Social Determinants Of Health (SDOH), is defined by CMS as “the range of environmental, social and economic factors that can impact health outcomes.” According to the Centers of Disease Control and Prevention (CDC), the conditions in which people work, live, and learn can impact a significant range of health risks and outcomes. Lack of resources, including poverty and distance to health care, can result in poor outcomes for lower-income brackets and other vulnerable populations.
Unfortunately, many people in the US don’t have access to the care they need due to a lack of financial and other resources. One in 10 people have no health insurance, resulting in being less likely to have a primary care provider, and even if they are able to be treated, are less likely to be able to afford the medications and other health services needed. In other cases, many people living in rural areas are too far from the nearest hospital or doctors’ offices.
Medical interventions, either from a distance (for example, through telehealth programs) or in person, can improve access to care, as can improving insurance coverage to enable patients to get important services such as cancer or heart screenings and prevent more chronic illness down the road.
AI Analytics Focuses on Identifying SDOH Cardiovascular Risk
Providers and others serving these populations are making SDOH an increasing topic of research, by turning to artificial intelligence capabilities which in turn is critical to improving risk identification, care outcomes, and a reduction in disparities in access to and quality of care.
Cardiovascular disease is one prime example as it increases worldwide, especially in low- and middle-income countries, and in the US, particularly among communities of color. Cardiovascular disease is the cause of nearly a third of all deaths globally, and as might be expected, the incidence is especially high among people in the lower socioeconomic areas, as is the upward trend due to SDOH factors.
Changing social and environmental influences, such as smoking, obesity, and the increased consumption of cheaper processed foods among those lacking access to fresh produce and other whole foods, in addition to genetics, are some of the underlying causes and contributing factors of cardiovascular disease. Incorporating these determinants into artificial intelligence models helps researchers and providers do a better job of predicting chronic disease risk factors, including environmental and socioeconomic.
New York University researchers have found that machine learning can accurately predict the risk of heart disease while guiding health care providers in choosing the most appropriate treatment. When social determinants of health are factored in, providers can do a better job identifying and treating potential cardiovascular issues before they become chronic or acute.
Studies have also shown that cumulative exposure to daily stresses or other ongoing trauma due to social determinants of health increases the risk of cardiovascular disease. Additionally, there is a strong link between mind-heart-body influences on health: positive or negative thoughts and attitudes can impact cardiovascular health.
Health care providers can only do so much, as a patient’s health is also heavily influenced by what goes on outside a medical facility, according to Amy Andrade, former assistant vice president of research at Meharry Medical College’s Data Science Center. For AI to succeed in improving social health outcomes, providers need sufficient amounts of data to address community needs, based on well-designed algorithms. Once underlying issues are addressed, health care outcomes can be significantly improved.
AI Reduces COVID and Related Healthcare Disparities
The COVID pandemic highlighted more than any other recent events just how diverse populations and people of color are affected by the virus, including close living conditions, lack of health care, poor diet, and other factors. According to UnitedHealthcare’s chief consumer officer Rebecca Madsen, more than 80% of our health is impacted by conditions outside of genetics, such as social determinants of health (SDOH). Among these are economic, social, gender orientation, and other markers that can result in unequal treatment and access to quality health care.
UnitedHealthcare is one of the providers with the goal of improving response to social determinants of health needs with the aid of a predictive analytics advocacy model designed to identify and connect a patient’s needs to available community resources.
In another SDOH study published in the journal Cancer of the American Cancer Society, researchers surveyed 135 women with gynecological cancers at a gynecologic oncology clinic of Olive View- UCLA Medical Center, known as a public safety-net hospital. Many of the participants were immigrants living below the federal poverty threshold, with a distress screening and social needs assessment, along with other surveys. The majority of patients had at least one unmet social need, such as isolation and lack of safety at home, with lack of money and transportation to obtain medical care. Housing instability and food insecurity are closely followed as threats to good health outcomes, frustrating one of the prime goals of CMS and other healthcare organizations.
M-Scribe: Helping to Make Sense of Special Billing Issues
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