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A Best Practices Guide for Large-Scale Laboratory Information System Implementations
March 3, 2026
Implementing a laboratory information system (LIS) software across a large, complex organization is never just a technology project. It is an operational transformation that touches nearly every role, workflow, and downstream dependency in the laboratory enterprise. For many organizations, the fear surrounding these initiatives is not unfounded. Enterprise LIS system implementations carry real risk: operational disruption, timeline overruns, staff fatigue, and cost escalation, often driven not by the software itself, but by underestimated effort and misaligned expectations.
Reducing Risk, Aligning Expectations, and Preparing for Success at Enterprise Scale
Based on LigoLab’s previous experience supporting large and highly complex lab information system implementations, a clear pattern emerges: most implementation challenges do not stem from weak technology or bad intent. Instead, they arise from gaps in planning, resourcing, sequencing, and ownership. The encouraging reality is that nearly all of these risks are preventable when they are surfaced early and addressed deliberately.
This guide is designed to help large organizations understand the true cost and operational drivers beyond licensing, acknowledge the realities that often get underestimated, and prepare their teams to execute implementations with clarity, confidence, and control.
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Why Large LIS System Implementations Feel So Risky
Enterprise laboratories operate at a level of complexity that smaller implementations rarely encounter. Multiple facilities, heterogeneous workflows, diverse client requirements, regulatory constraints, and deeply embedded legacy processes all converge. At the same time, these organizations are expected to maintain service levels, turnaround times, and revenue continuity throughout the transition.
The fear often comes from unanswered questions:
- How much internal effort will this really take?
- Who owns configuration decisions (and when)?
- How disruptive will this be to day-to-day operations?
- What happens when we discover workflows we forgot to mention?
- How do we avoid recreating our limitations in a new platform?
Addressing these fears requires reframing the implementation not as a one-time event, but as a sequenced transformation with shared ownership and realistic expectations.
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The True Cost of an LIS System Goes Far Beyond Licensing
Licensing is the most visible line item, and often the least risky. The real drivers of cost, effort, and timeline sit elsewhere:
- Internal resource allocation
- Workflow analysis and redesign
- Interface development and validation
- Data governance and ownership decisions
- Change management and adoption
- Phased execution and post-go-live optimization
Organizations that plan for these realities early tend to move faster, with less friction, and achieve stronger outcomes.
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1. Resource Allocation: The Most Underestimated Risk
One of the most common assumptions in large implementations is that once the platform is delivered, configuration and optimization will naturally follow. In practice, this is rarely true.
While all laboratory information system vendors provide training, best practices, and implementation guidance, the detailed configuration of the platform must reflect your organization’s specific workflows, client requirements, and operational edge cases. That knowledge lives within your teams, not in the LIS software.
Best Practices
- Assign named resources early, with protected time.
- Ensure representation from operations, lab billing, IT, compliance, and leadership.
- Engage subject-matter experts who understand not just “how things work,” but why they evolved that way.
- Treat configuration as real work, not a side task layered on top of existing responsibilities.
Across LIS lab implementations of this scale, having the right people engaged at the right time is one of the strongest predictors of success.
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2. Avoid Recreating Legacy-Constrained Workflows
Another frequent pitfall is the push to replicate workflows that evolved due to limitations of prior LIS systems rather than optimal operational design.
During gap analysis, teams often request configurations that replicate the legacy environment because they feel familiar or “safe.” This is a common and avoidable misstep. A strong LIS partner will challenge these requests and recommend best-practice alternatives; for example, LigoLab actively guides customers toward more optimal designs, while ensuring the final configuration decisions always remain with the customer.
What we consistently observe is this pattern:
- Legacy workflows are recreated initially
- Teams experience the new platform’s capabilities
- Those workflows are later redesigned to align with best practices
- The project absorbs additional cost, time, and rework
Best Practices
- Explicitly distinguish between “this is how we do it” and “this is how we want to do it.”
- Document tradeoffs when choosing familiarity over optimization.
- Encourage teams to pilot redesigned workflows early.
- Treat the implementation as an opportunity to simplify, not preserve complexity.
Calling out these decisions early helps avoid taking the same bite twice.
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3. Late Discovery of Workflow Nuance
Every large laboratory has hidden nuances: client-specific rules, historical exceptions, manual workarounds, and downstream dependencies that only surface once users are hands-on.
When these nuances emerge late:
- Systems are configured generically first
- Retrofits become necessary
- Costs rise, and timelines stretch
Best Practices
- Engage deeply with the platform early, not just in theory, but in practice.
- Use real cases, real clients, and real exceptions during discovery.
- Encourage teams to surface “the weird stuff” upfront.
- Accept that perfection isn’t possible, but early visibility dramatically reduces risk.
Early engagement keeps the elephant manageable.
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4. Clarifying Ownership: Who Owns What, and When
Ownership ambiguity is one of the quietest causes of delay.
The LIS company owns:
- The platform
- Best-practice guidance
- Training and enablement
The laboratory owns:
- Operational decisions
- Data governance
- Final configuration choices
- Workflow acceptance
When this line is not explicit, work can stall without escalation as each side assumes ownership lies elsewhere.
Best Practices
- Define ownership clearly during project kickoff.
- Assign decision-makers, not just contributors.
- Establish escalation paths for stalled decisions.
- Reinforce that configuration decisions are business decisions, not technical ones.
Clear ownership keeps the project moving in one direction.
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5. Don’t Overload Phase One
Large organizations often feel pressure to solve every historical problem before go-live. While well-intentioned, this approach frequently slows implementation and delays adoption.
The strongest outcomes come from recognizing that the laboratory information system is designed for iterative improvement.
Best Practices
- Stabilize core workflows first.
- Prioritize go-live readiness over perfection.
- Build governance and muscle for continuous optimization.
- Treat post-go-live as a planned phase, not an afterthought.
Smaller, intentional bites go down far more smoothly.
6. Integration and Infrastructure: Interfaces Take Time
Interfaces are routinely underestimated, even when they appear technically straightforward.
Each interface requires:
- Coordination across lab vendors
- Data ownership clarity
- Validation cycles
- Exception handling
- Ongoing monitoring
Delaying interface work compresses timelines later and creates downstream pressure.
Best Practices
- Start interface work immediately.
- Prioritize high-impact interfaces first.
- Use an interface engine when possible.
- Align interface timelines with operational milestones.
Early interface momentum reduces late-stage risk.
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7. Development Requests and Scope Management
At enterprise scale, new development requests almost always surface mid-implementation as teams see new possibilities.
This is expected, but an unmanaged scope can derail timelines.
Best Practices
- Size and prioritize development requests explicitly.
- Separate “must-have for go-live” from “valuable next phase.”
- Sequence development against the broader roadmap.
- Communicate tradeoffs transparently.
Not every request belongs in the first bite.
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8. Collaborative Project Management and Transparency
Complexity demands visibility. Ad-hoc spreadsheets and offline tracking fragment priorities and obscure dependencies.
Advanced LIS system implementations use:
- Notion as the primary project management system
- A shared Google Sheet to track all outstanding items and status
This creates a single source of truth and supports joint ownership.
Best Practices
- Centralize tracking and documentation.
- Make progress, blockers, and dependencies visible to all.
- Treat project management as a shared responsibility.
- Update continuously, not episodically.
Transparency scales better than heroics.
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Questions Every Laboratory Should Ask Before a Large-Scale LIS Implementation
Large laboratory information system implementations succeed or fail based on the questions asked early and whether organizations are prepared to act on the answers. The following questions (and answers) reflect real-world lessons from enterprise-scale implementations and address the areas that are most often underestimated.
Resourcing and Time Commitment
What daily availability is required from our internal team?
Plan for 1–3 hours per day from the primary project manager (PM) and/or LIS administrator once discovery and configuration begin. This is not optional time; it is core project work. If subject-matter experts (SMEs) are needed for specific workflows (e.g., cytology, microbiology, histology), their availability should be scheduled proactively. Resource constraints at this stage routinely extend discovery and configuration by months.
Is this time commitment for LIS administrators or laboratorians?
Initially, this time falls on the PM/LIS administrator, provided they have deep operational knowledge. If not, department managers and workflow owners must be engaged, especially during discovery. Availability requirements shift by phase, but discovery places the heaviest demand on those who truly understand how work gets done.
Best Practices:
Whenever possible, assign the same individual as both project manager and LIS administrator to streamline decision-making and reduce friction.
Phasing and the First “Bite” of Scope
What should our first, most meaningful project slice be?
Start with a workflow-centric approach, not a department-by-department or system-by-system migration. General cytology and related ancillary testing often provide a strong starting point, building momentum while limiting complexity. Avoid splitting a single workflow across multiple platforms.
Which existing systems can be deferred or replaced later?
Non-core tools, such as charge review or provider portals, can often be phased later if workflows remain temporarily on a legacy LIS system. Systems tightly coupled to anatomic pathology workflows should move together. In many cases, it is more effective to retire legacy middleware rather than rehost it.
Best Practices:
Begin parallel workstreams immediately:
- Data export/import
- Interfaces
- Instrument connections
- Configuration and validation
- Targeted enhancements
Go-Live Strategy
Is a “big bang” or phased go-live recommended?
A full big-bang go-live carries significant risk at enterprise scale. Phased go-lives, by workflow, client, or health system, allow teams to validate end-to-end performance with a manageable scope. Running systems concurrently is possible, but it increases training and cognitive load.
Can we mix systems during the transition?
In limited cases, departments can function as “reference labs” during transition (e.g., routing specific tests to the new LIS). However, avoid splitting a single workflow whenever possible, as this introduces confusion and rework.
Best Practices:
Align reporting models early. Moving toward separate tests and reports by specialty (rather than layered addenda) often improves downstream electronic health record (EHR) clarity and aligns better with modern LIS system architectures.
Change Management and Workflow Design
Should we replicate current workflows or redesign them?
This decision must be explicit. Replicating workflows reduces short-term disruption but limits long-term gains. Adopting optimized, LIS-native workflows requires more change upfront but delivers better scalability, efficiency, and ROI.
What resistance should we expect?
Pushback is normal, particularly when long-standing exceptions are challenged. Unresolved requests for custom behavior often create delays. Standardization, especially in high-volume environments, reduces downstream friction.
Best Practices:
Decide early where optimization is required, and document trade-offs when choosing familiarity over improvement.
On-Site Support and Cutover Week
What happens on-site during go-live?
Before go-live, teams complete refresher training, final validation, and configuration adjustments. During go-live week, an on-site triage team tracks specimens end-to-end, resolves issues in real time, and coordinates with remote support. Expect rapid production updates if needed.
How should we prepare internally?
Prioritize major locations for on-site coverage, align department leadership, and plan for backfill or temporary staffing so SMEs can focus on go-live support.
Budgeting and Executive Expectations
What should we brief the CFO and board on now?
Budget not just for licensing, but for:
- Internal PM and SME time
- On-site go-live support
- Temporary staffing or backfill
- Parallel execution tracks
Executives should understand that data migration, interfaces, instruments, configuration, and enhancements run concurrently, not sequentially.
What decisions need to happen early?
Define your phasing strategy (workflow, client, or hybrid), inventory internal applications for replacement or enhancement, and begin planning data exports, interfaces, and instrument connections as early as possible.
Why These Questions Matter
Organizations that ask and answer these questions early replace fear with clarity. They align leadership expectations, protect timelines, and reduce rework.
As with all enterprise initiatives, success is less about the software itself and more about preparation, ownership, and sequencing. Asking the right questions is the first step toward getting the outcomes you expect.
Preparing Your Organization for Success
Large laboratory information system implementations succeed when organizations:
- Align expectations early
- Resource deliberately
- Redesign workflows thoughtfully
- Clarify ownership
- Sequence work intelligently
- Embrace iteration over perfection
Fear often comes from uncertainty. Transparency, about effort, ownership, and sequencing, is the antidote.
With deliberate planning and a shared understanding of what truly drives cost and complexity, enterprise LIS system implementations can move from being high-risk events to controlled, confidence-building transformations that position laboratories for long-term success.
Ready to Modernize with Confidence?
Partner with LigoLab to navigate the complexities of large-scale LIS transitions. Our team brings proven enterprise implementation expertise, best-practice guidance, and hands-on support to help your organization move to a modern LIS system with confidence. Contact LigoLab today to start the conversation.
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