Social Determinants of Health are many non-medical things that can affect how healthy someone is. These include money situation, education, the neighborhood where someone lives, social life, and access to doctors and hospitals. Studies over many years show that these things often matter more than the medical care itself.
Doctors and clinics that take care of many patients need to know about these social factors. They collect information about a patient’s social and money situation, add it to health records, and use it to plan care. This helps providers find patients who have problems like no stable place to live or not enough food. These problems often lead to worse health and more doctor visits.
Using this information helps doctors not just treat symptoms but also fix the deeper causes of health problems. This can lead to better health for patients over a long time.
Artificial Intelligence (AI), like Natural Language Processing (NLP) and machine learning, helps look at large amounts of patient information. It can find important social factors that humans might miss. This is faster and can be more accurate than reading records by hand.
A company called IQVIA made an AI system that was given an award in 2023. This system reads patient records to find social details like income, habits, and living conditions. Carina Jenkins from IQVIA said their AI found 56% more patients at risk than before. This helps social workers and care teams give the right help.
Finding more patients who need help means doctors can give support sooner. This leads to better patient care, fewer hospital visits, and fairer health outcomes.
IQVIA’s system uses both set rules and machine learning. It shows doctors how it makes decisions, which helps build trust. This is important because many health groups are careful about using AI.
In the United States, agencies like the Centers for Medicare & Medicaid Services (CMS) and the National Committee for Quality Assurance (NCQA) require medical groups to collect and report social determinant data. This is to improve fairness and make sure care focuses on the patient.
Programs like ACOREACH reward doctors not just for seeing many patients but for better and more efficient care. This includes using social data to manage health and reduce avoidable trips to the hospital.
When health groups use AI to analyze social data, they can meet these rules better. They avoid penalties and can earn rewards.
AI can do more than find patients at risk now. It can also predict who might be at risk later. Healthcare spending in the U.S. is expected to be very high by 2028. This makes it important to use AI to plan better care and save money.
Zyter’s TruCare platform uses AI with automation and outside clinical support to sort patients into risk groups: high, medium, and rising risk. This helps doctors act early.
Using this sorting can reduce healthcare costs by up to 25%. It also stops up to 30% of medium-risk patients from becoming very costly patients within five years.
The key is moving from looking only at past data to using current health records, social data, and authorizations. This helps find problems earlier and fix gaps in care.
Tools like WellStack’s Population Health Decision Hub gather all data—claims, clinical info, and social factors—in one place. This helps to sort risks, predict problems, and find care gaps easily.
Health administrators can use these tools to send help to patients who need it most. For example, they can run automated campaigns and create specific care plans. This is important when resources are limited and some communities need more help.
These systems also let doctors, social workers, and community groups work together better. They can handle both health and social needs quickly.
Using social data well needs teamwork. Doctors, social workers, mental health helpers, and community groups work together to help patients fully.
Platforms like Persivia’s CareSpace® support this teamwork by sharing social data in a way that all care team members can use. They help care teams see the full patient story, talk to each other, and make shared care plans.
Working together helps patients get help with things like housing or jobs. This makes it easier for patients to follow treatment plans and avoid preventable hospital visits.
Medical offices often have too much paperwork and tasks related to social data and coordination. AI automation can help by making these processes faster and easier.
For instance, AI can handle prior authorization requests by quickly approving simple cases. This reduces delays and saves staff time.
AI also helps by sending personalized messages to patients. This keeps patients involved and helps doctors fix care gaps found by AI predictions.
Combining AI with outside clinical support, like in Zyter|TruCare’s model, helps manage population health well. AI does routine work while experts handle tough cases. This setup saves money and helps clinical teams work better without lowering care quality.
AI platforms also bring in patient monitoring data to one place with secure communication. This allows managers and clinicians to watch patient health in real time and react quickly.
Healthcare leaders and IT managers must add AI tools and social data into existing systems. These AI tools need good data from different places and must follow privacy rules like HIPAA.
When done well, these tools help make useful dashboards, find risks, and support doctor decisions. Showing how AI makes choices, like IQVIA’s “open box” method, builds trust.
IT teams also need to plan secure data sharing among community partners who give social data. Using shared platforms like WellStack’s Decision Hub or Persivia’s CareSpace® helps teams work together across many care levels.
Healthcare groups in the U.S. should think about using AI systems that analyze social data and automate work. These tools help provide care before problems get worse, reduce workload, and focus on the patients who need help most. As managing health for large groups grows harder, using these tools will be important to keep quality care and meet rules.
IQVIA’s NLP technology analyzes complex unstructured patient records to extract critical insights into patient care and disease states, enabling more precise and personalized healthcare interventions.
IQVIA’s NLP helps clinicians and social workers by accurately identifying SDoH such as socioeconomic, behavioral, and environmental factors from patient data, facilitating targeted interventions for at-risk patients.
The NLP technology enabled a 56% increase in identifying at-risk patients based on SDoH, allowing social workers to focus on patients who need targeted care interventions.
IQVIA uses both rules-based and machine learning approaches in an ‘open box’ system, allowing users to understand and verify AI results, which builds clinician trust and promotes adoption.
The NLP system integrates both rules-based methods and machine learning techniques to provide flexible, accurate, and interpretable healthcare insights.
Capturing SDoH is mandated by programs like CMS and NCQA to improve quality reporting and is increasingly vital for value-based care initiatives such as ACOREACH.
IQVIA NLP unlocks insights into social determinants that significantly affect health outcomes, supporting more effective population health strategies and resource allocation.
The award recognized the technology’s advanced ability to analyze unstructured data accurately and transparently, enhancing patient care by addressing social determinants of health effectively.
It assists clinicians in identifying at-risk patients and empowers social workers to deliver targeted social interventions, improving overall patient outcomes.
Understanding SDoH is critical for compliance with quality reporting and value-based care models, making it essential for healthcare organizations to improve care delivery and health equity in coming years.
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