Poorly managed and uncontrolled chronic diseases are the leading cause of death and disability in the United States, and represent a growing national crisis. Five of the top 10 causes of death – heart disease, cancer, diabetes, obesity, and hypertension – are chronic conditions, or closely linked to preventable and treatable chronic conditions. The prevalence of these diseases has steadily increased over the past two decades. Today, 42% of Americans have two or more chronic conditions, and the need for preventive care to help patients manage these conditions has never been more important.
Cardiovascular disease alone accounts for nearly 20% of annual deaths in the United States, with cardiac disorders and hypertension affecting 31% of the US workforce. At the same time, many Americans continue to forgo essential preventive care services – such as mammograms and flu shots – which can lead to late-stage diagnoses, avoidable hospitalizations, and escalating healthcare costs. A recent analysis of the commercially insured population revealed that over half (53.5%) of eligible women skip recommended mammograms, and only 35% of COPD and asthma patients receive flu vaccines, despite their increased vulnerability to complications.
A value-based approach to care can address these deficits and improve long-term outcomes, particularly when paired with AI-driven analytics designed to proactively manage chronic disease risk and reduce cost burdens.
Preventive Care is at the Center of Successful Value-Based Models
The traditional fee-for-service (FFS) model prioritizes volume over outcomes, rewarding expensive interventions after someone is already sick, rather than efforts to keep patients healthy. Alternatively, VBC prioritizes preventive care, benefiting patients by focusing on long-term health through actions like vaccinations and preventive screenings, and prioritizing care quality over care volume.
Providers benefit from reimbursements aligned to activities that improve patient health. Patients benefit from having a care team focused on their overall health and optimal outcomes. Payers and self-funded employers benefit by reducing spending on things like avoidable emergency room visits, preventable hospitalizations, and costly treatment for diseases caught in later stages. It’s a win for everyone.
Leveraging AI-Driven Insights to Improve Preventive Health Strategies
Professional organizations, such as the U.S. Preventive Services Task Force, American Diabetes Association, and American Heart Association, continually curate clinical guidelines from large collections of peer-reviewed publications. These practice guidelines represent up-to-date recommendations for screening and other preventive interventions targeting chronic disease.
Additionally, AI-powered analytics can refine and prioritize insights in order to target the highest risk members of a population. For example, machine learning algorithms trained on a specialized risk-scoring process can identify a person’s risk of being diagnosed with chronic disease in the next 12 months. That information can improve healthcare stakeholders’ ability to engage patients in recommended preventive care with advanced analyses of patient risk factors.
To ensure accuracy, these models must go beyond traditional rules-based logic to examine claims history, biometric data, family and medical history, EHR data, and more. Properly designed AI algorithms – trained extensively on diverse and anonymized healthcare data – can identify and flag patients at higher-than-average risk. Then providers, payers, and employers can use AI tools to intervene earlier and optimize resource allocation to direct care efforts to areas with the greatest need. When AI risk prediction models have proper training, validation, and optimization – and are paired with the expertise and experience of care team members – healthcare organizations can work with patients to slow chronic disease progression.
Navigating the Hurdles to Widespread Preventive Care Adoption
Despite the advantages, there are still challenges for healthcare organizations and patients on the path to widespread, effective preventive care. Patient non-engagement remains a top concern, driven by factors that can include lack of awareness, logistical hurdles (such as issues accessing care), cost concerns, and low health literacy.
At the same time, providers are stretched thin and struggle to shift to more proactive care. This is especially true when they lack comprehensive data and simple tools to translate analytics insights to actionable care steps. Disconnected systems and fragmented data make it difficult to identify which patients need preventive care and when. AI-powered solutions and enterprise data management platforms can help by unifying information from EHRs, claims, and other sources, ultimately reducing manual work and surfacing worthwhile insights.
Employers and payers also have a role to play, offering incentives for preventive care adherence and supporting programs that make care easier to access, such as mobile clinics, workplace screenings, or digital health and wellness tools.
Aligning to Improve Preventive Care
Building a more proactive healthcare system requires collaboration across the industry. Employers, payers, and providers must align on strategies that prioritize early engagement and more personalized interventions. With AI-enabled insights and VBC incentives, organizations can overcome longstanding barriers for a scalable and sustainable primary and preventive care system.
VBC success requires a shift in healthcare from reactive to proactive. When organizations are supported by the right technology and incentives, stakeholders can help engage at-risk populations earlier, improve outcomes, and reduce the financial burden of chronic disease for their patients. As the industry continues to navigate this transition, organizations that invest in data-driven technology today will be best positioned to drive measurable clinical, operational, and financial results.
About Joseph Siemienczuk, MD
Joseph Siemienczuk, MD, is the Medical Advisor for Clinical Solutions at Cedar Gate Technologies. Prior to Cedar Gate, Dr. Siemienczuk served as the Chief Medical Officer for Enli Health Intelligence. He led Enli’s efforts to translate the latest evidence-based guidelines into codified clinical algorithms used by the Enli platform to enable providers to practice at the height of their licensure and reduce unwarranted variation in care delivery. He also held the position of CEO of Providence Medical Group where he oversaw the operational and financial accountability of the integrated healthcare delivery system, which spans over 700 physicians, 80 clinics, and eight hospitals.
