One main challenge in value-based care contracts is checking patient health results and care quality on time. Traditional claims data helps with billing and payment but often arrives late—sometimes after three months. It mostly shows billable services and does not give the full clinical picture. This delay can make it hard to coordinate care and act fast enough to improve patient health and control costs under value-based agreements.
To fix these problems, some solutions like Milliman MedInsight’s updated Value-Based Care (VBC) Platform combine Electronic Health Record (EHR) data with claims data. EHRs record clinical details like lab tests, treatments, medicines, social factors, and risks almost right away when care is given. Combining this with claims data gives a fuller and quicker view of a patient’s health and care. This helps doctors and managers see if care meets contract rules, spot care gaps, and take action earlier to fix health problems.
Milliman MedInsight uses a Data Confidence Model to keep data quality high during integration. Accurate data helps target patient groups better, sort risks more clearly, and measure performance well under several value-based contracts. The platform’s data displays and clear reports help accountable care organizations (ACOs), health systems, and physician groups manage contracts more smoothly.
By putting clinical and claims data together, medical practices get a full understanding of patient groups. This lets them focus on needed care and improve treatment plans. Rich Moyer, Chief Product Officer at Milliman MedInsight, says their platform lets groups quickly adjust to economic changes and manage many contracts from one place, making handling value-based agreements easier and more organized.
Risk adjustment is very important in value-based care contracts because it considers the different health needs and complexity of patient groups. It helps providers get fair payment for the care they give. Good risk adjustment lets organizations predict healthcare costs better and set suitable budgets for patient care.
Milliman Advanced Risk Adjusters (MARA) is a tool made to create detailed, person-centered risk profiles. MARA includes sudden, long-term, complex health issues, and social factors in its assessments. Unlike older models, MARA uses social information from ICD-10-CM Z codes, mixing social and clinical data that affect patient health. This wider view helps care teams and payers understand patients’ medical and social needs, which is important for good value-based care.
MARA’s Rising Risk models group patients by how their healthcare costs might change—whether costs will stay the same, go up, or go down. This helps providers focus resources on patients whose risk is growing and who might benefit most from early care. For example, a Medicare study showed that patients with one Complexity Indicator (part of MARA) cost twice as much as those with one chronic condition and three times as much as those with one medical condition.
MARA also supports tracking patient risk over several years with its Rolling Risk feature. This helps health plans and providers watch risk changes over time, which is useful for value-based contracts lasting many performance periods. The tool works on cloud and local systems and connects well with popular analytics tools like Microsoft Power BI, making it easy for managers to use risk data.
Using risk adjustment tools like MARA helps healthcare organizations make smart financial and care decisions. They can find patients who cost more and need more care sooner, then change care plans to get better results and keep finances balanced under value-based models.
Artificial intelligence (AI) and workflow automation are becoming more important in managing value-based care contracts and risk adjustment. AI looks at large amounts of combined clinical and claims data to create useful insights about patient risk, care gaps, and resource use.
Oracle Health Data Intelligence shows how AI can rank patients who need outreach most and suggest next steps to lower emergency visits and hospital stays. Oracle’s AI tools also help care managers work five times faster by making patient summaries from recent visits, medicine changes, and upcoming appointments. This lets staff spend more time making decisions and talking with patients instead of gathering data.
Premier’s Stanson Health Clinical Decision Support system adds AI into EHR workflows. It helps providers by suggesting correct Hierarchical Condition Category (HCC) codes for risk adjustment right away. Better coding helps make sure providers get paid properly in value-based care. AI also finds care gaps early, warning providers about needed preventive care or chronic disease treatment to close care gaps faster.
In payers’ work, Veradigm uses AI-powered tools to watch quality scores and flag care gaps, sending alerts inside provider EHR systems. Tools like Veradigm RxTruePrice give patients medicine cost info at care time, helping fix issues when patients don’t take medicines because of money.
Automation in data handling cuts down paperwork. By fitting analytics into existing clinical routines (such as with Epic EHR), staff avoid breaking their workflow and spend less time searching for data or doing manual reports. This supports decisions with current info and helps keep contract rules and quality reporting without added admin work.
Medical practice managers and IT teams benefit by using AI and automation tools that fit their current systems. These techs improve data correctness, speed up reports, and boost work efficiency—all needed to run value-based care contracts well.
Value-based care contracts often require meeting quality goals like those set by the Healthcare Effectiveness Data and Information Set (HEDIS®). HEDIS is a common system made by the National Committee for Quality Assurance (NCQA). More than 90% of U.S. health plans use HEDIS measures to check care quality and progress in value-based goals.
To improve HEDIS scores, care gaps must be closed. These include making sure patients get vaccines, cancer screenings, diabetic care, medicine use, and blood pressure control. Veradigm’s Quality Analytics platform, approved for HEDIS years 2024 and 2025, gives tools for plans and providers to find care gaps and improve workflows for better quality results. It links real-time with payer data, giving immediate access to useful analytics at care points, which helps with preventive services and chronic care.
Also, cloud-based platforms help with government rules by including electronic clinical quality measures (eCQMs). This makes reporting easier for Centers for Medicare & Medicaid Services (CMS) programs and alternative payment plans. These systems cut down admin work by automating data collection and reporting, letting providers spend more time caring for patients and less on paperwork.
Medical practice managers and IT staff in the U.S. can use these tools to standardize quality checks, meet changing rules, and improve payments tied to value-based contracts.
Money management is a big concern for medical practices in value-based care models. Using combined analytics tools helps groups watch total care costs, find where spending is too high, and adjust plans to stay within budgets without hurting care quality.
Milliman MedInsight’s VBC Contracts app offers features like financial health views, risk checks by provider and ACO, and predicting multi-year results. These help organizations plan finances and handle risk better by guessing contract settlements in advance.
Oracle Health Data Intelligence helps financial teams cut costs by spotting problems early and improving payment accuracy using full patient data. Its antimicrobial stewardship analytics lower costs by using medicines better, reducing unnecessary prescriptions, and improving health outcomes.
By tracking key numbers and cost drivers closely, medical practices can link care and financial goals, helping them succeed in value-based care payment systems.
For medical practice managers, owners, and IT staff in the United States, using advanced analytics and full data integration is necessary in value-based care. Combining clinical and claims data, using risk models that include social factors, and applying AI-driven automation help track contract performance, improve patient care, and manage money well.
Good implementation needs careful planning, investing in flexible technology that works with different EHR systems, and teamwork among clinical, financial, and operations groups. Companies like Milliman, Oracle, Veradigm, and Premier offer scalable options to meet the needs of U.S. healthcare providers, from small clinics to big health systems and ACOs.
By focusing on up-to-date data accuracy, AI decision support, and full performance analytics, medical practices can meet the rising demands of value-based care contracts while improving patient care and work efficiency.
This clear method of managing value-based care contracts and risk adjustment helps healthcare groups in the United States handle complex payment models and keep making better patient and financial results over time.
Oracle Health Data Intelligence is a cloud-based suite that integrates patient data across clinical, claims, pharmacy, social determinants, and more. It improves healthcare outcomes by enabling predictive analytics, better decision-making, and proactive care management, leading to reduced costs, improved patient care quality, and enhanced operational efficiency.
The solution is EHR-agnostic, meaning it works with any electronic health record system to integrate disparate data sources. This eliminates blind spots caused by siloed information, allowing healthcare organizations to obtain a comprehensive view of patient health and close care gaps more effectively.
AI powers prioritization of patients who are most likely to benefit from outreach, suggests next best steps for care, and supports clinical, financial, and operational decisions, helping providers prevent costly emergency visits and hospitalizations while optimizing value-based care outcomes.
New capabilities include value-based care contract performance tracking, AI-powered patient prioritization, an Oracle Health companion app for closing care gaps, expanded content catalogs for MIPS and HEDIS, updated HCC classifications for risk adjustment, and cost/utilization analytics to better understand spend and demographics.
It cleanses, normalizes, and unifies data from multiple sources to create longitudinal patient records. Features like natural language query reporting, emergency medicine order analysis, social determinants screening, and antimicrobial stewardship analysis accelerate insights for clinical and business users.
It increases care team efficiency with AI-powered summaries that surface key patient information, including recent encounters and medication changes, and provides on-demand access to supplemental clinical records. This fosters better coordination, patient engagement, and improved care experiences.
By offering a cloud-based, cross-EHR solution for CMS’s Alternative Payment Model Performance Pathway (APP), it uses electronic clinical quality measures (eCQMs) to streamline reporting processes, enhance data accuracy, and ease compliance with Medicare and Medicaid regulations.
Organizations can expect optimized financial performance with better tracking of value-based care contracts, reduction in total cost of care, increased reimbursements, and improved understanding of costs and utilization among patients with chronic conditions through detailed analytics and reporting.
By integrating comprehensive patient data and utilizing AI-driven insights, the system highlights missing or needed care services, supports documentation such as Hierarchical Condition Categories (HCC), and guides clinicians and care managers toward proactive interventions to close those gaps.
The platform runs on Oracle Cloud Infrastructure (OCI) which provides military-grade security used by national defense agencies and large enterprises, ensuring sensitive health data is protected with top-tier security standards against breaches and unauthorized access.
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