The Future of HealthTech Mid-Year Check-Up — AI Economics, Cost Pressure, and Relative Positioning

Research evidence excerpt

The Future of HealthTech Mid-Year Check-Up — AI Economics, Cost Pressure, and Relative Positioning

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budgets are allocated.

AI Durability in HealthTech: Four Drivers of Defensibility

We assess AI disruption risk through four dimensions that shape insulation and

differentiation over time. We reference Waystar’s AI defensibility framework and

assess HealthTech AI durability across four dimensions: mission-critical infrastructure,

proprietary datasets, scaled networks, and industry expertise. These factors tend to drive

stickiness, outcomes, and defensible economics as AI capabilities proliferate. Together,

they help distinguish more insulated businesses from those increasingly exposed to new

AI entrants.

Mission-critical infrastructure creates switching costs because embedded

workflows are hard to replace. This pillar covers software that is deeply embedded in

day-to-day activity, where reliability and continuity matter and where failure creates

operational and financial risk. The emphasis is on solving immediate problems, such as

decreasing time to revenue for providers, speeding up clinical trial timelines through

better patient matching and activation, and reducing administrative work in coding,

documentation, scheduling, and claims. The more integral the system is to daily

operations, the harder it is to displace, and the clearer the path tends to be for AI to

deliver measurable efficiency and labor leverage within the workflow.

Proprietary datasets strengthen differentiation because they improve model

performance and make outcomes harder to replicate. Access to data beyond what is

publicly available becomes a moat when it improves workflows, outcomes, and

innovation in ways that generic AI models cannot replicate. The impact shows up in end