Digital Pathology Insight Series
Planning a Digital Pathology Workflow Implementation: A Lab Manager’s Checklist for Minimizing Workflow Disruption
The labs that experience the smoothest digital pathology transitions do not succeed because the technology is simple. They succeed because they planned for the complexity before it became a problem and understood which risks were within their control to mitigate.
Implementation anxiety is the most underrated barrier in digital pathology adoption. Lab managers who have cleared the business case, secured the capital budget, and selected a platform still face the prospect of transitioning a live clinical service, with real patients, real accreditation obligations, and real staff, from a workflow they know to one they are learning. The disruption risk is real, and it is worth taking seriously. What it is not worth doing is letting that anxiety delay a transition that the operational evidence overwhelmingly supports.
Published operational experience from laboratories that have completed full-scale lab digitization provides a detailed map of where disruption actually comes from and, crucially, which planning decisions prevent it. The Schwen et al. (2023) comprehensive review of lessons learned across multiple digitized pathology laboratories identified a consistent set of implementation risk factors: inadequate parallel running periods, underestimated IT infrastructure requirements, insufficient staff training time, and QC workflow gaps in the transition from manual to automated quality management. Each of these is preventable with structured planning.
This checklist distils that evidence into a practical digital pathology workflow implementation planning tool organized across four phases. The ⚠️ items are the risk-carrying decisions. These are the choices where published experience shows the highest rate of preventable disruption.
| Phase 1 |
Infrastructure & Integration Assessment - 8–12 weeks before go-live |
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Audit current LIS/PACS integration architecture and document all data handoff points
Digital pathology workflows require LIS integration for case assembly and patient matching. Gaps identified here delay go-live; gaps identified after go-live cause clinical errors.
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Benchmark network bandwidth against peak scan data transfer requirements
Network bandwidth for local core infrastructure typically runs 100 Mbit/s to 10 Gbit/s in published implementations (Schwen et al., 2023). Undersized networks are the most common cause of post-go-live performance complaints.
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Confirm storage infrastructure sizing for peak daily data volume plus 5-year growth projection
A single WSI at 40x is approximately 2 GB. A 500-slide/day lab generates roughly 1 TB of raw data per day. Storage planning must accommodate not just current volume but the 5–10% annual volume growth documented across the sector (Munari et al., 2024).
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Establish redundant scanner capacity plan for downtime coverage
Published data specifies scanner uptime requirements of 99.9% for clinical operations (Schwen et al., 2023). Plan reserve capacity before go-live, not after the first downtime event.
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Confirm that scanner, software, and display together constitute the FDA-cleared end-to-end configuration for primary diagnosis
FDA 510(k) clearance applies to a specific system configuration. Using the cleared scanner with non-cleared software or a non-validated display creates a compliance gap. Document this before go-live for CAP inspection readiness.
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| Phase 2 |
Staff Training & Competency Planning - 6–8 weeks before go-live |
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Identify scan team leads and train to full operational competency before parallel running begins
Training dependencies are a primary source of go-live delays. One published implementation estimated 6 weeks of technician time plus 40 hours each of senior scientist and pathologist time for scanner evaluation alone (Schwen et al., 2023). This is separate from operational training requirements.
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Build documented standard operating procedures for all scanning workflows before staff training begins
Tacit knowledge transfer is the highest-risk training mode. Staff trained from undocumented workflows carry institutional knowledge that cannot be verified, audited, or replicated. Digital scanning protocols should be documented before, not during, training.
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Confirm pathologist training on digital sign-out navigation covers all diagnostic modalities in scope
Published studies consistently show a short adaptation period for pathologists new to digital sign-out, with reported comfort increasing substantially after the first 50–100 cases (Browning et al., 2023). Build this adaptation volume into the parallel running period, not the live clinical period.
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Establish a feedback mechanism for staff to report image quality or workflow concerns during parallel running
The MSK implementation documented an in-viewer QA feedback tool allowing reviewers to flag image quality, artifact, or network-performance issues directly to scan staff (Hanna et al., 2019). This closed-loop mechanism catches issues before they become systematic quality problems.
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| Phase 3 |
CAP Validation & QC Protocol Establishment - parallel running period |
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Design the CAP local validation study before parallel running begins, not after
The CAP 2022 guideline requires minimum 60 cases per intended application, two-week washout between modalities, and ≥95% intra-observer concordance (Evans et al., 2022). The study design must be agreed before the first validation case is read.
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Document automated QC rescan parameters and confirm they are active before validation cases are scanned
For the E1000 Dx: the automated focal map rescan, the first FDA-cleared autonomous QC rescan system, should be confirmed active and its parameters verified before any validation scanning begins. Post-validation QC parameter changes require revalidation.
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Establish a glass slide fallback protocol and communicate it to all clinical staff
Pathologist comfort with digital primary diagnosis was substantially higher when glass slides remained available on request than when they did not (54% vs. 23% in survey responses; Hanna et al., 2019). Establish and communicate a glass slide fallback protocol rather than improvising one when needed.
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Set TAT benchmarks for digital workflow and monitor against glass slide baseline from day one of parallel running
TAT is the primary metric by which clinical stakeholders will judge the transition. Establish the baseline before parallel running, set target metrics, and have monitoring in place from the first day of parallel operation.
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| Phase 4 |
Go-Live & Steady-State Operational Monitoring - first 90 days |
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Maintain daily scan team check-ins for the first four weeks post go-live
Published experience consistently identifies the first four to six weeks of live operation as the highest-risk period for process issues to surface. Daily brief check-ins create an early warning system that catches problems before they compound.
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Monitor rescan rate weekly and investigate any rate above 3% as a process signal
The published benchmark for rescan rate in mature digital implementations is substantially below 5%. A rescan rate above 3% in the first 90 days indicates a process, training, or equipment parameter issue that warrants structured root cause analysis before it becomes the new baseline.
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Conduct structured post-implementation review at 30 and 90 days and document findings for CAP inspection readiness
A documented post-implementation review is evidence of the quality management process that CAP inspectors assess during accreditation review. Building it into the project plan before go-live ensures it happens as a structured process rather than an informal conversation.
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Conclusion: Plan for the Complexity, Then Remove It
Digital pathology implementation disruption is not inevitable. It is a predictable consequence of specific planning gaps that published operational experience has identified clearly: network under-capacity, compressed training timelines, validation studies designed after parallel running begins, and QC protocols established reactively. The checklist above addresses each of these categories directly.
The labs in the published literature that completed full-scale digitization without significant clinical disruption did not do so because their implementations were simple. They did so because the complexity was mapped in advance and addressed systematically. That is what this checklist enables. It is the operational foundation on which a digital pathology transition that delivers its promised efficiency and quality gains is built.
Digtial Pathology Workflow Planning: Key Takeaways for Lab Digitization
References
- 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.
- 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.
- 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.
- Koefoed-Nielsen, H., Kidholm, K., Frederiksen, M. H., & Mikkelsen, M. L. N. (2024). Expectations and experiences among clinical staff regarding implementation of digital pathology: A qualitative study at two departments of pathology. Journal of Imaging Informatics in Medicine, 37(5), 2500–2512. 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.
- Browning, L., Winter, L., Cooper, R. A., Colling, R., Verrill, C., & Rittscher, J. (2023). Impact of the transition to digital pathology in a clinical setting on histopathologists in training. Journal of Clinical Pathology, 76(10), 712–718. Read more.
- 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.