Digital Pathology Insight Series
When Your Lab Outgrows Your Current Scanner: Five Lab Capacity Planning Considerations for High-Throughput Digital Pathology
Slide scanner capacity problems rarely announce themselves cleanly. They accumulate in overtime rosters, in backlog reports, in the quiet knowledge that the current setup cannot absorb the next growth step. Recognizing the signs early is the difference between a planned upgrade and a crisis response.
Most lab managers do not decide to evaluate a new scanner. They find themselves already past the point where they should have. In many cases, shortcomings in lab capacity planning only become visible once throughput limitations begin impacting day-to-day operations. The workflow has been stretched, patched, and redistributed until the signs that appeared six months ago, and probably twelve months ago, have become part of the daily operational reality. By the time the throughput failure is visible in TAT reports or accreditation findings, the evaluation window has narrowed to crisis procurement rather than structured assessment.
Pathology request volumes are rising at an estimated 5–10% per year, driven by ageing populations, expanded cancer screening programs, and the growing complexity of precision oncology workups (Bray et al., 2024 as cited in L’Imperio et al., 2026). At that growth rate, a laboratory operating at 80% of scanner capacity today will reach 88–98% within twelve months, even before accounting for peak demand events. The five signs below describe the operational milestones that can assess a lab’s progression toward a capacity failure. Most labs will recognize more than one.
Your daily slide volume routinely exceeds 80% of rated scanner capacity
This is often the earliest signal that it may be time to upgrade your lab scanner. Published operational data from high-volume implementations shows that scanner throughput in routine practice consistently falls below advertised specifications because of scan time variability, rescan events, and loading overhead (Schwen et al., 2023). A scanner rated for 400 slides per shift does not deliver 400 slides per shift in a live clinical environment. If your daily volume is already at 80% of the rated figure, your real-world headroom is limited.
Benchmark: one published implementation documented a peak requirement of 800 slides per day, with 120 slides per hour during peak periods (Stathonikos et al., as cited in Schwen et al., 2023). Peak planning, not average planning, is the correct framework for capacity assessment.
Pathology Rescan events are consuming measurable staff time every shift
Every slide requiring a rescan due to image quality failure represents a staff intervention: identification, retrieval, parameter adjustment, requeuing. At a 5% rescan rate across 400 slides per day, that translates to 20 interventions each day. Every intervention consumes staff time and extends the case completion cycle. The Kuhlman et al. (2025) comparative analysis of all six FDA-cleared WSI platforms identified substantial variation in rescan rates across systems. Rescan rate is not a fixed cost of digital pathology. It is a scanner selection variable.
If your team is spending structured time every shift managing rescan queues, the rescan architecture of your current scanner is a throughput tax that a different system could eliminate.
Case backlogs are accumulating during peak periods and not clearing between shifts
A backlog that clears overnight is a capacity issue. A backlog that persists across multiple shifts and requires scheduled overtime to address is a structural capacity failure. Published operational data from pathology digitization projects documents peak scanning requirements substantially above daily averages, with implementations specifically designed to accommodate 800+ slide days within their normal scan team staffing (Schwen et al., 2023). If your current scanner cannot absorb a peak day without a backlog that disrupts the following day’s schedule, it is not sized for your actual demand profile.
The diagnostic consequence: delayed cases extend TAT, and extended TAT in oncology pathology has documented patient outcome implications (Walsh & Orsi, 2024).
Your scan team FTE requirements are growing faster than your slide volume
In a well-designed digital workflow supported by effective lab workflow management, the FTE required to manage QC and scan operations scales sub-linearly with volume: automation handles an increasing share of quality management as volume grows. If your scan team headcount is growing proportionally to your slide volume, or even faster than your slide volume, your QC automation may not be performing at the level needed for efficient scale. Published data shows that 1 FTE per 3–4 WSI scanners is the benchmark for image QC in a well-automated implementation (Ardon et al., 2023, as cited in Schwen et al., 2023). Systems requiring higher QC staffing ratios are absorbing a workforce cost that automation can carry.
You cannot accommodate a new tissue type, subspecialty, or AI tool within your current scan configuration
The most forward-looking sign is not a current failure. It is a future capability gap. Digital pathology is the enabling infrastructure for computational pathology and AI-augmented diagnostics. A 2024 systematic review and meta-analysis reported high pooled diagnostic accuracy for AI tools across multiple cancer types (mean sensitivity 96.3%, specificity 93.3%), while cautioning that 99% of included studies had at least one area at high or unclear risk of bias, so performance depends heavily on the quality and standardization of the underlying scan data (McGenity et al., 2024). If your current scanner cannot reliably produce images at the quality tier required for the AI tools you intend to deploy, or cannot handle the tissue types your expanding subspecialty scope requires, the constraint is not your AI strategy. It is your scanning infrastructure.
EMPAIA ISB 2025 quality awards at both 20x and 40x are an independent benchmark for determining which cleared platforms meet the image quality threshold that AI tool validation requires.
What the Slide Scanner Evaluation Should Actually Look Like
Recognizing the signs is the first step. Structuring the evaluation correctly is the second. A lab manager who has identified one or more of the five signs above is at the point in the buying cycle where a structured scanner evaluation, rather than an informal vendor conversation, will produce a defensible, evidence-based decision.
The published framework for WSI scanner evaluation in clinical pathology is well-established: FDA 510(k) clearance for primary diagnosis is the non-negotiable first filter; independent image quality benchmarking via EMPAIA ISB provides a second tier; peer-reviewed comparative concordance data (Kuhlman et al., 2025) provides the clinical performance comparison; and a CAP-compliant local validation study (Evans et al., 2022) provides the institutional confirmation. A lab manager who follows this sequence should arrive at a selection decision that is defensible to accreditors, administrators, and clinical colleagues.
At 1,500 slides per day, with automated QC that requires no manual intervention for focus failures and a safety container that prevents a single suboptimal slide from stopping the run, the E1000 Dx is designed to address that constraint.
Conclusion: The Right Time to Evaluate A New Slide Scanner Is Before You Need To
Slide scanner capacity problems have a reliable trajectory: they are manageable until they are not, and the point at which they stop being manageable typically coincides with a demand peak, a system event, or an accreditation cycle that removes the option of a deliberate evaluation. The lab managers who navigate this successfully are those who recognize the signs early, at Sign 1 or Sign 2 rather than Sign 5, and begin a structured evaluation while they still have time to do it properly.
The five signs described here are not predictions. They are observations from published operational data documenting the actual progression of capacity constraints in clinical pathology laboratories. As part of an effective capacity planning strategy, laboratories should assess these indicators regularly and take action before growth, staffing pressures, or new diagnostic requirements create operational bottlenecks. If your laboratory is living more than two of them today, the evaluation is overdue. If you are living one, it is timely.
Laboratory Capacity Planning: Key Takeaways for High-Throughput Labs
References
- L’Imperio, V., Dei Tos, A. P., Carbone, M., Malapelle, U., Bellevicine, C., Troncone, G., Pisapia, P., Marletta, S., Brunelli, M., Scarpa, A., Grassi, S., Cazzaniga, G., Pagni, F., & Eccher, A. (2026). Pathology in motion: Automation from specimen to report. Critical Reviews in Oncology/Hematology, 222, 105263. Read more.
- Schwen, L. O., Kiehl, T. R., Carvalho, R., Zerbe, N., & Homeyer, A. (2023). Digitization of pathology labs: A review of lessons learned. Laboratory Investigation, 103(11). Read more.
- Kuhlman, B., & Bedi, R. (2025). Comparative analysis of FDA-cleared whole slide imaging systems for primary diagnosis. International Journal of Pathology and Clinical Research, 11(1), 165. Read more.
- Ardon, O., Klein, E., Manzo, A., Corsale, L., England, C., Mazzella, A., Geneslaw, L., Philip, J., Ntiamoah, P., Wright, J., Sirintrapun, S. J., Lin, O., Elenitoba-Johnson, K., Reuter, V. E., Hameed, M. R., & Hanna, M. G. (2023). Digital pathology operations at a tertiary cancer center: Infrastructure requirements and operational cost. Journal of Pathology Informatics, 14, 100318. Read more.
- US Food and Drug Administration. (2025). 510(k) premarket notification K241717: Epredia E1000 Dx Digital Pathology Solution. FDA CDRH. Read more.
- Walsh, E., & Orsi, N. (2024). The current troubled state of the global pathology workforce: A concise review. Diagnostic Pathology, 19, 63. Read more.
- McGenity, C., Clarke, E., Jennings, C., Matthews, G., Cartlidge, C., Freduah-Agyemang, H., Randell, R., Treanor, D., & Bankhead, P. (2024). AI in pathology: A systematic review of clinical performance across cancer types. Lancet Digital Health, 6(8), e545–e555. Read more.
- Evans, A. J., Brown, R. W., Bui, M. M., Chlipala, E. A., Lacchetti, C., Milner, D. A., Pantanowitz, L., Parwani, A. V., Reid, K., Riben, M. W., Reuter, V. E., Stephens, L., Stewart, L., & Thomas, N. E. (2022). Validating whole slide imaging systems for diagnostic purposes in pathology: Guideline update from the College of American Pathologists. Archives of Pathology & Laboratory Medicine, 146(4), 440–450. Read more.