Value-based care (VBC) is slowly taking over fee-for-service models in the U.S. healthcare system. This means the focus is on patient health results, not how many services are given. This change brings chances to improve care quality and control costs. But it also brings challenges that healthcare groups must handle. One big challenge is correctly finding and recording patient risks. This is called risk adjustment. It also involves accurate coding and taking care of the health of large groups of people.
Artificial Intelligence (AI) and workflow automation are now tools that help healthcare workers and managers meet these challenges. By automating simple tasks, combining different data types, and giving quick clinical and operational information, AI helps improve coding accuracy, risk adjustment, and population health management. All these are needed to succeed in value-based care systems.
Risk adjustment assigns a risk score to patients based on their health conditions and background. This score predicts future healthcare costs and helps set payment models under value-based contracts. In VBC, doctors who treat sicker people or those with complex conditions get paid more because their care is harder.
The Centers for Medicare and Medicaid Services (CMS) uses the Hierarchical Condition Category (HCC) model for risk adjustment. Correct coding of HCCs makes sure providers get paid fairly. For example, writing down “diabetes with chronic kidney disease stage 4” instead of just “diabetes” leads to a higher risk score and better payment.
However, doing coding by hand often takes a lot of time and can cause mistakes. Studies show that wrong or incomplete documentation leads to lost money and a higher chance of audits and fines. CMS said that from 2013 to 2016, Medicare overpaid about $40 billion due to wrong or unsupported diagnosis reports. This shows the need to improve coding and documentation.
AI-powered risk adjustment software automates many difficult coding tasks done by staff. These tools use Natural Language Processing (NLP) to study both organized EHR data and unorganized clinical notes. This helps spot clinical details that might be missed in traditional coding.
For example, Innovaccer’s AI platform showed up to 30% better coding accuracy and much less time spent on documentation. These platforms give clinicians real-time alerts and coding tips, helping fix gaps during patient visits instead of later.
When Edifecs bought Talix, it showed how advanced AI and NLP workflows are being added to healthcare. Talix has a custom set of over a million health ideas, helping it understand meaning better and speed up correct documentation. The combined tech reduces manual chart searching, lowers staffing costs, and improves revenue by making sure risk-adjusted coding is correct, especially for Medicare Advantage, Medicaid, and ACA patients under value-based contracts.
Population Health Management (PHM) software helps healthcare organizations improve health outcomes in whole communities while controlling costs. PHM systems combine clinical data, insurance claims, social factors affecting health, and behavior data to give a full view of patient health.
AI and machine learning are built deeply into PHM software to do risk sorting, predict future health issues, and find care gaps. For example, Innovaccer’s PHM solution, ranked #1 by the 2024 Black Book Research Survey, collects data from many EHRs and other sources. It uses AI to make workflows easier and automate documentation for correct risk adjustment coding.
Including social factors like economic status and living conditions helps providers handle bigger issues that affect health. This helps ensure fair care, which is important in modern value-based care.
PHM systems also help lower readmission rates and stop extra hospital visits by finding high-risk patients early and allowing quick care. Jefferson City Medical Group cut diabetes-related readmissions by 20% and hospital visits for chronic heart failure by 15% using AI for early risk sorting and personal care plans.
AI helps make many office and clinical tasks faster, saving staff time. For example, Skypoint AI workers run 24/7 to handle front-office phone calls and patient registrations. They check eligibility, Medicaid status, benefits, scheduling, and prior authorizations. These tasks affect patient access and finances.
This kind of automation cuts down errors and makes patient intake quicker. It lets office staff spend more time with patients personally.
Also, AI agents are built into Electronic Health Records (EHR) systems to automate clinical workflows. Skypoint’s AI agent “Lia” automates prior authorizations, care coordination, paperwork, and prep for visits right inside EHRs. This improves how efficiently providers work and lowers burnout.
The AI Command Center by Skypoint keeps track of over 350 key measures in clinical, operational, and financial areas. This AI system gives predictive warnings and automates workflows across departments. It stops errors and delays before they harm patient care or money matters.
Risk models that update often with live data help healthcare groups aim their efforts better. Instead of only looking at past costs, these models find patients who will likely need more care and let providers act early.
Healthcare groups in the US face staff shortages and heavy admin workloads. AI automation helps save up to 30% of staff time. This lets teams focus more on patient care.
By lowering repetitive tasks like coding, prior authorizations, scheduling, and paperwork, AI reduces the load on providers. Jefferson City Medical Group says that AI-driven workflow changes helped reduce burnout and boost staff mood. These changes include online patient check-ins, appointment reminders, and instant delay alerts.
Investing in better work conditions for staff is important for lasting success in value-based care. Happier clinicians often lead to more satisfied patients and better care quality.
Rules from CMS about coding and documentation are getting stricter. Healthcare groups must focus on risk adjustment programs that catch accurate data during patient visits. AI tools give real-time tips and coding help. This cuts reliance on checking charts after visits, which often causes audit failures.
Innovaccer’s AI platform provides coding gap analysis to help groups find and fix risk adjustment gaps earlier. Roxanna Cross from Innovaccer says involving providers is the most important part of improving risk score accuracy and patient experience. AI tools that connect data and support providers help with compliance and lower financial risks.
The US Department of Health and Human Services Office of Inspector General found that home visits and looking back at medical records are high-risk audit areas. This shows the need for accurate, real-time AI solutions.
Correct risk adjustment directly affects money earned under value-based contracts. Providers get paid based on how hard their patients’ care is, shown by coded conditions.
AI coding tools help providers record all needed diagnoses and conditions. This improves Risk Adjustment Factor (RAF) scores. Better scores mean more payment and less chance of being paid too little.
Also, AI-supported documentation makes clinical notes better. This helps with hierarchical condition category (HCC) coding used by CMS to assign risk scores. For example, Premier Inc.’s AI Clinical Decision Support system works inside EHRs to give evidence-based advice and coding alerts. This improves both clinical care and finances at the same time.
Healthcare groups have limited resources to run VBC programs. AI can help pick important areas to focus on. These might be cutting readmissions, raising preventive screenings, or managing chronic diseases.
Ron Rockwood from Jefferson City Medical Group says it is best to focus on two or three key projects tied to contract goals. AI risk sorting helps make sure resources go where they give the best results.
AI-powered dashboards create clear views of performance. This encourages sharing what is learned and steady improvement among staff. These things help keep progress going in value-based care.
Integration with EHRs Is Essential: Many US providers use EHR systems like Epic, Cerner, and Allscripts. AI tools that fit well with these systems reduce clinician workload and help real-time coding, documentation, and risk adjustment work better.
Regulatory Compliance: AI solutions must follow HIPAA, HITRUST, and CMS rules to keep patient data safe and meet audit needs.
Financial Incentives: Providers working with Medicare Advantage, Medicaid Managed Care, and ACA contracts focus strongly on accurate risk adjustment and coding. This helps them get paid right and avoid penalties.
Focus on Patient Experience: Automations that smooth pre-visit steps and cut wait times improve patient satisfaction scores. These scores then affect quality measures like HEDIS and Stars ratings.
AI workflow automation is becoming key to running healthcare operations well. By automating routine admin and clinical steps, such as prior authorizations, scheduling, benefits checks, and documentation, healthcare groups can work more efficiently.
For example, Skypoint AI agents work all day and night to speed up front-office tasks and cut manual work. This leads to quicker patient access, fewer mistakes, and better data accuracy. These are vital to keep compliant and get the best reimbursements in value-based contracts.
AI agents built into EHR workflows, like Lia, help providers by automating tasks that usually take time. These include care coordination and preparing for visits.
Automation is not just about scheduling and registration. AI also tracks hundreds of key measures through command centers. It gives predictive analytics and workflow advice. This lets health systems fix problems quickly and match efforts to goals.
Plus, AI risk models that update with real-time data help decide which patients need care first. This improves health results for whole populations and saves resources.
AI-powered workflows are helping solve important challenges in value-based care. They improve risk adjustment accuracy, make coding better, automate repeated tasks, and support population health management. These technologies help healthcare providers in the United States handle complex value-based contracts, reduce admin burdens, and focus on giving good care to patients.
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.
Simbo is using AI agents to automate all phone related workflows. Connect now to know more.
Schedule AI Agents Demo Now© Since 2019, Simbo AI.
