Blog
Best Practices for Preparing Medical Labs for AI Integration in Technical and Financial Operations
June 10, 2025
As artificial intelligence (AI) continues to transform the healthcare industry, independent medical laboratories are uniquely positioned to benefit from its powerful capabilities. From predictive analytics and automated laboratory workflow management supported by modern laboratory information system (LIS) software to more accurate lab billing and optimized resource allocation, AI has the potential to streamline both technical and financial operations across clinical, pathology, and molecular laboratories. However, successful AI integration requires deliberate planning, stakeholder alignment, and robust infrastructure.
This blog outlines best practices medical labs can follow to prepare for AI integration, while highlighting real-world examples and the steps necessary to achieve a seamless and secure transformation.
Learn More: The Future of Medical Labs - Embracing Tech & Personalization
1. Establish Clear AI Objectives Aligned with Lab Goals
Before exploring AI tools, labs must first define what they want to achieve. Goals should align with both technical operations (such as improving turnaround time and reducing errors in specimen processing) and financial operations (such as increasing the clean claim rate and reducing write-offs).
Example: A regional reference lab using AI as a solution for automating the review of quality control (QC) data and reducing manual intervention, and applying machine learning to the laboratory billing process to predict claim denials and prioritize follow-ups accordingly.
Action Steps:
- Form a multidisciplinary steering committee (IT, lab management, compliance, finance).
- Prioritize use cases with measurable impact.
- Define success metrics (such as a 25 percent reduction in manual QC checks or a 10 percent increase in first-pass claim acceptance).
On aligning AI with lab performance goals:
“AI only delivers real value when it aligns with a lab’s financial and operational goals. That’s why our approach to AI integration with LIS systems starts with strategy, not technology.”
— Suren Avunjian, LigoLab CEO
2. Ensure Your LIS System & Laboratory Billing Solutions Can Support AI Tools
Outdated laboratory information systems and disconnected lab revenue cycle management (lab RCM) software often lack the flexibility needed to support modern AI tools. To fully harness the benefits of AI, labs need a unified, API-enabled platform designed for seamless integration and intelligent automation.
Example: LigoLab’s integrated and future-ready informatics platform features rule-based automation and open APIs, allowing AI tools to access relevant lab data and workflows without disrupting system performance.
Action Steps:
- Assess the current LIS system and lab RCM readiness for AI (API access, data normalization, cloud compatibility).
- Upgrade or migrate to a modern LIS lab platform and an advanced billing software for labs that supports AI plug-ins.
On replacing legacy systems to unlock AI potential:
“Legacy LIS software solutions and outdated lab billing software simply weren’t built for this era. To truly unlock the power of AI, labs need platforms that are open, interoperable, and designed with intelligent automation at their core.”
— Suren Avunjian, LigoLab CEO
White Paper: The Connected Laboratory: Leveraging LIS & RCM to Grow Your Business
3. Prepare Your Data for Machine Learning Models
AI thrives on clean, structured, and well-labeled data. Labs must evaluate the quality and accessibility of their data in areas like test results, CPT/ICD-10 coding, payer responses, and instrument logs.
Example: A large pathology group utilizing AI to predict supply usage from several years of historical specimen volume, reagent use, and test type data, formatted consistently and free of gaps.
Action Steps:
- Audit current data sources for completeness and consistency.
- Standardize data formats (HL7, FHIR, USCDI standards).
- Implement a governance framework to monitor data quality and accessibility.
Industry Insights: Laboratory Information Systems and Their Key Role in Lab Data Analytics
4. Invest in Infrastructure and Security
AI tools can be resource-intensive, requiring real-time processing or large-scale historical analysis. Labs must ensure their infrastructure, including servers, cloud services, and cybersecurity policies, can support these demands.
Example: A clinical lab installing dedicated GPU servers to detect anomalies in real-time specimen processing and upgrading its security protocols to meet HIPAA and other regulatory standards.
Action Steps:
- Conduct a technical infrastructure audit (storage, processing, redundancy).
- Upgrade to cloud or hybrid infrastructure if needed.
- Ensure compliance with HIPAA and other relevant data privacy standards.
On risk reduction through AI-enhanced compliance:
“Labs face increasing regulatory pressure, and AI can help them stay one step ahead. By embedding compliance directly into workflows, we’re turning what used to be vulnerabilities into strengths.”
— Suren Avunjian, LigoLab CEO
Industry Insights: Regulators Are Rewriting HIPAA - 2025 Survival Guide for Clinical & Pathology Labs
5. Develop a Change Management Plan
Introducing AI affects workflows, roles, and decision-making processes. Clear communication and a phased rollout minimize disruption and foster adoption.
Example: A dermatopathology group piloting an AI tool to pre-screen slide images by first training a small group of users, gathering feedback, and then expanding access in stages based on performance benchmarks.
Action Steps:
- Create training materials and host AI readiness workshops.
- Identify AI champions within the lab to lead adoption.
- Phase implementation by starting with low-risk, high-reward use cases.
Industry Insights: Digital Pathology Redefined - Uniting AI, Viewers, and a Robust LIS System for a Seamless Workflow
6. Establish Validation and Monitoring Protocols
AI must be validated like any other lab tool. This includes ensuring accuracy, reliability, and compliance across different patient populations, instruments, and lab sites.
Example: A high-volume clinical lab testing its AI-based claim scrubber across multiple laboratory billing scenarios and payer rules before allowing it to auto-submit claims.
Action Steps:
- Establish a training environment for validation of AI tools.
- Run parallel comparisons with the current clinical lab workflow.
- Document performance, accuracy, and any discrepancies.
Case Study: In-House vs. Outsourced Laboratory Billing – Navigating the Best Path Forward for Your Lab
7. Address Ethical and Regulatory Considerations
Labs using AI must be transparent about how decisions are made, especially in diagnostic or lab billing contexts. Regulatory agencies are beginning to enforce AI-related disclosures and compliance.
Example: A genetics lab using AI-based coding assistance supported by audit trails, user override options, and explanations for each coding recommendation.
Action Steps:
- Ensure explainability for all AI-generated outputs.
- Document patient data usage and consent.
- Monitor emerging regulations from the FDA and CMS.
Learn More: Supporting Innovation - How LigoLab Empowers Labs to Develop and Validate Their LDTs
8. Partner Strategically with AI Vendors and Consultants
Rather than building AI tools from scratch, labs should partner with trusted laboratory information system companies, AI developers, or third-party consultants who understand the specific needs of laboratory environments.
Example: A molecular diagnostics lab collaborating with its LIS company and an AI startup specializing in natural language processing to extract key data from scanned requisition forms, boosting order entry efficiency.
Action Steps:
- Vet lab vendors for healthcare expertise.
- Ensure alignment on data security, support, and service level agreements (SLAs).
- Request case studies or peer references to verify past performance.
On building trust and transparency in AI implementation:
“Transparency is critical when implementing AI. Labs need to understand how decisions are made and be able to trace every outcome. We’ve built that accountability directly into our medical LIS and lab billing platform.”
— Suren Avunjian, LigoLab CEO
Industry Insights: Bridging the Gap in Modern Laboratories - Why a Comprehensive Digital Platform Outperforms a Traditional Lab Information System
9. Continuously Evaluate AI’s Impact on Lab Operations
After AI tools go live, labs must track outcomes against predefined key performance indicators and gather user feedback to refine lab workflows.
Example: A multisite pathology practice implementing monthly reviews to assess AI-driven report generation accuracy, tech adoption, and turnaround times.
Action Steps:
- Build dashboards to monitor the impact (cost savings, TAT improvements, etc.).
- Survey users regularly for feedback.
- Adjust AI parameters and retrain models as needed.
On the transformative power of LIS-AI integration:
“When you combine AI with a modern lab information system platform, you’re not just automating tasks - you’re building a smarter lab that learns, adapts, and continuously improves. That’s the future we’re helping our partners realize.”
— Suren Avunjian, LigoLab CEO
Unlocking AI’s Full Potential: How Forward-Thinking Labs Can Prepare Today for Smarter Diagnostics Tomorrow
AI is not a magic wand - it’s a tool that, when thoughtfully implemented, can unlock powerful improvements in lab productivity, financial performance, and patient care. Medical labs that take the time to plan, prepare, and validate their AI strategy will be best positioned to thrive in the next era of diagnostic medicine.
With the right LIS system foundation, data readiness, stakeholder buy-in, and a commitment to continuous improvement, AI can be a transformative force, not just for laboratory operations but for the entire healthcare ecosystem.
Need help preparing your lab for AI integration?
Contact LigoLab’s product specialists today to learn how our unified medical LIS & lab RCM platform is already enabling intelligent automation across the diagnostic continuum.
