Hospital leaders today are navigating a landscape of immense and contradictory pressures. You are tasked with pioneering the future of medicine while simultaneously managing the stark reality of shrinking budgets. In this environment, the conversation around AI in healthcare has grown louder, with enterprise AI presented as a powerful solution promising to unlock new efficiencies and drive revenue.
Yet for many executives, the adoption of enterprise AI feels less like a strategic investment and more like a high-stakes financial gamble. The path to innovation is paved with significant capital expenditures and uncertain outcomes. It’s a reality that forces leaders to question not just the potential of a new technology, but the fundamental wisdom of the investment itself.
From Blind Faith to Guaranteed Returns
The source of this risk lies in the conventional approach to buying healthcare technology. This traditional procurement model is defined by significant upfront investments and fixed, recurring fees that are paid regardless of the results delivered. It is a structure that forces a hospital to pay for a promise, shouldering the entire financial burden if that promise goes unfulfilled.
This creates a fundamental disconnect between a technology vendor’s compensation and a hospital’s actual success. When a multi-million-dollar enterprise AI implementation fails to deliver a tangible return on investment, the subscription fees continue to drain precious operational budgets.
It’s time we moved past this broken business model of blind faith and demanded a new standard of guaranteed returns.
Building AI That Delivers Verifiable Results
A technology partner can only break from the “pay-for-promises” model if their underlying technology is fundamentally superior and proven to deliver. A guarantee is only as credible as the AI engine that powers it. This belief has been the cornerstone of our mission at Inspirata for over two decades.
Our singular focus has been oncology, a complex field where critical clinical data is often locked away in unstructured reports. We have dedicated over 20 years to building a true end-to-end AI platform, powered by an AI/NLP/LLM hybrid engine, that supports a cancer center’s entire informatics journey. This integrated system begins with automated casefinding for the cancer registry, extends to detailed data abstraction and cancer reporting, and culminates in matching cancer patients to life-saving clinical trials.
We can re-engineer the clinical trial pipeline in three steps:
- Automate the foundational work of the cancer registry by using AI for reportable case identification, automated coding, and abstracting critical tumor-related data elements and cancer reporting.
- Utilize the same structured clinical data for trial matching allowing teams to assess patient populations for trial feasibility and auto-generate cohorts of patients for pre-screening.
- Provide point-of-care insights by scoring patient-to-trial matches, aligning patient profiles with complex trial criteria to find the perfect clinical trials options for cancer patients.
A High-Value Problem Demanding a No-Risk Solution
Nowhere is the gap between the promise of AI in healthcare and financial reality more apparent than in the challenge of clinical trial matching and trial accruals. The process of identifying and screening eligible patients is a notoriously manual, time-consuming effort often called the “chart chase.” This inefficiency contributes to failed trials, burns out clinical staff, and represents a massive loss of potential revenue from poor clinical trial accrual.
This is a high-value problem that demands a no-risk solution. While technology can automate this process, the traditional procurement model still forces hospitals to make a significant upfront investment. A truly effective solution must address not only the workflow challenge but also the financial barrier to adoption.
Introducing the Performance-Based Partnership
Amongst the sea of failed AI initiatives, partnerships provide an island with a 67% success rate according to MIT’s NANDA initiative. This is why we are moving beyond outdated procurement models to champion a new approach: a true pay-for-performance model structured as a partnership. This model is built on a simple, powerful concept where we align our success directly with our clients’ outcomes. We deploy our clinical trial matching technology with no upfront cost or fixed licensing fees.
For us, this isn’t just a business model; it’s a commitment.
As one of the many having lost family members to this disease, I believe our success must be a direct reflection of patient success, which is why our compensation is based purely on the measurable value we create. Specifically, we are paid a percentage of the incremental revenue our hospital partners generate from the increase in sponsored clinical trial accrual our technology delivers. This transforms the dynamic from a simple transaction into a true strategic partnership, with shared goals and shared success.
From Funding Gaps to Financial Growth
Many clinical trial programs, historically supported by grants, now face significant funding gaps due to government cuts. This new financial reality means that profitability is directly linked to the ability to successfully recruit for industry-sponsored trials. A pay-for-performance partnership addresses this challenge head-on by eliminating the upfront risk of adopting the technology needed to drive clinical trial accrual for these profitable studies.
This creates a powerful revenue stream that ensures the trial program’s sustainability and growth. The income from pharma-sponsored trials is not a hypothetical figure on a vendor’s slide deck; it is real, tangible revenue that flows directly back to the hospital. This allows the incremental revenue to be reinvested to hire more research staff, expand the trial portfolio, and turn the end-to-end AI platform into an engine for achieving the hospital’s core mission.
A New Standard for Technology Procurement
The time for writing blank checks for technology promises is over. In an era of profound financial pressure, healthcare leaders can no longer afford to gamble on uncertain returns. The future of enterprise AI will be defined not by the sophistication of an algorithm, but by the tangible, measurable business outcomes it delivers.
I urge my fellow leaders to demand more from their vendors. Don’t just ask what a platform can do; ask how your potential partner will guarantee its performance. It is time to seek out true partners who are willing to share the risk, stake their own success on the results they deliver, and help you build a stronger future for healthcare technology.
