David Hamilton , 2025-04-17 13:38:00
With rising operational costs, major shifts in Medicaid, and other financial pressures bearing down on the healthcare system, forecasting revenue and allocating resources effectively has never been as important for health plans as it is today. And anticipating the future has probably never been so challenging.
Health plans have been gradually deploying artificial intelligence programs and sophisticated analytics for years to make programs more effective while reducing costs and mitigating financial risk.
But with today’s challenges, the gradual approach has become a luxury. According to McKinsey, health plans should pick up the pace.
Healthcare organizations need to jump in with both feet when it comes to AI, as mitigating risk is quickly becoming an existential business issue. Machine learning has quickly gone from being a nice-to-have to a must-have.
For every $10 billion of payer revenue, AI solutions could save $150 million to $300 million in administrative costs, save $380 million to $970 million in medical costs, and increase revenues by $260 million to $1.24 billion, according to McKinsey
Health plans should no longer be debating whether or not to invest in AI and automation but must shift the focus entirely on how to deploy these technologies strategically.
Some health plans are starting to see the benefits of predictive analytics enabled by AI, which, when combined with clinical BPO services that add expert decision-making to care management, give them a scalable approach to improving the health outcomes of members while helping cut costs. AI-driven care coordination is simplifying complex workflows for many payers.
An example of this innovative approach involves going deeper into risk stratification by better identifying and intervening for rising risk populations, or people not yet classified as medically high-need or high-cost — but who are on their way to becoming so.
Due to the climbing rates of heart disease, obesity, asthma, and mental health conditions, the rising risk patient population is growing. For payers, getting a handle on rising risk for earlier clinical support and intervention will be a make-or-break issue as the healthcare system continues to deal with increasing cost pressures.
Understanding rising risk
Categorizing patients into high, moderate, and low-risk groups based on data analysis has always been foundational in healthcare for both providers and payers. It’s what ensures the best resource allocation by making sure the highest risk patients get the attention they need.
But many healthcare organizations could be reaping more value from the risk-stratification process by using new technologies to identify and quickly craft medical interventions for rising risk patients.
Traditionally, insurers have stratified patients into risk categories based on claims data, which analyzes healthcare utilization after services have been rendered and paid for. This means health plans have been making critical decisions by looking in the rearview mirror. New technologies and improved access to clinical records are now enabling plans to look forward, which is increasingly necessary in a healthcare industry beset by uncertainty.
The earlier that rising risk patients can be identified, the sooner health plans can implement strategies to prevent or slow disease progression, reduce trips to the hospital, and bring long-term costs down.
Risk stratification goes from being reactive to predictive with the addition of advanced data analytics and AI. The health plans that have already embraced this approach can detect early indicators of disease before costly interventions are necessary.
New predictive models analyze not just claims data but prior authorization trends, historical diagnostic and prior therapeutic steps in EHR data, and real-time clinical inputs to identify patients who are the most likely to see a deterioration in their health condition.
But identifying rising risk is just one benefit. Acting on this insight opens up a range of cost-saving opportunities for healthcare organizations.
Getting ahead of problems
Understanding rising risk patients lays the groundwork for proactive healthcare, something that helps patients, families and the organizations that care for them.
By combining clinician-led care coordination, automated prior authorization, remote patient monitoring, AI-driven alerts, and other new technologies and approaches, health plans and other managed care organizations can improve access to the most impactful and necessary care — especially for patients with chronic conditions who are likely to grow sicker over time.
This approach enables health plans to identify both individuals — and entire patient populations — who are either high risk or on the verge of becoming high-risk, create and tailor interventions in real time, and deploy strategies to get out in front of escalating health conditions.
The ultimate value proposition is the automated support of complex, proactive decision-making. Integrating predictive insights with automated prior authorization workflows makes it possible by ensuring critical services — specialist referrals, diagnostic tests, and medications — are approved without delay. As a result, plan members with rising risk can get the care they need to forestall worsening health, which means better health outcomes.
Health plans that have not yet experienced AI benefits might wonder how these various pieces come together to achieve greater impact at a lower cost than traditional health plan care management programs.
For many, AI-enabled clinical business process outsourcing (BPO) is the linchpin — and enables a model where both administrative and medical costs can be fixed, and predictable. Clinical BPO combines clinician expertise and critical management services with an AI-enabled population health management platform, creating a forward-looking program for rising risk management. Combining these capabilities enables population health management services at a fixed PMPM cost for both the administrative cost of the program and the medical costs for the individuals whose health is managed in those programs.
The benefits of BPO to any risk-bearing managed care organization include:
- Access to clinical expertise across multiple specialties
- Automated care management processes
- Agentic AI and predictive modeling
- Reduced administrative costs
- Sharing of medical cost risk in guaranteed performance arrangements
Healthcare organizations that decide to mitigate risk, taking a proactive AI-supported approach is an imperative today. There is no sign that the cost pressures besetting healthcare will abate any time soon.
Understanding risk is the important first step in bringing it under control, and rising risk is an area that many health plans need to act on — now.
Photo: champc, Getty Images
David Hamilton is the Chief Growth Officer at Zyter|TruCare, leading strategic initiatives to drive business growth, expand market presence, and strengthen partnerships with key payer and provider organizations. With extensive leadership experience from organizations such as Randstad Digital, Datavant/Ciox, DXC/Gainwell, and Cognizant, David brings deep expertise in healthcare technology, services, and business process solutions.
David’s leadership focuses on enhancing healthcare data interoperability, risk adjustment strategies, and payer-provider collaboration, ensuring that organizations navigate regulatory shifts and operational complexities effectively. At Zyter|TruCare, he leverages this background to deliver impactful solutions designed to improve connectivity, streamline administrative processes, and enhance patient-centric care.
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