Calculating cost avoidance with AI: Measuring what doesn’t happen
Current models don’t accurately affect the biggest potential benefits that artificial intelligence can bring to the industry.
In healthcare’s race toward adoption of artificial intelligence, prevailing conversations often focus on automation, how algorithms can shorten workflows, reduce manual tasks and deliver measurable cost savings.
But the true promise of AI may lie not in what it does, but in what it prevents.
The next frontier of healthcare value is moving beyond reducing the cost of doing something faster and instead measuring what doesn’t happen at all – such as the hospital admission that never occurs, the claim that’s never paid in error or the adverse event that never reaches the patient record.
Measuring prevention
Traditional ROI models are built around visibility. They work when the value of an instance of process improvement can be observed directly, measuring current costs, implementing a solution and quantifying the reduction. This model works perfectly when AI automates a task previously accomplished by humans.
But cost avoidance is different. When AI prevents a negative event, the benefit is invisible. The event never happens and, therefore, its avoided cost doesn’t show up in standard financial models.
How do we calculate the return on investment when the outcome is defined by absence rather than occurrence?
Understanding cost avoidance
Cost avoidance measures the value of prevention, estimating the financial impact of negative outcomes that would have occurred without intervention. In healthcare, this can span clinical, operational and administrative domains.
Examples include:
- Avoided hospital admissions through early detection of patient deterioration.
- Prevented claims fraud or overpayments identified before disbursement.
- Reduced readmissions or redundant testing as a result of better clinical decision support.
- Prevented coding or documentation errors that would have caused compliance penalties or lost revenue.
- Accurate prediction of patient acuity that prevents downstream resource strain.
In each case, when AI systems intervene, they don’t make a process faster, but rather they stop something costly from happening.
The role of counterfactual modeling
Quantifying cost avoidance requires a method to estimate what would have happened if AI hadn’t intervened. This is known as counterfactual modeling – the science of measuring the difference between reality and an alternate version of it.
In practice, this involves building two scenarios. One is building a baseline (pre-AI), which is the historical trend of events such as readmissions, claim denials or patient escalations. The second is post-AI, based on the observed trend after AI deployment.
By comparing these cohorts, we can infer the number of negative events avoided and assign a cost to each. This enables organizations to estimate savings based on prevention rather than correction.
For example, if a predictive model identifies sepsis hours earlier, reducing the average length of stay by one day per patient, that improvement can be quantified in terms of bed cost, nursing hours and treatment expense. If fraud-detection AI intercepts erroneous payments before they leave the organization, those prevented losses become measurable avoided costs.
When events don’t vanish, they shrink
Some interventions eliminate problems entirely, while others simply reduce their severity or frequency. A claims anomaly might still occur, but earlier detection minimizes exposure. A deteriorating patient might still need escalation, but intervention within minutes instead of hours reduces days that patient might spend in an intensive care unit.
This nuance is important. Measuring cost avoidance isn’t always binary. It’s often a gradient of avoided impact. Time-based metrics can be particularly powerful here.
Methods to calculate cost avoidance
There is no single formula, but several strategies help bring structure to the estimation:
Time-to-detection/time-to-intervention analysis. Compare the average time between event onset and intervention before and after AI implementation. The reduction in time directly correlates to cost savings in treatment intensity or length of stay.
Trend Interruption analysis. Evaluate historical trends for a specific adverse outcome such as readmission rates or fraud incidence and assess the inflection point when AI was introduced. A sustained downward deviation indicates quantifiable cost avoidance.
Cohort comparison. Compare outcomes between patient or transaction groups exposed to AI vs. those not exposed. This isolates AI’s contribution and supports a more credible estimate of avoided costs.
Each of these methods transforms an abstract concept – what didn’t happen – into a measurable business outcome that finance leaders can understand.
Transformational potential
Healthcare’s cost structure is dominated by preventable events – adverse drug reactions, readmissions, documentation errors and fraudulent claims. These are rarely solved through efficiency alone. They are solved through foresight by systems that can sense and intervene before harm or loss occurs.
Cost avoidance is the primary promise of AI, not a secondary benefit. While process automation may yield incremental savings, prevention yields exponential impact. The ability to predict and preempt is what makes AI transformative rather than transactional.
When health systems begin to view their AI investments through the lens of avoided costs, they shift from measuring efficiency to measuring resilience. They start valuing the unseen, the crises that didn’t unfold, the dollars never spent, the patient harm never realized.
A new metric for AI ROI
For decades, healthcare has relied on cost reduction as the north star of ROI. Yet in a data-driven, predictive ecosystem, the most valuable outcomes are often those that can’t be seen directly.
Quantifying cost avoidance is admittedly more complex, but that complexity mirrors the real world. Prevention is more difficult to measure than correction, but it’s infinitely more valuable.
As AI continues to mature across the healthcare continuum, evaluation models must mature with it. The future of ROI will include the invisible ledger of avoided costs, where value is measured not only by what AI does, but by what it saves us from ever having to do.
Jan Sevcik, FACHDM is co-founder and CEO at Medical Search Technologies, which specializes in assessing AI for healthcare applications.