Health payers are under significant pressure from rising healthcare costs, raising renewed questions about how they will fund access to new treatments and manage affordability.
This article – and a panel discussion I was part of at the 2026 Geneva Association’s Health and Demography Conference – explores emerging drivers of healthcare costs and revisits existing cost management strategies, challenging payers to take a more active approach by leveraging their data, applying proven strategies, and taking advantage of AI-enabled innovation.
While the drivers of healthcare costs vary from market to market, the U.S.1,2 often provides leading indicators for payers worldwide. In addition, policy changes in the U.S. can affect healthcare costs internationally, while some non-U.S. payers fund care in the U.S. The following sections explore several current U.S. cost drivers and emerging risks, the development of which non-U.S. payers should be closely monitoring in their markets.
GLPs have emerged as a significant driver of healthcare costs for U.S. payers3,4 as approved indications and access have expanded from diabetes to obesity and cardiovascular risk reduction, alongside increasing consumer demand. Payers should expect further widening of indications in the future – such as neurodegenerative conditions – while oral versions of the drugs may alter utilization and adherence patterns. U.S. prices of GLPs have been significantly higher than the offsetting benefits to date, based on emerging evidence,5 driving net increases in healthcare costs, although recent regulatory initiatives have exerted downward pressure.3 In non-U.S. markets, the prevalence of indicated conditions may differ, prices are often lower, and generic versions may come to market earlier,6 but if utilization follows a similar trajectory to that of the U.S., there likely will be a meaningful net increase in healthcare costs, particularly in the short term.
Cell and gene therapies have relatively low utilization, but with treatment costs totaling several million dollars (in many cases), these therapies have the potential to significantly increase healthcare costs and make them more volatile. The pipeline of more than 4,000 new therapies in development7 could expand the use of high-cost therapies significantly – for example, the treatment of autoimmune conditions. As indications, approvals, and treatment centers expand globally, payers outside the U.S. will need to consider how to manage the risk and funding of these therapies.
From accelerating clinical trials to improving imaging diagnostic accuracy and speed, AI is already demonstrating the potential to positively affect health outcomes. However, AI-based ambient scribes – automated note-taking tools – also have the potential to increase healthcare costs by increasing coding intensity, and emerging evidence suggests this may be the case.1,8 Changing coding intensity presents a problem not just under fee-for-service billing, where additional services may be billed, but also under alternative reimbursement models, where patients may be classified into higher risk categories, driving up reimbursement. Payers will need to closely monitor coding behavior and adapt reimbursement models and levels to manage resulting increases in healthcare costs.
Recent changes in U.S. drug pricing9 – where domestic prices will be internationally benchmarked against other developed countries (a policy known as “most favored nation” pricing) – have the potential to affect drug prices in other countries, given the scale of the U.S. pharmaceutical market. For example, to maintain U.S. prices, companies could raise the international prices used as benchmarks, increasing healthcare costs for public and private payers. The policy could also result in drugs being withheld or withdrawn from peer-group nations to avoid anchoring a price at a low level.
Where healthcare cost increases are related to treatment advances, significantly reducing those specific costs may be challenging in the short term. However, payers can focus their attention on implementing or improving existing managed care programs to better manage and optimize claims costs in other areas. In this way payers can maintain access to treatment advances and relieve the budgetary pressure they cause without increasing premiums and compromising affordability.
Pre-authorization, case management, provider networks, and pharmacy benefit management have been employed for many years by payers seeking to actively manage healthcare costs. But are these programs as effective and efficient as they could be? “Efficient” inpatient provider networks look beyond fee discounts and select providers based on risk-adjusted benchmarking of total admission cost. For example, provider A may offer greater fee discounts compared to provider B, but when their length of stay, level of care, associated provider and facility costs, and the complexity of their patients are taken into account, provider B may offer better value to the payer. Off-the-shelf diagnosis-related group (DRG) software can accelerate the development of these models, while more sophisticated networks would also be selected based on outcomes. In pre-authorization and case management, AI may offer decision-support and automation-improvement opportunities (subject to appropriate guardrails).
In less mature markets, introducing relatively standard forms of these programs may still yield significant savings, balanced against the increased administrative costs of implementation, along with provider and consumer friction.
Population health management (PHM) is a data-driven approach that enables payers to improve health outcomes and reduce healthcare costs across their insured population by identifying risk-stratified cohorts and implementing targeted, tailored interventions to address their needs. A common approach is to stratify the insured pool into low-, rising-, and high-risk subgroups – based on predictive models – and develop interventions for each. For example, high-risk subgroups would typically contain individuals with multiple chronic illnesses who would benefit from multimorbidity management interventions. The focus for the low-risk (healthy) subgroup would typically be wellness and prevention to maintain that healthy state.
While principles of the PHM approach have not necessarily changed in recent years, richer data – including from unstructured sources, wearables, and remote photoplethysmography (rPPG) tools – create further opportunities for payers to manage healthcare costs and outcomes.
Essential to this progress are more powerful analytic tools to draw insights from that data and more efficient ways (for example, GenAI) to convert those insights into personalized care pathways.10 Compared to broad-based wellness and prevention or traditional disease management initiatives, risk-based PHM approaches target the right individuals with the right interventions, leading to better outcomes and short- and long-term return on investment for the payer.
Reimbursement reform long has been seen as key to “bending the cost curve.” By moving away from fee-for-service to bundled fees or capitation, often with strong links to outcomes, incentives to efficiently manage the cost of care transfer from the payer to the provider. In some markets, national-level reforms will promote this change, while in others, private payers will need to take the initiative. For example, payers with good data and analytic capabilities are well placed to identify procedures with wide, unexpected variation in cost that are prime targets for bundled fees (or, in a phased implementation, notional bundled fees). By working with provider organizations – sharing data and crafting solutions – individual payers can make incremental progress toward wider reimbursement reform.
Incentivizing the delivery of healthcare in the most cost-effective setting is another long-standing managed care approach where further gains may be possible. Incentives or metrics in provider network arrangements can be used to promote in-room procedures (where appropriate), while consumer incentives such as co-payments can steer care to more cost-effective providers – including nurses or physician assistants – and countries, where practical. Wider utilization and acceptance of virtual care and remote monitoring have increased the scope for home-based care programs.
The success of many of these initiatives is predicated on detailed and complete health data, most critically accurate diagnosis, procedure, and drug coding, but also clinical data for more advanced applications. Payers that invest in data and analytics – capture, storage, validation, computing, and models – to understand the drivers of healthcare costs and areas of inefficiency, and use it effectively to craft strategies to manage them, will be better positioned to manage growing healthcare costs into the future.
In many international markets, private payers remain relatively passive in managing increasing healthcare costs and will need to adopt more active, and activist, strategies. While the range of initiatives required is extensive, and success may not be immediate, payers that embrace systematic, integrated approaches and stay the course can achieve a competitive advantage. Such payers are also likely to best serve consumers by managing premium affordability and providing better access to healthcare advances.
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