Choosing a digital pathology solution is one of the most consequential technology decisions a pathology practice can make. Get it right and you accelerate turnaround times, improve diagnostic quality, and build a platform that scales with your practice for years. Get it wrong and you end up with an expensive system your pathologists resist using, a workflow that added complexity instead of removing it, and a vendor relationship that falls apart the moment something goes wrong.
After working with hundreds of labs through the evaluation and implementation process, Lumea has seen the same mistakes come up repeatedly. Here are the ten most common ones, and what to do instead.
1. Focusing Solely on Cost
Cost matters, but it is rarely the right primary filter. A lower-priced solution that requires duplicate workflows, doesn’t integrate with your LIS, or lacks the rendering speed that drives adoption will cost significantly more in lost efficiency, staff time, and eventual replacement than a well-designed platform priced at a premium.
The more productive framing is total cost of ownership over three to five years, which includes licensing fees, implementation costs, storage, training, ongoing support, and the productivity impact of the transition period. A solution that delivers a 76% reduction in lab time or a 53% faster ancillary test turnaround can pay for itself within months. One that doesn’t deliver those gains never will, regardless of the initial price.
What good looks like: ask vendors for documented ROI data from current customers in practices similar to yours, not theoretical projections.
2. Not Evaluating LIS Integration Depth
This is the most consistently underestimated factor in digital pathology vendor evaluation, and the most common source of post-implementation regret. Every vendor will tell you they integrate with your LIS. What that means in practice varies enormously.
A shallow integration means the viewer opens as a separate application with a separate login and no bidirectional data flow. Case information doesn’t carry over automatically. Reporting happens outside the viewer. The result is a parallel workflow that adds steps rather than removing them, which is precisely why many labs that adopted digital pathology early ended up with systems collecting dust.
A deep integration means case intake, image routing, AI results, molecular test ordering, and report generation all happen in one workspace without toggling between systems. Before any contract is signed, ask the vendor to demonstrate their LIS integration live on a real case, not a staged demo. Ask specifically whether the integration is bidirectional and whether it was built by the vendor or relies on a third-party middleware layer.
3. Ignoring Rendering Speed
Rendering speed is the single most important factor in pathologist adoption, and it is the one most commonly glossed over in vendor demos. A demo environment with a handful of optimized slides on a high-bandwidth connection will always look fast. Your production environment with hundreds of concurrent users and real case volumes may not.
Ask vendors for their average rendering time under production load, not demo conditions. Ask to speak with reference customers about their daily experience. If pathologists experience lag when navigating slides, they will revert to glass. This is not a preference issue; it is a rational response to a tool that slows them down.
What good looks like: full slide rendering in under a second under typical production load, with smooth navigation at all magnification levels on the devices your pathologists will actually use.
4. Not Requesting a Real-Case Demo
Most vendor demonstrations are carefully choreographed with optimized slides, ideal network conditions, and a trained presenter who knows exactly where to navigate. This tells you what the software looks like when everything goes right. It does not tell you how it performs in your actual workflow.
Before any purchasing decision, ask for access to a sandbox environment where you can load your own cases and have your own pathologists navigate them. Real cases reveal issues that optimized demo slides hide: performance on larger or more complex specimens, behavior on slightly out-of-focus scans, and how the interface feels to someone who didn’t help build it.
Dr. Syed Hoda of NYU Langone, who led a full digital transformation without prior digital pathology experience, described asking vendors basic, practical questions from a user’s perspective as one of the most valuable parts of his evaluation process. The vendors who could answer those questions clearly, and demonstrate rather than describe their answers, were the ones worth taking seriously.
5. Overlooking Pre-Analytical Tissue Quality
Most digital pathology evaluations start at the scanner. The assumption is that specimen quality is a given, and the technology evaluation begins with the image. This is a significant oversight.
The quality of the digital image is fundamentally limited by the quality of the tissue on the slide. Fragmented cores, disoriented specimens, and poor fixation produce poor images regardless of scanner resolution or viewer quality. Labs that have invested in pre-analytical tissue-handling technology consistently report better diagnostic outcomes from their digital pathology deployments than those that didn’t, because they are giving pathologists better material to work with from the start.
Lumea’s BxBoard® reduces prostate core fragmentation by 35% and the BxChip® increases tissue surface area on the glass slide by an average of 14.5%, contributing to an 18% improvement in cancer detection rates. These gains happen before a single slide is scanned. When evaluating a digital pathology solution, ask vendors what role they play, if any, in improving specimen quality upstream of the scanner.
6. Choosing a Solution That Can’t Scale
The right solution for your practice today needs to still be the right solution when your case volume doubles, when you add a second site, or when you expand into new specialties. Many platforms are designed for a specific scale and struggle significantly when pushed beyond it.
Ask vendors specifically how their platform performs at two to three times your current volume. Ask for reference customers who have scaled significantly since implementation and speak to them directly. Scalability failures tend to show up as performance degradation, not outright crashes, which means the problem often develops gradually and is attributed to other causes before the real issue is identified.
What good looks like: a cloud-based or hybrid architecture that scales storage and compute resources automatically without requiring infrastructure changes on your end each time volume increases.
7. Not Involving Key Stakeholders Early
Implementations fail when the people who will use the system daily weren’t involved in choosing it. Pathologists who feel a system was imposed on them approach it with skepticism that compounds into resistance. Lab staff who weren’t consulted discover workflow gaps that could have been identified in advance. IT teams brought in late find infrastructure requirements that delay go-live.
The labs that achieve the smoothest transitions involve pathologists, pathologists’ assistants, histotechs, IT staff, and billing personnel from the beginning of the evaluation process, not just at sign-off. Their input improves the selection decision and their involvement creates the ownership that drives adoption.
This is not just a change management best practice. It is the difference between a platform your team chooses and defends, and one they tolerate and eventually work around.
8. Failing to Evaluate AI Ecosystem Openness
Whether or not you plan to use AI tools immediately, the platform you choose determines which AI tools will ever be available to you. Platforms with closed or proprietary AI ecosystems lock you into whatever algorithms the vendor has built or partnered with. Platforms with open ecosystems let you integrate the best available tool for each application as the market evolves.
This distinction matters more than it might appear today. The AI pathology market is moving quickly, and the tools most relevant to your specific case mix in 2028 may not exist yet. Locking yourself into a closed ecosystem now means you will either need to replace your entire platform to access them, or make do with inferior tools because switching is too disruptive.
Ask vendors explicitly: can you integrate any third-party AI algorithm, or only approved partners? What is the process for adding a new AI tool? Who controls that decision?
What good looks like: an open marketplace where your lab can select AI tools from multiple vendors and have them surface results directly within the diagnostic workspace, without toggling to a separate application.
9. Not Accounting for Long-Term Costs
The initial licensing fee is rarely the largest cost component of a digital pathology implementation over a five-year horizon. Storage costs, which grow with every case scanned, can become substantial at scale. Training costs recur every time you hire new staff or upgrade the platform. Support contract fees, integration maintenance, and upgrade costs all add up.
Cloud storage in particular deserves careful modeling. A lab generating 100GB of new whole slide images per day accumulates significant storage costs at scale, and many labs discover this later than they should. Ask vendors for a five-year total cost of ownership model based on your actual projected case volumes, including storage growth.
Also ask about what happens to your data if you ever switch vendors. Data portability and migration costs are real considerations that are rarely discussed in initial sales conversations but matter significantly if your needs change.
10. Underestimating Implementation Support
A great platform poorly implemented delivers poor results. The quality of vendor implementation support, including how they handle LIS integration, how they guide FDA validation, how they train staff, and how responsive they are when problems arise, is as important as the platform itself.
Ask vendors for specifics: who manages implementation, what is the typical timeline, what deliverables are yours versus theirs, and what happens when go-live is delayed. Ask for references specifically from customers who had difficult implementations and how the vendor handled them. Any vendor who only offers smooth-implementation references should be viewed with appropriate skepticism.
The labs that achieve the fastest time-to-value from digital pathology implementations are almost always the ones that selected a vendor who treats implementation as a partnership rather than a handoff.
What Good Vendor Evaluation Actually Looks Like
Avoiding these mistakes means approaching vendor evaluation as seriously as any other major clinical or operational decision. A structured evaluation process typically includes:
- A formal RFP or requirements document that captures your specific workflow, integration, and compliance requirements before you begin vendor conversations. This prevents vendors from defining your requirements for you.
- A live demonstration on your own cases, not vendor-selected slides, in an environment that reflects your actual infrastructure.
- Reference checks with current customers in practices similar to yours, specifically including users at the bench level, not just administrators or executives.
- A total cost of ownership model over three to five years, not just first-year licensing costs.
- A pilot deployment with a defined scope before full commitment, giving your team genuine hands-on experience before go-live.
Lumea’s platform was designed around the requirements that matter most in real clinical environments: rendering speed that drives adoption, deep LIS integration that eliminates parallel workflows, an open AI ecosystem that doesn’t lock you in, and pre-analytical tissue-handling technology that improves the specimen before it ever reaches the scanner. If you’re evaluating digital pathology solutions, we’re happy to be part of that process. Request more information today.
