Data Unveiled: The Impact of VBC and AI on Preventive Care and Chronic Disease Management
Date Posted: Tuesday,
June 03, 2025
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The conversation around chronic disease prevention remains critically important, both for ensuring patient and member health and achieving value-based care (VBC) goals of reducing healthcare costs and improving care quality. The CDC reports in “Chronic Disease Prevalence in the U.S.” that more than 4 in 10 Americans (42%) have two or more chronic conditions, and 12% have at least five, so healthcare stakeholders—from payors and providers to self-funded employers and benefits administrators—are looking for effective ways to address the gaps in preventive care.
Recent analyses of commercially insured patients highlight these challenges, revealing stark deficiencies in preventive care for three of the most common chronic diseases. As reported by Business Wire, d ata reveals that more than half (53.5%) of women forgo their recommended screening mammograms despite breast cancer being the most common form of cancer. For heart disease, which remains the leading cause of death in the U.S., 31% of working Americans or family members have a cardiac disorder. Meanwhile, only 35% of COPD and asthma patients receive flu vaccines despite their heightened vulnerability to complications.
These findings highlight an urgent need for a more systematic preventive care approach that integrates advanced analytics and AI-driven predictive capabilities to close these gaps and improve patient outcomes.
The Case for Preventive Care
Traditional fee-for-service (FFS) models reward volume and reactive care, which is counterintuitive to preventing disease onset and progression, and does not align incentives between patients, providers, and payors. Providers could potentially get paid more for late-stage disease detection (which requires more intensive and costly care) or unmanaged chronic disease (which requires more frequent encounters with a healthcare system), leading to a perverse incentive system. While many providers do encourage patients to get preventive screenings and vaccinations, and proactively manage chronic diseases, the FFS pay structure doesn’t reward or support these activities. The end result of fee-for-service is an overburdened healthcare system with more sick patients, and increased financial strain on patients and payors.
By contrast, VBC aligns incentives across constituents throughout the healthcare ecosystem, and helps establish reimbursements aligned with patient outcomes. This benefits patients by focusing on actions that can improve their health over the long term, such as preventive screenings and vaccines. It benefits providers by aligning reimbursement to activities that improve patient health. And preventive care benefits payors as well as patients by minimizing unnecessary spending on emergency department visits, hospitalizations, and late-stage disease interventions.
For example, increasing the number of people who get their recommended mammogram screenings can improve outcomes for breast cancer patients by detecting tumors early when there are more options for effective treatment. Similarly, implementing targeted interventions for cardiac patients—identified early through AI-driven risk assessments—could help manage risk factors before they lead to serious events like heart attacks or strokes. By embedding these strategies into everyday healthcare workflows, providers can improve early detection and help patients manage chronic diseases before they escalate.
Patients, providers, and payors benefit from this shift, as improved health outcomes translate to lower long-term costs and better quality of life for individuals managing chronic conditions.
The Role of AI-Driven Analytics in Strengthening Preventive Care
AI is changing the way healthcare stakeholders approach preventive care by uncovering deeper insights into patient risk factors and improving care coordination.
Advanced predictive models powered by AI facilitate targeted interventions for some of the costliest chronic diseases, enabling large-scale risk reduction by delaying or avoiding disease onset, and slowing disease progression. However, to be effective, AI predictive models must have robust underlying analytics and a large pool of anonymized patient data from which to learn and improve predictive capabilities over time. As AI capabilities increase, these tools are critical to assist providers and payors in optimizing outcomes, reducing costs, and addressing care gaps.
Today’s most advanced machine learning algorithms are capable of identifying individuals at high risk for chronic disease diagnosis in the coming year. These AI tools expand analytics capabilities beyond traditionally used rules-based logic, enabling analysis of a much wider range of metrics—including claims history, biometric screenings, and other health indicators—to detect potential cases that would otherwise have fallen below conventional diagnostic thresholds. Continuous collaboration between data scientists and clinical teams ensures the validation and improvement of these models.
AI can also optimize resource allocation by targeting preventive care efforts where they are needed most. For instance, health systems can use AI to direct resources toward populations with low screening rates, improving outreach and follow-up while reducing costs.
As the use of AI tools grows in healthcare, it’s also critical that companies developing these tools maintain the highest standards for data security and patient privacy, ensuring the responsible use of technology in advancing care. The stakes are high in healthcare, where users are dealing with patient lives. AI development must include robust testing to eliminate bias and error, and should never be intended as a replacement for the skill and knowledge of care providers. Instead, it should be a tool to augment providers’ capabilities to offer the right care, at the right time, in the right place.
Breaking Barriers to Preventive Care Adoption
Despite the clear benefits of preventive care, several challenges remain to get every patient the care they need. One major barrier is patient non-engagement, as many people delay or skip recommended screenings due to a lack of awareness, logistical hurdles (such as limited access to convenient and ongoing care), or financial concerns.
Employers and insurers can help address these obstacles by offering incentives for preventive care adherence and expanding access to affordable and accessible care based on population needs. For example, if analytics reveals low flu vaccination rates in a specific geographic area, an organization could use that information to launch targeted mobile vaccine clinics in the area.
Health literacy is also an issue, as many patients remain unaware of the importance of early screenings or may not fully understand their own risk factors for chronic disease. Expanding outreach efforts and simplifying access to preventive care services—such as mobile-friendly scheduling, virtual consultations, and workplace health programs—can help bridge this gap.
Another significant challenge is the administrative burden on providers, who are stretched thin and may struggle to prioritize preventive care within their workflows. Fragmented patient data contributes to this burden, forcing providers and care teams to navigate multiple systems and formats to identify the patients with preventive care needs. Enterprise data management systems with advanced interoperability capabilities can help by unifying information from EHRs, claims data, and other sources, reducing silos and manual work. AI-driven insights further support providers by quickly sorting through all the available data to pinpoint the most pressing patient needs, streamlining decision-making, and improving care.
Aligning for Action: The Future of Preventive Care
A collaborative approach is essential to a value-based future where preventive care takes center stage. Employers, payors, and providers must work together to increase preventive measures, leveraging AI and predictive analytics to identify high-risk individuals. Health plans and employer wellness programs can encourage patient adherence to screenings and vaccinations with appropriate incentives. At the same time, providers can use AI-driven risk stratification to tailor interventions to individual patient needs.
Ultimately, the success of preventive care strategies depends on a shift in mindset across the healthcare industry. Instead of waiting for patients to present with advanced disease, we must proactively identify at-risk populations and engage them in their healthcare journeys early. The integration of AI and VBC technology makes this possible.
As the healthcare industry navigates evolving value-based care models, embracing a more proactive, technology-driven approach to preventive care will be critical. AI-driven insights and VBC incentives provide a clear pathway for closing the gaps in early detection and chronic disease management, leading to a healthier population, lower costs, and a more sustainable healthcare system.
David L. Morris is EVP and Chief Commercial Officer at Cedar Gate Technologies. He has over 30 years of operational and executive leadership experience at blue-chip companies throughout the healthcare ecosystem. David drives client success across the value-based healthcare landscape by addressing the technology and service needs of the payor, provider, employer, pharma, and retail markets. He serves as Chief Commercial Officer with oversight of all commercial efforts, including market strategy, sales, and client success management.
Cedar Gate Technologies | High-performance healthcare in a singular platform
Prior to joining Cedar Gate, David held key leadership roles as EVP and Chief Commercial Officer with Citra Health Solutions, Chief Revenue Officer with Eliza Corporation (acquired by HMS Holdings), Senior Vice President of Global Sales and Services with Bupa Health Dialog (acquired by Rite Aid), National Vice President of Healthcare for SAP America, IMS Health (acquired by IQVIA), Baxter and George Washington University (GWU) Medical Center.
David received his undergraduate degree from George Washington University’s School of Government and Business Administration and now resides in Boston, MA.
