Care gaps are missed chances to provide important health services that prevent illness or better manage chronic conditions. These can happen when patients miss recommended screenings, delay routine check-ups, or do not follow management plans for diseases like diabetes or hypertension. Closing care gaps is important in value-based care because payment models often rely on quality measures and patient health outcomes.
Preventive care includes services like screenings (for example, colorectal cancer screenings), immunizations, routine lab tests (such as HbA1c for diabetic patients), and monitoring chronic conditions like blood pressure control. When provided on time, these services can lower complications, hospital stays, and overall health expenses.
However, finding patients with care gaps has typically been a slow and inefficient process. Reviewing charts manually requires many hours from clinical and administrative staff, which can cause delays in contacting patients and missed follow-ups.
AI technology, especially platforms that analyze electronic health record (EHR) data, can greatly reduce the manual effort needed to identify care gaps. By scanning both unstructured and structured patient data, AI algorithms can quickly spot missing preventive care or gaps in chronic condition management that need attention.
An example is Navina AI, whose clinical AI platform enables primary care teams to review patient records faster, decreasing chart review time by up to 30%. Navina’s AI applies more than 600 algorithms tailored for primary care, helping clinicians understand a patient’s health status in under two minutes—much faster than manual review.
Navina provides clear, clinically supported summaries within EHR workflows, reducing the overload of fragmented data. This allows clinicians to spend more time with patients and less time on administrative work.
More than just gathering data, Navina’s algorithms offer actionable recommendations like Hierarchical Condition Category (HCC) codes to improve patient risk adjustment accuracy. Accurate coding helps providers get proper value-based payments and identify high-risk patients who need targeted care.
Preventive care measures are key components of value-based contracts. AI platforms can help improve Healthcare Effectiveness Data and Information Set (HEDIS®) measures, which insurers use to assess and reimburse the quality of care. These include control of HbA1c, diabetic eye exams, colorectal cancer screenings, and blood pressure management.
For example, Navina’s quality management platform has increased performance on important HEDIS measures by up to 24% in various practices. By automating care gap detection and delivering clinical alerts during visits, it supports timely care that improves outcomes.
At Jefferson City Medical Group, integrating Navina into their EHR cut the time to identify patients overdue for colorectal cancer screening from 40-50 hours manually to just one hour using AI. This efficiency helped raise their Medicare Star Rating from 4.25 to a perfect 5 Stars, showing AI’s impact on pay-for-performance results.
Additionally, AI-driven tools for risk stratification let providers focus on high-risk groups like those with diabetes or chronic heart failure. With AI guidance, targeted care led to reductions of 20% and 15% in hospital readmissions for these groups within the same system.
Automating routine administrative tasks is important for expanding clinical capacity and reducing staff burnout, especially given healthcare workforce shortages. AI digital assistants and voice tools help clinicians by simplifying documentation, order entry, appointment reminders, and care coordination.
The Austin Regional Clinic used an AI voice-powered app integrated into their EHR with notable results. Providers saved one to two hours daily on documentation, cutting EHR time by more than 45%. According to Dr. Manish Naik, the Chief Medical Information Officer, this automation reduced physician workload and improved patient care by allowing more direct interaction.
AI assistants also help with outreach by contacting patients on behalf of practices to confirm care gaps like missed wellness visits or HbA1c tests and assist with scheduling. This process closes about 13% of outstanding care gaps, resulting in over 120 new visits yearly and boosting preventive care and practice revenue.
Furthermore, AI-driven order population tools automatically create clinical orders to address care gaps. Providers save an average of 40 clicks daily, and adherence to national quality metrics improves by around 15%. These workflow improvements reduce admin tasks and promote patient-centered care.
These benefits make AI integration a strong option for U.S. medical practices, especially those handling large primary care groups or participating in accountable care organizations (ACOs) and similar risk-sharing arrangements.
Understanding the details of value-based care contracts can be complicated. Jonathan Meyers, CEO of Seldon Health Advisors, stressed the need to understand aspects like risk adjustment methods, quality measures, and shared savings to avoid unexpected financial results.
AI tools support accurate data capture and provide real-time tracking of quality, helping organizations meet contract requirements efficiently. This reduces the need for time-consuming manual audits and offers transparency to care teams and leadership.
As payers and CMS place more emphasis on outcome-based reimbursement, AI tools become more important in addressing care gaps and preventive care. They also help practices comply with programs like CMS’s Bundled Payments for Care Improvement (BPCI) and TEAM initiatives by offering modular analytics and decision support needed for these payment changes.
Value-based healthcare depends on cooperation among clinicians, administrators, health plans, and payers. AI platforms help connect these groups by combining various data sources into useful clinical information.
For example, Cedar Gate Technologies offers AI-driven platforms that help providers and payers streamline workflows, reduce manual tasks, and improve coordination. AllianceChicago has eliminated more than 18 manual processes using such platforms and achieved cervical cancer screening rates over 93%, surpassing Healthy People goals.
By including social determinants of health data alongside clinical information, AI tools support addressing both medical and non-medical barriers to care. This approach improves outcomes in underserved patient groups.
While AI and automation bring benefits, smooth adoption needs careful handling to reduce disruptions for clinicians and administrative staff. The AI system should work seamlessly within current EHR workflows.
Navina, for example, quickly gained widespread use with over 86% of providers active weekly and more than 90% engaged within the first week. Clinicians value AI insights backed by clinical evidence, which builds trust during patient visits.
Training and ongoing support are important so healthcare teams learn how to use AI for preventive care and closing care gaps effectively. Medical administrators and IT managers in the U.S. should choose vendors that offer strong integration, clear processes, and ease of use.
Closing care gaps and improving preventive care remain ongoing challenges for value-based practices in the U.S. AI enhancements have shown they can make these tasks easier, reduce clinician workload, and improve performance on key quality measures.
Medical practice leaders and IT managers should consider adding clinical AI platforms and workflow automation to their operations to stay competitive and meet changing regulatory and payment requirements.
By reducing manual work in finding care gaps, supporting risk stratification, automating outreach and documentation, and providing real-time clinical decision support, AI can help deliver better health results at sustainable costs in value-based care settings.
Navina’s AI platform serves as a clinician-first AI copilot that turns complex and fragmented data into actionable insights, facilitating streamlined patient care and workflows in value-based healthcare.
Navina allows physicians to review patient records in less than 2 minutes by presenting the most relevant patient data in a concise clinical summary, significantly reducing the time spent on documentation.
Navina’s AI-powered HCC (Hierarchical Condition Category) recommendations help capture a complete picture of patients’ health, improving the accuracy of risk adjustment factors and chronic condition documentation.
The platform automatically identifies care gaps based on clinical evidence and patient exclusions, which helps reduce the time spent on data mining and improves quality measure satisfaction rates.
Navina offers robust analytics to track risk adjustment and quality performance over time, giving care teams full visibility into usage metrics aligned with clinical and value-based care objectives.
The AI platform is natively integrated into clinical workflows, providing an unparalleled user experience that prioritizes clinicians’ needs and allows for easier adoption by physicians.
An independent study reported that Navina’s AI reduces chart review burden by 30%, helping physicians save time and reduce burnout.
Navina enhances clinical collaboration and preventive care by closing critical care gaps, which leads to improved patient outcomes in value-based care environments.
After implementing Navina, practices reported a complete transformation in workflow due to centralized information presentation, enabling providers to focus more on patient interaction.
Clinicians appreciate that Navina provides clinical evidence to support every insight surfaced by the AI engine, which builds trust in the software’s recommendations during patient visits.
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