- Shannon Smith, Assistant Vice President of Clinical Success
The recent resurgence of measles, a disease previously declared eradicated in the U.S., clearly signals that our healthcare systems need to evolve. While COVID-19 exposed critical vulnerabilities in reactive healthcare management, the current measles outbreak beginning in Texas and rapidly spreading to states like Kansas emphasizes the necessity of predictive health strategies. Health plans and risk-bearing organizations must move beyond outdated systems and leverage advanced predictive analytics and artificial intelligence (AI) to anticipate, manage, and mitigate public health crises before they escalate.
The Current Reality
According to the Centers for Disease Control and Prevention (CDC), as of March 27, 2025, there have been 483 confirmed measles cases reported across 20 jurisdictions: Alaska, California, Florida, Georgia, Kansas, Kentucky, Maryland, Michigan, Minnesota, New Jersey, New Mexico, New York City, New York State, Ohio, Pennsylvania, Rhode Island, Tennessee, Texas, Vermont, and Washington. Since early February, the outbreak has rapidly escalated, with Texas alone accounting for over 422 cases. Genetic sequencing indicates widespread transmission linking multiple states. Despite aggressive public health measures—including extensive vaccination campaigns, community education, and stringent quarantine protocols—the rapid spread underscores a critical flaw: traditional healthcare measures typically activate only after a crisis has begun, limiting their effectiveness and placing substantial strain on clinical resources. Predictive analytics, by contrast, could have significantly altered this trajectory. By continuously analyzing vaccination coverage rates, school immunization records, healthcare utilization patterns, and community mobility trends, AI tools could have detected early signals of vulnerability. For instance, a drop in vaccination rates in specific communities or increased healthcare visits for measles-related symptoms could trigger immediate alerts. These early warnings would have enabled targeted community outreach, and focused vaccination drives weeks or even months before widespread transmission occurred, significantly reducing the outbreak’s impact.
From Crisis Management to Crisis Prevention
Health plans today operate under immense financial and operational pressures, further strained by volatile reimbursement rates and rising healthcare costs projected to reach $6.2 trillion by 2028. These economic realities demand that health plans not only respond more rapidly but also anticipate healthcare needs proactively.
Traditional population health management relies heavily on retrospective claims data and manual processes, leaving significant blind spots and missing early opportunities to intervene. In contrast, predictive analytics powered by AI allows healthcare organizations to anticipate public health threats using real-time data such as vaccination rates, clinical records, community demographics, and mobility trends. This foresight enables proactive interventions and resource allocation before crises intensify.
Anticipating Risks, Delivering Results: How AI Drives Proactive Health Management
Effective population health management requires more than just identifying risks—it demands precise, early interventions to prevent health crises. AI-driven predictive analytics transforms traditional healthcare by delivering deeper insights into patient and community health trajectories. These technologies analyze vast amounts of data, including electronic health records (EHRs), claims history, social determinants of health (SDOH), and lifestyle factors, to predict and identify where risks are emerging at both individual and population levels.
By leveraging predictive models, health plans can take targeted actions to prevent worsening conditions. For instance, AI analytics can detect early signs of low vaccination uptake, prompting focused outreach and educational efforts well before an outbreak occurs. Automation of administrative processes such as prior authorizations further ensures timely, uninterrupted patient care, removing delays that might lead to avoidable hospitalizations or disease transmission.
Why Clinicians Should Care: AI as a Clinical Ally
For clinicians, adopting predictive AI tools directly impacts patient care quality and daily workflow efficiency. AI can flag early indicators of declining health before they become critical, enabling healthcare teams to intervene proactively rather than reactively. Imagine having the capability to anticipate a patient’s deterioration in a chronic condition, allowing interventions well before hospitalization becomes necessary. Predictive insights combined with remote patient monitoring offer clinicians real-time data that translates into actionable clinical decisions, improving patient outcomes and reducing preventable hospitalizations.
Moreover, AI reduces clinician burnout by minimizing repetitive administrative tasks, allowing more time for direct patient care and meaningful patient interactions. Enhanced communication tools also facilitate rapid collaboration among care teams, improving coordination and delivering more effective, personalized care.
Why Now is the Time to Act
The current measles outbreak emphasizes a critical lesson: waiting to respond to health emergencies is inefficient, costly, and potentially deadly. Health plans and organizations must proactively invest in advanced predictive technologies to safeguard public health, ensure sustainability, and deliver efficient, high-quality care.
The resurgence of measles starkly illustrates that proactive population health management powered by predictive analytics and AI is no longer optional—it is essential. Health plans and clinical teams must embrace advanced technologies to anticipate health risks, streamline operations, and deliver timely interventions that protect community health and organizational stability.
