10-Step Estimation Process Checklist
View our 10 Step Estimating Process Checklist. This checklist should be tuned to the individual company’s needs and suggestions.
41% of healthcare C-suite leaders expect capital investment reductions over the next two years. Simultaneously, 57% rank AI-based clinical solutions as their top technology initiative. When the budget contracts and ambition expands, the estimation gap becomes the most expensive thing in the room.
The squeeze is real
The Sage Growth Partners Health IT Purchasing Forecast for 2026-2027 [link] captures a sector in tension. Capital budgets are tightening: 41% of surveyed C-suite leaders anticipate reductions, and only 5% expect increases of 20% or more. Meanwhile, AI has overtaken electronic medical record (EMR) optimization as the top technology priority, jumping from 19% of C-suites in 2023 to 57% today. Administrative AI solutions have seen a similar surge, rising from 6% to 29% over the same period.
The strategic pivot is clear: health systems are reallocating away from traditional overhead and toward tools that can demonstrably impact top-line performance. Return on investment has become the defining purchasing metric, with 77% of executives now ranking anticipated ROI as the most critical factor, up from 50% in 2023. But ROI projections require cost estimates. And healthcare IT has a well-documented problem with those.
The estimation track record
A recent peer-reviewed study published in the Journal of Management Information Systems [link] found that the mean cost overrun ratio for IT projects is 1.8, meaning projects, on average, cost nearly twice their initial estimates. The mean estimated cost was $6.6 million; the mean actual cost was $14.7 million. The distribution follows a power law, meaning that while most projects experience moderate overruns, a significant tail of extreme outliers drives the average far beyond planners’ expectations.
Healthcare amplifies these dynamics. HIPAA compliance alone adds 15-25% to software development costs, typically $50,000-$170,000 on top of what the same application would cost without regulatory requirements. Electronic health record data migration runs $20,000 to $50,000, depending on complexity. Training costs average $1,000 to $5,000 per staff member. Each of these costs may be accurate on its own, but healthcare organizations routinely undercount the cumulative burden because they estimate in silos rather than modeling the full lifecycle.
What the budget squeeze demands
When capital is abundant, estimation errors are absorbed; when capital is constrained, they become visible. The current environment in healthcare IT leaves no margin for the 1.8x overrun predicted by historical data.
The upshot: Health systems making AI investment decisions need estimation rigor that accounts for the full cost envelope: not just the software license or implementation fee, but the compliance overhead, the integration complexity, the training burden, the ongoing monitoring, and the opportunity cost of delayed deployment. Organizations that can produce defensible total cost-of-ownership estimates will secure budget approval. Those relying on vendor-provided ROI projections and best-case implementation timelines will join the long list of healthcare IT projects that looked affordable until they were not.
Estimating Total Cost of Ownership (TCO)
Find out how you can use Total Cost of Ownership (TCO) model to create an estimate which includes all the costs generated over the useful life of a given application.
Should Cost Analysis
Learn how Should-Cost Analysis can identify savings opportunities and drive cost efficiency in procurement and manufacturing processes.
ROM Estimate: The First Step Towards a Detailed Project Plan
Find out what ROM (rough order of magnitude) estimate is and why is it a crucial element of every project planning cycle.
Software Maintenance Cost
Find out why accurate estimation of software maintenance costs is critical to proper project management, and how it can make up to roughly 75% of the TCO.
