Value-based care focuses on patients and prevention. It tries to give better health results while spending less money. This is unlike older systems where doctors are paid for each service they do. Some examples of value-based care models are Accountable Care Organizations (ACOs), Bundled Payments, and Patient-Centered Medical Homes (PCMHs). According to Humana’s yearly report, Medicare Advantage patients in value-based care had 32.1% fewer hospital stays and 11.6% fewer emergency room visits. This led to $11 billion in savings in 2023. This shows value-based care can help save money and improve health.
For those who run medical practices and manage IT in the U.S., value-based care means focusing on good clinical records, combining data well, and smooth patient care across different providers. If these areas are not managed well, it can lead to fines, less money from insurers, and worse care for patients. So, it is very important to make coding accurate and properly adjust risk to match services with payment models in value-based care.
Having correct clinical coding is very important in value-based care. It affects how risk is measured and how providers get paid. Risk adjustment looks at how sick patients are to make sure providers get paid fairly for treating serious illnesses. Manual coding can often have mistakes or miss important details. This hurts care quality scores and money earned.
Artificial intelligence (AI) tools, like natural language processing (NLP) and machine learning, help by finding important clinical information from electronic health records (EHRs), doctor notes, and other data. AI can also find cases that need more checking and lower the number of unnecessary reviews. One big health system used AI and cut their case review rate by 20%, while also increasing payments and quality scores.
Putting money into clinical documentation teams that use AI can make risk documentation more accurate. For example, a Northeastern health system improved its expected death rate accuracy from 2.1% to 6.1%. This helped improve their rankings on lists like Vizient and US News & World Report. Making sure coding matches real patient conditions leads to better quality scores, correct payments, and safer care for patients.
Care coordination means making sure everyone involved in a patient’s care works together well. This includes primary doctors, specialists, insurance companies, and community services. Good communication lowers repeated tests, stops unnecessary hospital visits, and makes patients happier. Many providers have trouble because their data systems do not talk well to each other and their workflows are not efficient.
The U.S. Department of Health & Human Services supports standards like FHIR and QHIN that help different systems share data easily. AI tools that connect to various EHRs can share information in real time, manage referrals automatically, and speed up needed approvals.
For example, Skypoint’s AI agents work nonstop to take over routine jobs like prior authorization, referral handling, checking Medicaid eligibility, and scheduling appointments. They also help with paperwork and prepare visits before patients come. This lets clinicians spend more time with patients and less on forms. Some regional health groups using these AI tools have freed up 30% of their staff capacity, helping with staff shortages and workload.
Using AI for care coordination also helps when reporting quality to regulators. AI makes documentation more complete and accurate, which is needed for reports like HEDIS and CMS Stars. Meeting these standards helps organizations earn more money under value-based pay models.
AI is not just for coding and data. It can also handle repetitive office tasks that take up a lot of staff time. AI Command Centers watch hundreds of key measures all day. They send alerts that warn staff about problems before they happen. This keeps workflows running smoothly and supports clinical and financial goals.
Some AI platforms use a Unified Data Platform to gather information from claims, EHRs, social factors, and clinical notes. This data is kept in one place called a “lakehouse,” letting decision-makers quickly find useful information to improve care and meet rules.
Examples of AI automation include:
These automations help patients get care faster and staff spend more time on personal contact instead of paperwork. Fewer manual errors and delays improve patient satisfaction and help meet value-based care standards consistently.
Value-based care depends on clear data that shows quality. Measures like death rates, readmissions, safety issues, and length of stay help check care quality. AI helps clinical documentation programs create accurate and trustworthy records that support coding and risk adjustment data.
Leaders in finance and clinical roles, such as CFOs and CMOs or Chief Quality Officers, need to work together to guide these documentation efforts. Organizations that combine leadership, provide training on risk adjustment and pay-for-performance, and have specialists for quality checking tend to get better payments and perform well on rating lists like Leapfrog and US News & World Report.
AI tools can also track important ratios and metrics constantly. This helps healthcare groups find where they need to do better and change workflows. This active measuring aligns daily work with financial and regulatory goals in value-based contracts.
Value-based care is also starting to look at social factors that affect health, like where people live, if they have enough food, jobs, and legal help. These things often are not in regular clinical data.
AI platforms can bring social risk screening into clinical workflows and connect patients with community support automatically. This helps reduce health gaps and improves managing the health of the whole population, which supports value-based payments.
Federal programs like the FY25 Health Center Controlled Network encourage better data sharing and training workers to use AI. AI referral tools and risk models try to fix care gaps caused by social risks.
Using AI and data systems means patient privacy and following healthcare laws are very important. AI platforms follow strong security rules like HITRUST r2, which includes HIPAA, NIST, and ISO standards. These keep electronic health information safe from hacking and unauthorized access.
Healthcare groups must also use multi-factor authentication, encryption, intrusion detection, and regular security checks in their AI plans. Strong cybersecurity keeps patient trust and makes sure regulations are met, which is needed for long-lasting value-based care success.
For administrators, owners, and IT staff in U.S. medical practices, using AI tools can help make value-based care work better. AI improves coding and making sure risk is noted correctly, which helps keep payments steady under value-based contracts. It also improves care coordination by automating tasks and increasing front-office efficiency. Leadership with clear plans and using AI help meet quality rules and public reporting needs. AI also helps manage social risk factors, reducing health disparities and matching national care goals.
Using technology along with training and strong leadership can help U.S. healthcare providers meet value-based care challenges now and in the future.
Skypoint’s AI agents serve as a 24/7 digital workforce that enhance productivity, lower administrative costs, improve patient outcomes, and reduce provider burnout by automating tasks such as prior authorizations, care coordination, documentation, and pre-visit preparation across healthcare settings.
AI agents automate pre-visit preparation by handling administrative tasks like eligibility checks, benefit verification, and patient intake processes, allowing providers to focus more on care delivery. This automation reduces manual workload and accelerates patient access for more efficient clinic operations.
Their AI agents operate on a Unified Data Platform and AI Engine that unifies data from EHRs, claims, social determinants of health (SDOH), and unstructured documents into a secure healthcare lakehouse and lakebase, enabling real-time insights, automation, and AI-driven decision-making workflows.
Skypoint’s platform is HITRUST r2-certified, integrating frameworks like HIPAA, NIST, and ISO to provide robust data safeguards, regulatory adherence, and efficient risk management, ensuring the sensitive data handled by AI agents remains secure and compliant.
They streamline and automate several front office functions including prior authorizations, referral management, admission assessment, scheduling, appeals, denial management, Medicaid eligibility checks and redetermination, and benefit verifications, reducing errors and improving patient access speed.
They reclaim up to 30% of staff capacity by automating routine administrative tasks, allowing healthcare teams to focus on higher-value patient care activities and thereby partially mitigating workforce constraints and reducing burnout.
Integration with EHRs enables seamless automation of workflows like care coordination, documentation, and prior authorizations directly within clinical systems, improving workflow efficiency, coding accuracy, and financial outcomes while supporting value-based care goals.
AI-driven workflows optimize risk adjustment factors, improve coding accuracy, automate care coordination and documentation, and align stakeholders with quality measures such as HEDIS and Stars, thereby enhancing population health management and maximizing value-based revenue.
The AI Command Center continuously tracks over 350 KPIs across clinical, operational, and financial domains, issuing predictive alerts, automating workflows, ensuring compliance, and improving ROI, thereby functioning as an AI-powered operating system to optimize organizational performance.
By automating eligibility verification, benefits checks, scheduling, and admission assessments, AI agents reduce manual errors and delays, enabling faster patient access, smoother registration processes, and allowing front office staff to focus on personalized patient interactions, thus enhancing overall experience.
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