In recent years, a discontinuity has been emerging in health insurance related to when the need for healthcare becomes observable and manageable.
The evolution of clinical practice—across early diagnosis, continuous monitoring, and risk management—is shifting the point at which the insured enters the care pathway, introducing an intermediate phase between a state of health and a manifest clinical event.
This shift transforms need from a discrete event into a process distributed over time, characterized by progressive signals and increasingly frequent follow-up activities.
Impact on the Insurance Model
The health insurance model remains built around formal triggers and codified benefits. It works when need translates into clearly identifiable actions—procedures, hospitalizations, therapies—but is less structured in the phase where need is still forming. This creates a misalignment: medicine generates demand for care earlier and continuously, while coverage activates when that demand takes on a defined and reimbursable form.
The anticipation of need emergence is not neutral for the portfolio. The increase in screening and monitoring translates into higher frequency of services in early stages: more visits, tests, and follow-ups. The critical issue is not only the increase in frequency, but its nature: it is more difficult to model, as it is linked to distributed behaviors rather than discrete events. The phenomenon is already observable in recurring signals within portfolio data: increased outpatient frequency, longer claims sequences, and greater dispersion between frequency and severity. In this context, leakage phenomena also emerge; services that are in fact preventive are formally classified, through prescription, as reimbursable visits or diagnostic tests. This is not fraud, but a dynamic that shifts volumes into covered areas, making frequency more difficult to interpret.
Diagnostics and Predictive Models
Diagnostic and therapeutic innovations are reducing the informational value of point-in-time measurements used at underwriting (such as BMI, blood glucose, and cholesterol), which on their own are no longer sufficient to represent risk. With the introduction of new therapies, the risk profile assessed at policy inception can change significantly over time: it may decrease rapidly in some cases, or increase, especially in the presence of treatment discontinuation.
Diagnostics and predictive models increase information asymmetry: insured individuals have better knowledge of their own risk than the insurer. Direct-to-consumer tests, such as genomic testing, may lead higher-risk individuals to insure themselves more (anti-selection) and at the same time make premiums less sustainable for these segments.
This is compounded by growing economic pressure: some innovative therapies have costs so high that they challenge the ability of traditional insurance models to absorb them.
Consistency Between Offering Model and Portfolio Impact
Finally, this shift introduces an issue of consistency between the offering model and technical sustainability, which already emerges at the level of portfolio analysis. If an increasing share of activity takes place in this intermediate phase and becomes reimbursable through formal classification as covered services, the risk is that part of the cost is already reflected in the loss ratio without being explicitly recognized as such. This can contribute to a gradual erosion of technical balance and make pricing more complex. For the insurer, the issue is not only whether to extend coverage, but whether the portfolio is already exposed to these dynamics without having been designed and governed accordingly.
This misalignment translates into a gap between the moment when need emerges and the activation of coverage: a space in which need is already clinically relevant but not yet insurable.
In this context, prevention takes on a different role. From an ancillary lever, it can become a point of activation of the insurance relationship along the healthcare pathway, intercepting needs that are not yet formalized. Its integration, however, introduces technical complexities. In particular:
- the different time horizon between prevention and policy: economic benefits are distributed over multi-year periods and often exceed the contractual duration.
- variability of engagement: effectiveness depends on the insured's adherence, often heterogeneous.
- dispersion of economic return: ROI varies by condition, target population, and intensity.
In this context, the design of health coverage cannot ignore the point at which need emerges along the care trajectory, because it is in that phase that a growing share of costs is generated, often already present in the numbers but not explicitly recognized. The issue is not whether to intercept these needs, but whether the portfolio is already exposed to such dynamics without having been designed and governed accordingly. From this perspective, prevention services are not an extension of the offering, but a potential lever to manage frequency and usage dynamics that are already underway.
