Social determinants of health mean the conditions where people are born, live, work, and grow old. These include things like economic stability, education quality, social life, access to healthcare, and the neighborhood around them. Many patients face problems like not having enough food, no good transportation, unstable housing, and trouble paying for medicine. These problems make it hard for them to get steady and timely healthcare.
Doctors and healthcare leaders know that working on these social issues is important. It helps improve health results and cuts down unnecessary hospital and emergency room visits. The Centers for Medicare and Medicaid Services (CMS) now include social risks as part of value-based care. For example, the 2025 Medicare Advantage Value-Based Insurance Design (VBID) model offers extra benefits that focus on health-related social needs to reduce differences in care.
Trying to fix these problems is not easy. Some hospitals and healthcare groups resist change. Different sectors do not always work well together. Money is limited, and data on social risks can be hard to collect and use correctly. But new health technology and AI tools are helping to face these challenges.
Biomedical informatics mixes healthcare, data, and technology to help manage social determinants of health. AI tools look at many kinds of data like electronic health records, insurance claims, patient backgrounds, social risk information, and even data from wearable devices. This helps find patients who are at risk because of social problems.
Mountasser Kadrie, PhD, MHA from George Washington University, says AI does more than just manage data. It turns raw data into useful information that helps doctors make decisions. AI can spot groups with higher chances of chronic diseases linked to social issues and suggest prevention plans. These programs help doctors create care plans that fit each patient’s social situation, which improves patient participation.
AI can also make care plans personal by mixing medical rules with social risk signs. This makes healthcare advice easier for patients to follow. Remote monitoring and telehealth, powered by AI, help reach people in rural or poor areas. These tools help overcome problems like distance and lack of resources.
AI-based predictive analytics helps healthcare move from reacting to problems toward preventing them. By combining health data with social factors like poor neighborhoods, housing, and food access, doctors can better find who needs extra help.
Studies show that AI models predicting things like death, hospital readmission, and time spent in the hospital do better than traditional methods. For Medicaid patients, AI models using social data predict hospital use and costs more accurately. Adding medication adherence data improved heart disease risk predictions by 18% in diabetic patients.
For healthcare managers, predictive tools can flag patients at higher risk because of social and health reasons. This supports quick care coordination, screening, and personalized contact. It also helps reduce hospital readmissions within 30 days by about 12%, which is important for quality and cost control in value-based care.
Health equity means giving every patient the resources they need for good health, not just the same resources to all. CMS rules now include this idea in Medicare Advantage ratings, risk adjustment, and extra benefit plans. The Health Equity Index helps rate providers and payers on reducing care differences.
In value-based care, AI uses social and demographic data to find underserved and at-risk patients. Linking patients to services like transport help, food aid, and medicine discounts helps stop social problems from turning into medical issues.
Still, running these health equity programs is hard because sharing data can be tricky, money is limited, and not all clinical teams accept the changes. Tools like health data exchanges, telehealth, and AI platforms for social needs are crucial to handle these challenges.
Researcher Munawar Peringadi Vayalil says AI programs that connect patients to community resources for social problems show how technology can extend care beyond the clinic. However, digital knowledge and access to devices are important to avoid new problems for vulnerable groups.
Managing social determinants along with medical care adds more work for busy healthcare teams. AI and automation can cut down admin tasks, letting doctors and staff focus on patients.
For example, AI-powered front-office automation like tools from Simbo AI help with communication and patient contact. AI-handled phone services manage appointments, reminders, medicine questions, and screening invites. These systems work all day and night, reducing the load on front desk staff and helping providers serve more patients well.
Also, Lumeris has an AI called “Tom” in clinical workflows. It schedules follow-ups, checks medicine use, and contacts patients after discharge automatically. This frees healthcare workers from routine jobs while helping care coordination and patient teaching.
By automating data collection, risk checks, and tasks, AI tools improve doctor schedules and patient access. Mike Long, CEO of Lumeris, says these tools help with the big shortage of primary care in the U.S., which now is over two billion hours annually.
For administrators and IT managers, using AI means joining patient engagement and social care smoothly into daily work, improving both efficiency and patient experience.
Using AI-driven social determinant programs needs good data systems, teamwork, and ongoing review. Medical leaders should focus on:
Groups like BJC Health System’s Center for Health AI and MIT’s Manolis Kellis Lab lead in creating algorithms that improve health decisions by including social data. Their work shows how partnerships among providers, schools, and tech companies can improve care and reduce costs.
Looking forward, AI’s role in social determinants of health will grow. More devices like wearables, as well as genetics and environment data, will join risk models. This gives a fuller view of health and allows earlier help.
Telehealth and mobile health apps will grow too. They make it easier for people in underserved areas to stay in touch with doctors and get care. Better clinical trials and workplace programs for equity will also improve evidence and support changes in the health system.
Healthcare payment methods will reward reducing care differences and helping patients with complex social needs. Medical practices that use AI tools within workflows will perform better as these models develop. They will meet the needs of many different patients.
In summary, AI and data analytics offer useful ways for medical practices to improve patient involvement and health results by addressing social determinants of health. Using personalized AI tools in clinical work, automating routine jobs, and working with community partners can help lower barriers, improve care, and meet the goals of value-based care in the United States.
Tom is an AI-powered Primary Care as a Service™ solution developed by Lumeris to support overburdened physicians and expand primary care access by integrating into clinical workflows and executing autonomous patient management actions.
Tom tackles primary care provider shortages, administrative burdens, limited patient access, and the growing mismatch between demand and supply by expanding care capacity and proactively managing patient care tasks.
Unlike traditional systems that only provide information, Tom autonomously acts on data in real-time, scheduling appointments, monitoring medication, conducting outreach, and personalizing care within shared care plans.
Tom autonomously schedules screenings and appointments, manages care coordination, monitors ongoing patient needs, and escalates complex cases, effectively optimizing provider schedules and reducing administrative workload.
Tom incorporates social determinants of health data alongside clinical guidelines to personalize patient interventions, improving engagement and outcomes while addressing non-clinical factors impacting health.
Tom aims to bridge the 2-billion-hour annual shortage in primary care by expanding provider capacity and enabling access for an estimated 100 million Americans without primary care providers.
Collaborators include BJC Health System’s Center for Health AI, Endeavor Health, MIT Computer Science and AI Lab, Oliver Wyman, ŌURA, and Wolters Kluwer, bringing expertise in AI, healthcare delivery, decision support, and wearable tech.
Lumeris reports improved quality metrics, better patient experiences, enhanced physician satisfaction, and high CMS star ratings (4.5 to 5.0) across multiple Medicare populations using Tom.
By increasing primary care capacity, reducing administrative burdens, and enabling proactive patient management, Tom lowers care costs by over 50% through improved efficiency and prevention.
Being embedded allows Tom to operate 24/7 alongside care teams, providing real-time insights and taking immediate, appropriate actions without disrupting provider workflows, thus enhancing schedule management and care delivery.
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