Social determinants of health are non-medical conditions that affect how healthy patients are. Examples include economic status, education, housing quality, neighborhood, job risks, and behavior. These factors can change how diseases happen, how patients respond to treatment, and how well they get better.
Even though these factors matter, social determinants of health (SDOH) data is not often found in clear, organized formats in electronic health records (EHRs). Most of this information is written as free text by doctors, social workers, or other healthcare staff. Because the data is unstructured, it is hard to take out and use in making medical decisions or managing the health of groups of people.
More people now realize that SDOH data is important because organizations like the Centers for Medicare & Medicaid Services (CMS) and the National Committee for Quality Assurance (NCQA) require health providers to collect it for quality reports. Also, care programs that reward better health results use SDOH when they plan patient care. For clinics in the U.S., using SDOH data can help improve fairness in health and meet rules needed for payments.
About 80% of the data in medical records is unstructured. It is found in notes written by doctors, discharge summaries, social worker reports, and other documents. This large amount of free-text data is not used fully because normal computer systems have trouble understanding it.
Taking out social information by hand from these notes takes a lot of time, work, and can lead to mistakes. For busy medical offices, spending staff time reading notes means they have less time to care for patients or do other important work.
Also, language in clinical notes can be tricky. For example, phrases like “denies smoking” or “no history of homelessness” need careful reading to not cause wrong information.
Natural language processing (NLP) uses computers and artificial intelligence to understand human language in text form. In healthcare, NLP can read unstructured notes in EHRs and find, code, and organize social factors that affect patients.
Recent studies show that advanced NLP models can correctly identify many social determinants from clinical notes. For example, the Medical Information Mart for Intensive Care (MIMIC-III) database, which has thousands of notes, is used to train models to find categories like job status, housing issues, substance use, and social support.
One model, called BERT (Bidirectional Encoder Representations from Transformers), works better than older systems. In tests, BERT had high accuracy scores and did better than rule-based tools like cTAKES or simple statistical methods. It could tell social-related sentences from other sentences well, helping avoid false results when reading clinical records.
This accuracy helps find patients at risk by combining social data with medical information. One study showed that NLP-based tools helped healthcare workers find 56% more patients at risk by getting social health information accurately. This helps social workers and care coordinators give the right help, improving patient care.
In the U.S., healthcare rules require accurate SDOH data to meet quality standards from groups like CMS and NCQA. These rules are part of care programs that pay for good patient results rather than services provided.
NLP tools help by turning free-text notes into organized data that clinical systems and reporting tools can use. This helps medical practices record social risk factors fully, which affects care quality scores and risk adjustments.
Also, data from NLP helps manage the health of groups of patients by showing patterns of social risks. This information helps healthcare organizations use resources better, plan outreach programs for groups in need, and watch for health differences among populations.
Artificial intelligence (AI) and workflow automation do more than data extraction. They make everyday work in clinics easier. AI tools like phone automation and answering services improve patient access and office work.
For example, some companies create AI phone systems that handle common patient questions, make appointments, and refill medications without needing a person. This lets front-desk staff focus on harder tasks.
Also, AI with NLP helps summarize long clinical notes into short, clear reports. This reduces paperwork and helps doctors avoid tiredness from working with electronic records.
NLP systems learn the specific language and terms used by different medical groups. This makes the results more useful and reliable for clinic work, management, and reports.
Using these automated systems, clinics in the U.S. can work more efficiently, spend less on administration, and improve patient satisfaction while getting better data for health outcomes.
The healthcare company IQVIA shows how NLP can help manage social determinants of health. Their mixed method uses both rules and machine learning. The AI they use is “open box,” meaning doctors can understand how decisions are made. This helps build trust.
IQVIA’s NLP system found more patients at social risk than older methods, by over half. This fast detection helps social workers provide care better.
Another example is ForeSee Medical, which uses NLP to find uncoded patient conditions. This helps improve coding accuracy, which affects Medicare payments. Their method helps doctors document all needed diagnoses safely and correctly, supporting good patient care and finances.
These examples show how NLP tools can help clinics work better, follow rules, and improve patient care quality.
Healthcare leaders should think about these points when choosing and using NLP solutions:
By keeping these points in mind, U.S. healthcare providers can use natural language processing to improve patient care and workflows while staying compliant and competitive.
As AI grows, NLP will get better at reading clinical text. New uses will include real-time support that gives doctors social risk data during visits. Looking at patient records over time that combine social and medical information will help create better risk models and personalized care plans.
Also, new deep learning models based on tools like BERT will classify more complex social factors and notice detailed social interactions that influence health.
More use of AI-driven front-office automation and note summarization will improve clinic workflows, reduce doctor burnout, and help patient involvement.
Medical practices that add NLP and AI carefully will improve patient care coordination, make better use of resources, and reach quality goals in changing healthcare settings.
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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