Therapy allocation is one of the most overlooked considerations in healthcare reform. While new technologies and innovative treatments often steal the spotlight, the deeper issue lies in how therapies are distributed and why. Historically, many European healthcare systems have rewarded volume over value, leading to the overuse of outdated or misaligned treatments. Known as "legacy therapies," these entrenched medical practices are often driven by what systems can bill rather than what patients actually need. As healthcare inflation rises and demand surges, this outdated incentive structure becomes not only inefficient but unsustainable.
This article dives into the economic and operational consequences of relying on legacy therapy models. It examines how infrastructure-driven billing distorts care priorities, contributing to rising costs and stagnant outcomes. Drawing on insights from Chapter 3.3 of our Playbook “Before It Hurts”, we contrast these legacy systems with emerging patient-centric approaches, including AI-powered treatment personalization and real-time health monitoring. We will learn how shifting from volume-based to value-based therapy allocation can promote cost transparency, reduce unnecessary interventions, and ultimately lead to smarter, more sustainable healthcare delivery models.
In systems operating under fee-for-service or DRG-based (Diagnosis-Related Group) models, therapy allocation is dictated not by clinical need, but by logistical opportunity. The incentive is to use what’s available: if an MRI machine is absent or an operating room is unbooked, providers are often financially motivated to fill that slot. This dynamic, known as supply-induced demand, causes a proliferation of low-value interventions. Rather than aligning with evidence-based care pathways, treatment becomes a function of infrastructure utilization, leading to overdiagnosis and excessive procedures. The disconnect between what is medically necessary and what is administratively expedient erodes both cost-efficiency and care quality.
Compounding the issue is a reimbursement ecosystem that structurally rewards volume. National audits in Germany, the UK, and other EU member states have revealed chronic misbilling issues, including widespread upcoding, where routine treatments are classified as more complex for higher reimbursement, and bundling of unnecessary diagnostics. While not always corrupted, these practices are incentivized by systemic pressures to meet financial targets rather than optimize patient outcomes. The result is a healthcare economy where financial success can be separated from medical effectiveness, further entrenching inefficiencies.
The economic impact is especially highlighted in the case of legacy therapies—treatments like insulin, statins, or older biologics that have long histories of use but remain protected by outdated pricing structures. Even with the availability of generics and biosimilars, prices often remain artificially high due to limited price transparency, slow regulatory enforcement, and weak competition in procurement markets. These legacy treatments absorb disproportionate slices of healthcare budgets, crowding out investments in preventive strategies and data-driven innovations that could yield greater value per euro spent. As long as volume-based incentives persist, so too will the dominance of therapies that are reimbursed well, even if they no longer represent the best care available. Check our blog post on why legacy treatments are becoming shockingly expensive here!
Rather than defaulting to the most billable treatment, AI-driven clinical decision tools are now capable of synthesizing real-world data, longitudinal health records, and patient-generated insights to propose highly individualized care. These platforms use machine learning to identify treatment options that are statistically more likely to yield positive outcomes for specific patient phenotypes, accounting for comorbidities, genetic markers, behavioral patterns, and biometric trends. By integrating data points such as VO₂max, resting heart rate, circadian rhythm deviations, and long-term activity trends, providers can tailor treatment in ways previously reserved for research settings. This shifts the clinical paradigm from reactive and generalized interventions to proactive and precision-based management. Moreover, because these AI models are continuously learning from new data, they allow for dynamic re-evaluation and adjustment of care plans over time, enhancing both efficacy and cost-effectiveness.
Legacy therapy models often thrive in environments where outcomes are difficult to measure, and inefficiencies go unchallenged. Wearables, remote monitoring systems, and patient-reported outcome tools are now transforming that opacity into measurable insight. By continuously collecting physiological and behavioral data, these technologies help nurture the true effectiveness of various innovations in real time. For example, a hypertension treatment protocol that fails to improve blood pressure variability or activity tolerance over several weeks can be flagged and adjusted earlier, avoiding months of wasted expenditure and clinical inertia. When paired with analytics dashboards for insurers and care teams, this data enables actionable healthcare cost transparency. Providers can benchmark performance, identify ineffective therapies, and prioritize treatments that align with real-world improvements in healt, not just protocol adherence. This precision dramatically reduces overtreatment, enhances patient trust, and reallocates resources toward interventions that generate demonstrable value.
Healthcare inflation is not simply a macroeconomic concern; it is a structural accelerant of inefficiency in care delivery. As labor costs, facility upkeep, medical supplies, and regulatory compliance become more expensive, providers operating under legacy therapy models are incentivized to recoup these rising costs through increased service volume and higher-priced procedures. The result is a reinforcing loop where older, well-reimbursed treatments like routine surgical interventions, diagnostic imaging, and long-standing drug regimens are used more frequently and cost more over time, despite limited gains in clinical value. This cycle drains public health budgets, inflates insurance premiums, and leaves little financial room for innovation or personalized care models.
Furthermore, innovation stagnates when the financial infrastructure rewards legacy billing over forward-thinking interventions. Providers have little reason to adopt more targeted or preventive approaches if these aren't reimbursed at comparable rates. But AI has the potential to disrupt this dynamic. Predictive analytics tools can model patient trajectories, flag those at risk of escalation, and guide early, less invasive intervention strategies that improve outcomes while lowering costs. These systems can also inform payors when a high-cost legacy therapy is likely to yield poor ROI relative to a newer, data-backed alternative. By shifting investment toward prevention and long-term disease management, rather than episodic, inpatient care, AI can help flatten the inflation curve and reorient spending around patient-centric value rather than institutional throughput.
To dismantle the deeply embedded volume-based incentive structures, governments and payors must rethink the regulatory frameworks that govern therapy allocation. This means going beyond minor payment reforms to adopt comprehensive systems that prioritize outcomes over throughput. Stricter audit regimes are essential to identify and curb practices like upcoding, overtreatment, and unnecessary bundling. But audits alone won’t suffice—true transformation hinges on enhancing data interoperability across care networks. By linking provider performance with longitudinal patient outcomes, regulators can enforce reimbursement models that reward sustained health improvement, not episodic intervention. Risk-balancing formulas also require refinement to prevent providers from being penalized for treating high-risk populations. Instead, incentives should support proactive, continuous care and reward reductions in hospitalization rates, chronic disease progression, and relapse events.
The role of insurers is rapidly evolving from passive bill-payers to active orchestrators of healthcare value. Forward-looking insurers are using patient-generated health data (PGHD) to monitor wellness, detect early signals of deterioration, and nudge healthier behaviors through incentive programs. Examples include dynamic premiums linked to wearable-verified sleep quality, physical activity, or stress management indicators. These aren’t just engagement tools, they serve as cost-containment mechanisms. By identifying modifiable risk factors early, insurers can direct members toward preventive interventions and avoid costly escalations to legacy therapies. Moreover, integrated digital platforms now allow insurers to collaborate directly with providers and care managers, creating closed-loop systems where interventions are measured, optimized, and continuously adapted. This shift is critical: when insurers act as health managers, they can help realign the entire ecosystem around patient-centered outcomes and long-term cost sustainability.
Legacy therapy systems are built for system efficiency, not patient efficacy. But in an era of chronic disease, aging populations, and ballooning costs, this model is failing. By embracing AI personalized treatment plans and cost transparency tools, insurers can allocate therapies based on patient needs, not billing targets.
Thryve steps in as a connecting bridge between patient data and quality health outcomes. With our API that was specifically developed for healthcare solutions, we provide services such as:
Ultimately, a shift from volume-based incentives to value-based care is no longer aspirational; it’s operationally and economically essential.
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