Why Add Social Determinants of Health to EHR

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Though not related to healthcare directly, social determinants of health (SDOH) often predetermine treatment efficacy. These factors describe patients’ environments—access to care, housing and food security, education, the community they live in, and more. Obviously, such determinants influence patients’ health risks and can even define health outcomes. Nevertheless, providers haven’t been taking them into account systematically.

Practitioners stably believe SDOH have little relation to clinical processes and workflows. Hence, there’s no room for them in EHR systems. But is it really so? We’ll look into the matter.

On the relevance of SDOH

While all the determinants may be excessive for care delivery purposes, there’s a group of SDOH that influence health immensely. Such determinants describe environments into which one is born and where one resides, works, and matures. They include income, social support, care and education availability, and employment. Together, these conditions shape the health status of individuals and communities. Disparities in any of these parameters translate into socioeconomic status (SES). The lower individuals fall on the SES scale, the poorer the health conditions they might have to endure. 

Amidst the pandemic, providers turned to diverse sources in search of dependencies and insights. Looking for answers, researchers discovered clear connections between the above-mentioned parameters and the risk of coronavirus in patients and communities. For example, researchers from OCHIN, a non-profit health information network from Oregon, examined their partner providers’ EHRs and established that African Americans are 2.5 times more likely to have COVID-19 in their medical records than Caucasians. Haven’t providers considered such correlations before? They have, it just worked in a different way.

A new phenomenon?

It’s not that providers have ignored social determinants throughout the years. Marketing and analytics specialists have been collecting SDOH with questionnaires, opinion polls, and diverse surveys. However, in this case, SDOH data collection mostly focuses on the popularity of some services among different patient segments. The results are mostly used across marketing departments and rarely reach clinicians and patients, the key health decision-makers. 

Having recognized the significance of SDOH for patient risks assessment and outcomes, providers have decided to marry EHR systems and SDOH. This can help upscale clinical decision-making and population health management. But how to reshape EHR development to make it possible? Healthcare experts offer some recommendations. 

SDOH-EHR integration tips

To consolidate researchers’ and providers’ efforts of enriching EHR data with SDOH, the University of California launched the Gravity project. The researchers developed a handy guide to help providers address such disparities as food insecurity, instability in housing, and transportation problems. Here are its basic steps: 

  • Defining the goal of screening and drafting intervention scenarios.  First of all, a provider needs to decide what social need they want to address and which health outcomes to aim at. Here experts recommend to “think big but act small.” This means clinicians can consider all of their patient populations, determine who are at risks imposed by socioeconomic factors, and then narrow the focus. For this purpose, chronic-condition patient populations may make a good target group. The team may choose a chronic condition with clinically validated social determinants and a well-developed management plan. This may facilitate SDOH-related health risks monitoring. 

When the social need is selected, it’s time to draft intervention scenarios. As a rule, it boils down to launching thematic programs and partnering up with relevant agencies and service providers.

  • Collecting SDOH without overloading clinicians. This is where providers’ marketing and analytics teams can assist. These teams have extensive experience in designing questionnaires, surveys, and predictive models for risk evaluation. 

However, there’s a catch. Though informative, such surveys may lose relevance promptly. Besides, participants’ replies are often based on their “here and now” experiences. Hence, the insights such surveys deliver may be situational.

To collect SDOH data, providers can also turn to public records. These may be census data or other sources featuring home addresses, phone numbers, car registrations, and more. Such data sources are updated regularly, so the conclusions they drive are up-to-speed and more stable.

  • Adding SDOH to an EHR system. There are several ways to aggregate SDOH into an EHR. First of all, administrative workers can add relevant data manually. While acceptable for small medical organizations, for large cross-state healthcare providers this time- and effort-intensive method is out of the question. They may go for adding socioeconomic data automatically from health information exchanges. At this point, providers will need IT specialists’ help. 
  • Ensuring interoperability. Addressing SDOH-related risks properly requires consolidated work of several providers. Therefore, it’s necessary to ensure interoperability between healthcare providers and social services in a region. For this matter, the Regenstrief Institute set up Logical Observation Identifiers Names and Codes (LOINC) in March 2020. The solution offers SDOH codes that help aggregate the data in EHR systems and make it interoperable across several healthcare organizations. Besides, in July 2021, the ONC published version 2 of the US Core Data for Interoperability. This collection of data classes and elements may power health and SDOH data sharing all across the country.

Summing up

Socioeconomic factors are paramount for defining health risks and outcomes at the level of patient populations and individual patients. This makes adding SDOH to EHRs a logical step. Continuous monitoring of these factors across patient populations and medical histories may help providers reduce health disparities for at-risk patients and other patients groups. What’s more, adding SDOH to EHRs may propel interoperability improvements across different healthcare systems and prevent healthcare system crises. 

Inga Shugalo is a Healthcare Industry Analyst at Itransition, a custom software development company headquartered in Denver, Colorado. She focuses on Healthcare IT, highlighting the industry challenges and technology solutions that tackle them. Inga’s articles explore diagnostic potential of healthcare IoT, opportunities of precision medicine, robotics and VR in healthcare and more.

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