The digital pathology business case that gets approved is not the one with the most ambitious benefit projections. It is the one whose cost savings are conservative, independently sourced, and directly traceable to your lab’s operating model.

Capital requests for digital pathology face a consistent challenge in hospital finance committees: the technology is credible, the operational case is understood, but the numbers presented are hard to verify. Benefit projections drawn from vendor materials are discounted as optimistic. Cost savings built on a single institution’s published experience are questioned as non-representative. The business case that survives scrutiny is the one that arrives at the finance committee with independently published data behind every line, conservative assumptions stated explicitly, and a clear total cost of ownership model that shows when the
investment pays back, not just whether it pays back.

This article provides a structural framework for developing a digital pathology business case, grounded in peerreviewed operational data. It is not a financial model. Your finance team will build that. It is the evidence base that helps make every line of the model defensible.

The Two Questions Every Finance Committee Asks

Before building the business case, it is worth understanding what many hospital finance committees look for. Two questions occur in many capital reviews for laboratory technology:

1. First: what does it actually cost, in total, over the expected asset life? Not the capital line, but the full total cost of ownership including ongoing staffing, IT infrastructure, service contracts, and storage. Finance committees have been misled by low sticker prices on technology that carries heavy operational costs. A business case that presents only the capital line will be returned for a TCO remodel.
2. Second: what do we save, specifically, and when do we break even? Not "we will save staff time," but "we will redeploy 2.3 FTE currently consumed by slide logistics to diagnostic support roles, generating approximately $X in equivalent labor cost, in year one.’ Specificity is credibility. Vagueness is a red flag.

The published operational data to answer both questions precisely exists. The task is to apply it correctly.

The Total Cost of Ownership Model: Six Cost Categories

A published detailed cost analysis for large-scale digital pathology operations identified six major cost categories that should all be included in a TCO model (Ardon et al., 2023; Schwen et al., 2023). A business case that omits any of these will likely be identified as incomplete:

Sources: Ardon et al. (2023); Schwen et al. (2023); Hanna et al. (2019). All figures are approximate. Institutional costs will vary.

The Savings Case: Four Lines That Finance Committees Accept

The strongest business cases present savings in four distinct categories, each with a published evidence source and a clear methodology for converting published benchmarks into institution-specific figures:

1. Logistics FTE redeployment

This is one of the most measurable digital pathology benefits, directly contributing to improved resource allocation and lab efficiency.

• Published benchmark: 5 minutes per case saved in laboratory logistics (Schwen et al., 2023)
• At 400 cases/day, this is 2,000 staff-minutes per day = 2.3 FTE equivalents continuously occupied by logistics tasks that digital workflow eliminates
• Apply your institution's loaded staff cost per FTE to calculate potential annual saving
• Frame this as redeployment to diagnostic support rather than redundancy. That positioning is often more acceptable operationally.

2. Archival retrieval and storage cost elimination

• Published benchmark: 93–97% reduction in glass slide archival requests (Hanna et al., 2019)
• Identify your current annual off-site storage vendor spend, courier costs, and slide file room physical occupancy cost
• Model a 90% reduction as a conservative assumption. This remains defensible against the published data.
• Physical storage space freed is a real estate cost recovery that finance committees value

3. QC staffing optimization

• Published benchmark: 1 FTE per 3–4 WSI scanners for image QC in a well-automated operation (Schwen et al., 2023; Ardon et al., 2023)
• Compare this to your current QC overhead: manual QC of glass slides, rescan management, quality exception handling
• A scanner with automated focal map rescan can reduce QC FTE requirement further by eliminating focus failure interventions
• Calculate the difference in QC staffing costs between current and projected digital workflow

4. TAT improvement and downstream revenue protection

• Published data: 13.4% reduction in pathologist workday for non-diagnostic tasks through digital workflow (Stratman et al. time-and-motion study, reported in Hanna et al., 2019)
• Faster TAT in oncology pathology has documented clinical pathway implications. Quantify the benefit in terms of case throughput capacity rather than time alone
• AI-augmented diagnostic tools, available only on digital platforms, have demonstrated meaningful accuracy improvements in high-volume screening tasks (McGenity et al., 2024)
• Frame AI readiness as a strategic capability investment rather than a current cost saving. This helps address the "future-proofing" questions often raised by CMOs and Medical Directors

What Not to Include and Why

Two categories of benefit regularly appear in digital pathology business cases and are regularly challenged by finance committees. Exclude them or handle them carefully.

Diagnostic error reduction as a cost saving. The clinical case for diagnostic accuracy improvement is strong and independently evidenced. Quantifying reduced diagnostic error in financial terms, such as avoided malpractice costs, reduced re-treatment, or lower downstream healthcare utilization, is extremely difficult to calculate accurately with institutional precision and will be challenged as speculative. Include the clinical accuracy data in the CMO/Medical Director section of the business case as a quality argument, not in the financial model.

AI tool revenue projections. Reimbursement for AI-augmented digital pathology remains inconsistent and jurisdiction-specific (Zhang et al., 2024). Including AI revenue projections in a near-term business case carries a high risk of challenge. Include AI readiness as a strategic option value: the ability to deploy validated AI tools once reimbursement and regulatory pathways are established. Avoid presenting it as a near-term cash flow line in the financial model.

Conclusion: Build the Case Before the Budget Cycle Opens

Hospital capital budget cycles have fixed submission windows. A business case assembled in the week before submission is a weaker document than one built over two to three months with institutional cost data mapped against published benchmarks. The lab manager who begins building the TCO model in the current fiscal period, using the framework above and populating it with actual labor, storage, and IT costs, arrives at the budget window with a document that is ready to withstand finance committee scrutiny.

The published evidence base for the digital pathology business case is comprehensive, specific, and independently verified. The work lies in the application: mapping published benchmarks to your institution’s operating model, making assumptions conservative and explicit, and presenting savings in the language finance committees use, including FTE cost, storage cost, break-even year, and return across the asset life.

Key Takeaways: Building a Defensible Digital Pathology Business Case

References

1. 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.
2. 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), 100244. Read more.
3. Hanna, M. G., Reuter, V. E., Samboy, J., England, C., Corsale, L., Fine, S. W., Klimstra, D. S., & Travis, W. D. (2019). Implementation of digital pathology offers clinical and operational increase in efficiency and cost savings. Archives of Pathology & Laboratory Medicine, 143(12), 1545–1555. Read more.
4. McGenity, C., Clarke, E. L., Jennings, C., Matthews, G., Cartlidge, C., Freduah-Agyemang, H., Stocken, D. D., & Treanor, D. (2024). Artificial intelligence in digital pathology: A systematic review and metaanalysis of diagnostic test accuracy. npj Digital Medicine, 7, 114. Read more.
5. Zhang, D. Y., Venkat, A., Khasawneh, H., Sali, R., Zhang, V., & Pei, Z. (2024). Implementation of digital pathology and artificial intelligence in routine pathology practice. Laboratory Investigation, 104(10), 102111. Read more.
6. Walsh, E., & Orsi, N. (2024). The current troubled state of the global pathology workforce: A concise review. Diagnostic Pathology, 19, 63. Read more.
7. Munari, E., Zamboni, G., Cecchini, M. J., Zamo, A., Sommaggio, M., & Brunelli, M. (2024). Pathology in motion: Automation from specimen to report. Annals of Diagnostic Pathology. Read more.
8. US Food and Drug Administration. (2025). 510(k) premarket notification K241717: Epredia E1000 Dx Digital Pathology Solution. FDA CDRH. Read more.