

SDOH concepts include:

• Employment insecurity: Measures whether the patient is employed and their current employment or unemployment experience. This includes whether they were harassed on the job or experiencing unequal pay. Employment insecurity can lead to financial stress, mental health problems, and reduced healthcare access. 
• Psychological circumstances: Measures current events that are affecting the patient’s health. This encompasses a wide range from unwanted pregnancies to exposure to war or violence. Stress, anxiety, and other negative emotions can have a direct effect on a patient’s physical health and contribute to disease development.
• Housing insecurity: Notes whether a patient has a consistent place to live or is forced to move regularly. Homelessness or housing insecurity can lead to exposure to the elements, mental health challenges, and increased vulnerability to infection.
• Social adversity: Examines a patient’s social experience including any discrimination or persecution the individual may be facing. Increased social adversity can cause an individual to socially isolate and develop feelings of depression. 
• Transportation: Observes the patient’s access to transportation including available public transport. Missed appointments can be the direct result of transportation inaccessibility which leads to a decrease in the quality of care. 
• Food insecurity: Indicates whether a patient has adequate food access and safe drinking water access. Receiving adequate nutrition is essential for maintaining optimal physical health. For example, if a child is food insecure, it can lead to serious developmental issues and chronic disease.
• Education and literacy: Observes a patient’s ability to read and comprehend hospital paperwork. Note that individuals with higher literacy and education rates typically make more informed health decisions.
• Occupational risk: Examines how a patient’s current employment affects their overall health. Determines if their job site places them at risk of toxin exposure, physical harm, undue stress, or other hazardous conditions that can contribute to injuries or illnesses.
• Economic insecurity: Measures a patient’s poverty level to determine if copays, rent, and hospital bills are manageable. A patient living with inadequate finances will face a greater barrier to quality care.
• Lack of support: Notes whether a patient has reliable support when experiencing difficult circumstances such as the death of a loved one. If a patient has a present support network, they will be able to receive practical, emotional, and physical assistance in times of need. 
• Upbringing: Takes a patient’s childhood, family, and upbringing into account to assess if a patient is carrying trauma from previous years. Adverse childhood experiences can increase the risk of chronic diseases and mental health issues later in life. 
• Language: Examines any language or communication concerns, so that a patient can both communicate their issues and understand oral and written treatment. Miscommunications can lead to misdiagnoses and inadequate treatment. 

These contributing factors cannot be ignored since, as previously stated, they can directly impact up to 80% of health outcomes. Thus, organizations that choose to neglect SDOH factors are only focused on the 20%. 

This is why providers must find ways to address SDOH in a meaningful and productive manner, which is where SDOH data comes in. The collection and analysis of SDOH data can help providers identify at-risk populations to provide informed, effective interventions. Measures like patient needs assessments and population-level health disparity analysis can let providers get to the root cause without the guesswork. 

SDOH Data Collection Challenges

SDOH data collection is a sensitive topic. After all, if a patient is experiencing abuse or is unemployed, they most likely would not disclose that information outright. Providers also have limited time to ask additional questions because many feel rushed during routine consultations and may not have the resources needed to collect SDOH data. 

Beyond SDOH data scarcity, there is the issue of standardization. How providers collect housing data, for instance, can vary across definitions and measurements, making quantifying data difficult. So, how can providers offer whole-person care with limited data and a lack of definitive measurements? The solution is three-fold. 

Solution 1: Focus on a Few Key SDOH Measures

As listed above, there are twelve total SDOH measures, each with variance and nuance. That said, providers can start by narrowing their scope. They can collect SDOH data on the largest, most obvious at-risk population groups. For example, if your city has a large homeless population, accountable care organizations can start to design programs that address these problems. 

Such programs may include services like free ride shares to and from a provider’s office and enrollment in nutrition programs. Or, an organization might provide air purifiers for at-risk populations to prevent respiratory illness during wildfire season. 

From there, organizations need to make each program measurable by setting benchmarks. For example, when providing ride shares, providers could measure those who participated in the program against those who did not and assess changes in routine care. 

Solution 2: Integrate with a Broader Pool of Data 

Because SDOH records are scarce, organizations and policymakers must look for information in as many places as possible, including clinical and claims data. Technological tools can also help widen the existing data pool.

Some data sources providers can use include:

• Comprehensive SDOH screening: Providers can begin to implement a more systematic and comprehensive SDOH screening protocol during visits. These standardized assessments can collect useful information and help providers understand the bigger picture behind a patient’s health. Questions like “How many nights over the past month did you go to bed hungry?” can uncover valuable SDOH insights. Recent use of PRAPARE for community health centers has attested to the progress of SDOH screening measures. This assessment and similar ones promise to provide useful access to invaluable data. 
• EHR (Electronic Health Record) integration: By integrating SDOH measures into a patient’s existing EHR, providers can compare SDOH information alongside the patient’s clinical data. This reduces the time needed for providers to locate files and enables enhanced coordination across care teams. 

Recent regulations that have mandated the use of HL7 FHIR APIs (Application Programming Interfaces) have sped up SDOH data sharing. This regulation makes greater interoperability possible and can help organizations overcome data silos associated with historical data collection methods. 

Additionally, it paves the way for increased patient engagement, empowering patients to take a more present role in their health management. Now, with access to their health data, patients can proactively monitor their symptoms and clearly communicate with their providers.

Solution 3: Contextualize Data Using Analytics

In order to use the SDOH data you’ve gathered, it must be contextualized. Healthcare analytics tools like customizable dashboards can help providers dig deeper into their patient populations and access data in context. 

For example, Arcadia Analytics’ SDOH dashboard can analyze SDOH by comparing it to other distinct measures, including:

• Utilization: Metrics like emergency room visits, hospital readmissions, and outpatient visits are analyzed within a dashboard view and compared to SDOH measures. This highlights the relationship between social factors and healthcare resource utilization. 
• Spending: How is cost impacted by SDOH? Providers can get a zoomed-in view of expenditures based on specific conditions. From there, providers can examine spending patterns and identify key correlations between SDOH and financial resource allocations.
• Demographics: Information on age, gender, and race can help providers bridge the gap between demographics-based SDOH disparities. 
• Geoanalysis: What insights can geographic data offer? Organizations can view geographic or regional data and pinpoint hotspots to provide location-based, targeted interventions. Even a zip code or census block can offer transformative health insights. 
• Condition prevalence: Organizations can get an inside look into condition prevalence in relation to SDOH. They then can analyze disease distribution across defined SDOH populations. 
• Language concerns: Identify language-related disparities using language-based measures to pinpoint barriers. Observe language proficiency, preferences, and translation service utilization to contextualize the cultural aspects of SDOH. 

Organizations can use these analytics tools to further health equity and make informed, data-driven plans. For example, these SDOH observations indicate the connection between high-risk SDOH population members and financial healthcare resource utilization:

• Members with SDOH concerns are 2x to be readmitted within 30 days than those without.
• Members with a housing concern tend to visit the ER 5x more in a calendar year than those without. 
• Members with social adversity have 6x higher per member per month PMPM costs than those without.

These findings reveal the need for SDOH-informed strategies to alleviate the burden placed on healthcare providers and patients alike. With SDOH evidence, organizations can continue to make the case for increased funding for health programs to measure and address these relevant socioeconomic factors. 

Conclusion

SDOH data collection has its challenges. The first step is to convince organizations and policymakers of its utility and promise to offer whole-person care. Without SDOH, providers are only taking in a fraction of their patient’s health. Therefore, the dismissal of SDOH only widens health disparity gaps and fuels a cycle of reduced patient engagement. 

However, the use of focused SDOH benchmarking, routine screening assessments, and robust analytics tools can help organizations take steps toward greater health equity. This gives each patient the potential to achieve and maintain optimal physical health regardless of their differing experiences. 

Arjun Gosain is a Data Architect on the Customer Insights team at Arcadia