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The idiom social determinants of health (SDOH) has been buzzing around the country for several years. The widespread acknowledgement behind this concept proves these determinants to be essential in achieving the best health outcomes for a variety of individuals. Although many health plans have been working on projects to address these factors, we’ve witnessed a lack of transparency, socioeconomic challenges, and isolation. 

Social Determinants of Health-What are Health Plans Doing?

According to research performed by Health Data Management, “The broad interest of a wide variety of health plans in SDOH seems to run counter to the belief that health insurers haven’t cared about initiatives that would bring long-term improvements to the health of members, because those members might be in different plans by the time their health improved, and so a health plan wouldn’t derive benefits from investments in members’ long-term health.” The current landscape of healthcare must take into consideration the above factors when analyzing the impacts of a patients’ health.  

As evidence and research increases, its suggesting SDOH needs of members can have an instant impact on consumers. Ghita Worcester, Senior Vice President of Public Affairs at UCare revealed the story of a 70-year-old woman who only had enough money to buy food from vending machines, causing her to become pre-diabetic and requiring her to be on 7 different medications. Not having access to healthy food caused a direct impact on her health. These kinds of stories are multiplying across many different health plan platforms and are triggering awareness around value-based care. 

While health plans are diving into addressing SDOH, they’re discovering it involves more than offering low-cost transportation, vouchers for food, and education. The approach needs to be personalized to the individual members and provide assistance in a way that will be effective for health plan enrollment.  

The impulse to address SDOH requires advanced information technology while gathering non-clinical information and creating an actionable workflow. Sam Zurl, Data Scientist at Softheon discussed his SDOH algorithm to attack this head-on, “We store our member’s demographic information and our claims data can give us insight into what groups of people make the most claims, meaning which members need the most help.” He explains further by telling us, “We use predictive models and statistics to predict based off of trends within the model, what future outcomes could occur.” Softheon’s models can handle a larger scale of data than humans can, especially while using the Poisson Regression formula. Zurl’s research in refining a SDOH algorithm intends to distinguish relationships between demographics, gender, age, marital status, location, and relationship to the subscriber to predetermine how many claims a consumer will submit each year.  

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