Healthcare payment in the United States is changing. Many payers are moving from paying for each service to paying for the quality of care. The American Medical Association (AMA) Center for Health Equity says about 73% of payers are making this change. This means doctors and hospitals need to use data to improve care and lower costs.
Value-based care needs lots of patient information. This includes electronic health records (EHRs), insurance claims, and social factors like where people live or their income. Pulling all this data together gives a clearer picture of a patient’s health. Hospitals and clinics group patients by risk levels like low, rising, and high risk. New studies say these groups should be broken down more by things like language, transportation, and money problems. This helps tailor care better for each person.
Low-risk patients mostly need health education and use technology like patient portals and telehealth. Rising-risk patients may have early chronic diseases and need special messages that consider their other problems. High-risk patients require quick and personal follow-up to avoid hospital stays and complications.
A big problem in value-based care is finding and fixing care gaps. These are things like missed tests or follow-ups. Integrated data systems that mix claims, clinical info, lab results, and outside data help solve this problem.
Milliman MedInsight is one such data analytics platform. Many payers and Accountable Care Organizations (ACOs) use it. The platform pulls data from various sources like private insurers and Medicare claims. It makes all data uniform, so teams can spot care gaps, reduce hospital readmissions, and improve coding.
In 2024, MedInsight clients saved $658 million in Medicare Shared Savings Program. This shows the money and health benefits of combining data. Their tools help make coding more accurate and catch errors in documentation. Poor coding can lead to lost payments and missed chances to help patients.
Linking clinical notes with easy workflows helps doctors and staff document patient health clearly. This supports meeting quality checks like HEDIS (Healthcare Effectiveness Data and Information Set). In 2025, HEDIS will add new measures, such as controlling blood pressure and better breast imaging. This makes accurate and fast data capture even more important.
Risk adjustment helps payers and providers understand how sick or well their patient groups are. Accurate risk scores affect how much money providers get paid, especially in Medicare Advantage and other contracts. If doctors miss diagnoses or coding mistakes happen, they can lose money and harm care.
David Mirkin from Milliman MedInsight says bad documentation and coding errors cause wrong risk scores. This can lead to money problems and rule violations. Good risk adjustment tools help find risks before or after care by checking records carefully.
A good risk adjustment system also helps doctors learn what to document and how to code properly for value-based care. AI tools make reviewing charts faster and lessen manual work while improving accuracy.
Accurate risk coding also links to pay-for-performance scores used by Medicare and private insurers. Keeping good coding and notes can raise scores like HEDIS and star ratings. These scores are important for getting good contracts and bonuses.
Risk-sharing arrangements (RSAs) are deals where payment depends on reaching goals for cost and patient health. These deals are more common as pay-for-performance grows.
Using past data — clinical, claims, money, and patient info — helps make fair RSAs. It shows how sick patients are and what resources they need. This allows realistic financial goals and contract terms.
But data can have errors, be incomplete, or not work well together. To fix this, providers build unified data systems. They join many data sources and work with tech experts who know modeling and data rules.
M Shahzad from blueBriX says mixing past data with real-time analysis helps improve contracts over time. AI and machine learning find patterns to predict risks and spot patients needing special care.
Artificial intelligence (AI) is becoming key in handling lots of healthcare data. AI automates tasks, improves accuracy, and helps make fast decisions. It works in areas like prior authorization, checking insurance, coding, and claims.
Oracle Health has AI tools that simplify admin work to cut costs and speed up care. Their Prior Authorization Agent finds out what’s needed, fills forms, and submits them digitally. This stops delays and extra paperwork. The Eligibility Verification Agent gives up-to-date insurance info at the doctor’s office, cutting surprise bills and helping understanding.
The Coding Agent automatically creates diagnosis and procedure codes by following payer rules. This lowers errors and helps risk adjustment and payments. Claims Processing Agents check claims against payer rules early, reducing denials and extra steps. These tools cut admin load for providers and payers, speeding payments and saving resources.
Putting AI in clinical and admin workflows makes work faster and care better. It automates daily documentation and coding so staff can spend more time on patient care and tough decisions.
Milliman MedInsight also uses AI for risk adjustment and improving docs. It speeds up chart reviews and finds missed conditions. Their AI helps with patient risk grouping and meeting quality standards.
For practice leaders, AI automation cuts errors, lowers denials, and makes patients happier by easing access to authorizations and benefits info at the front desk or over the phone.
Invest in Integrated Data Platforms:
Use systems that combine claims, clinical info, and social factors. This helps assess patient risks better and find care gaps. It supports managing patient populations and targeting outreach by risk and social needs.
Enhance Risk Adjustment and Coding Accuracy:
Since payments and scores rely on correct risk data, practices should adopt tools that improve documentation and coding with AI. This reduces errors and meets payer rules.
Leverage AI for Administrative Automation:
Use AI to automate prior authorization, insurance checks, and claims. This cuts delays, denials, and admin costs so staff can focus on patient care and complex tasks.
Focus on Social Determinants of Health (SDoH):
Address issues like language, transportation, and money through patient registries and outreach. This helps engage rising and high-risk patients, lowering readmissions and improving results.
Use Performance Benchmarking and Analytics:
Track results with analytics to find improvement areas, watch pay-for-performance, and prepare for rules like HEDIS 2025.
Healthcare providers and payers see real benefits using integrated data and AI in value-based care. For example, Southeastern Health Partners improved patient attribution, which helped improve quality scores.
A Vice President of Enterprise Business Intelligence praised MedInsight for deep data analysis that meets clinical and operational needs. A Director of Claims said MedInsight’s fast data normalization is much better than past vendors who took many years.
Executives said AI and automation reduce the need for big teams by handling data collection, cleaning, and analysis efficiently. This frees resources to improve clinical care.
Oracle Health says AI tools cut admin problems between payers and providers, making transactions more accurate and lowering costs for everyone.
Integrated data intelligence, better risk coding, and pay-for-performance measurements with AI automation are now key tools for medical practices and healthcare groups in the U.S. These tools help close care gaps, improve payments, meet quality rules, and support better patient care under value-based models.
Oracle Health’s AI-powered applications aim to accelerate payer-provider collaboration, reduce claims denials, lower administrative costs, and enhance care coordination to improve value-based care and optimize resource allocation.
Administrative costs related to healthcare billing and insurance are estimated to be approximately $200 billion annually, driven by complex processing rules and inefficient manual workflows.
AI agents embed payer-specific business rules in provider workflows, enabling accurate prior authorizations, eligibility verification, medical coding, and claims submissions, resulting in higher clean claim rates and fewer denials.
The processes include prior authorization, eligibility verification, coverage determination, medical coding, claims processing, and denial management.
It discovers prior authorization needs, retrieves documentation requirements, auto-fills information for review, and digitally submits requests, eliminating faxes and follow-ups to streamline approvals.
The Eligibility Verification Agent provides accurate eligibility and coverage details at the point of care, helping avoid surprise billing and allowing providers to recommend covered treatments and programs.
It autonomously generates medical, diagnosis, and DRG codes and applies payer-specific coding guidelines to reduce errors and facilitate accurate billing.
The Charge, Contract, and Claims Agents collaborate to ensure accurate charge capture and compliant claims submission, embedding payer rules to generate clean claims and reduce processing time.
Oracle Health Data Intelligence integrates payer insights on risk coding and care gaps directly into provider workflows, helping close care gaps and improve pay-for-performance metrics like HEDIS.
It replaces manual medical record transmission with a centralized, secure network, allowing real-time access to encounter data and eligibility validation, improving administrative efficiency and data security.
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