From personalized medicine to infectious disease surveillance, the clinical laboratory has remained as crucial as it ever was to healthcare delivery. Labs generate data that informs nearly 70% of all medical decisions. However, as we move through 2026, the supporting LIS (lab information system) technology is breaking under the immense pressure.
Because the weight is unbearable.
So, to kick off with solutions, rather than with bad news, here are the top ten technological challenges medical labs face – and don’t worry, we’ve got you covered with some best practices, solutions, and tips.
1. What Happens When Talent Shortage Meets the LIS Technology Transformation?
The numbers don’t lie – and they are alarming: medical labs require around 10,000 new professionals annually, but training programs produce only about 5,000 graduates each year. Yes, even with Elon Musk’s endless ranting about AI changing the workforce. The demand for medical laboratory professionals will grow to 13% through 2026. That’s almost double the average for all occupations. However, over 60% of current laboratory professionals are approaching retirement age.
So, how do we solve this staffing problem? Well, to begin with, we must understand that this isn’t just a staffing problem, but a technology adoption issue. Staff burnout is on a continuous rise, with medical labs resorting to overtime and inappropriate staff utilization simply to maintain basic operations. Decision-makers are finally recognizing what lab professionals have known for years: this crisis threatens the foundation of diagnostic medicine.
But one must hope that they also realize that next-gen LIS platforms can solve this crisis. Automation can eliminate redundant manual steps and allow existing staff to focus on higher-value work. Because if you can’t add headcount (and medical labs certainly can’t nowadays), you need LIS platforms that make every person more effective.
2. Interoperability: The LIS Technology Challenge That Won’t End
As you can probably imagine, interoperability has been healthcare’s promised land for over a decade. However, as of this moment, it remains more aspiration than reality. Medical laboratories continue to struggle with outdated HL7 interfaces, mismatched systems, and a tangled web of custom integrations that cost organizations 15-20% of their IT budgets to maintain.
Only 15% of health organizations have adopted FHIR APIs for routine operations, leaving the other 85% almost fully dependent on legacy standards. On the other hand, the average healthcare organization runs about 77 SaaS applications across multiple IaaS platforms. Each additional connection adds complexity and additional failure points.
And the consequences? Well, dire:
- Digital LIS pathology integration is consistently the hardest part of implementations.
- AI developers struggle to connect new tools to laboratory data streams.
- Clinical decision support systems can’t access timely results.
However, when LIS platforms follow industry standards (like FHIR) and provide well-documented APIs, integration turns from custom development projects to daily routine configuration tasks. It’s all about your LIS platform – is it ready to produce next-gen interoperability?
3. Is Personalized Medicine Still a Technology Paradox?
For some time now, personalized medicine promises to revolutionize healthcare by tailoring treatments to individual genetic profiles. However, it also creates massive operational complexity in laboratories. Just last year, more than 54,000 genetic tests related to over 11,000 conditions – and that’s only one simple example.
Next-generation sequencing, liquid biopsies, CRISPR-based diagnostics, and mass spectrometry. Any of them sound familiar? That’s because they are now daily realities in modern laboratories. Each platform introduces unique workflow requirements, data interpretation needs, and its own quality control demands. For example, molecular diagnostic labs not only deal with a wide variety of tissue sample types, but also numerous steps in material preparation, frequent implementation of technical platforms, and, to top it all, complex post-testing analysis methods.
The challenge here is basic: How can you manage the operational complexity they introduce? After all, various tumor samples require various order sets and multistep reflex testing. OR, try high test costs that demand their own specific sample prioritization. In many cases, the results are so complex that interfaces must handle so much more than simple positive/negative values. No, the platforms must showcase rich, structured data about mutations, variants, and clinical significance.
So, how can your lab live through this challenge? Well, it’s all about customization, and the LIS platform’s ability to provide an agile, ever-evolving dashboard system that works for you. And not vice versa. That is the only way medical laboratories can stay current with advancing medicine.
4. Cybersecurity: The Constant LIS Challenge
You know it, we know it. Heck, even the patients know it. But that doesn’t change the fact that the statistics don’t lie: One in three hospitals reports that cyber incidents directly impacted patient care. In 2024, healthcare data breaches affected approximately 70% of the U.S. population. The average cost of a data breach is expected to surpass $10 million by 2025. One specific cyber attack in the US caused a $2.87 billion loss, with more expected from settlements and lawsuits.
For laboratories, cybersecurity is a patient safety imperative first – and a severe IT concern after. Consider that more than 90% of hacked health records are stolen outside the EHR system, and 100% of hacked data is either unencrypted or accessed through stolen credentials. However, modern LIS platforms can (and must) incorporate security by design. Cloud-based systems should provide enterprise-grade security that would be cost-prohibitive for individual organizations to implement.
If your LIS does not offer such abilities, this year brings the wind of change.
5. What Happens When Regulatory Compliance Changes Faster Than Systems?
When digital pathology CPT codes were introduced, the major breakthrough was their ability to track and enable reimbursement for scanning services. However, it wasn’t long until laboratories began struggling to incorporate these “simple tracking codes” into their information systems, due to regulatory requirements. This is because laboratories must navigate an increasingly complex web of data protection regulations, quality control requirements, and reporting standards.
The cybersecurity clearance process has become so difficult and complex that it delays the implementation of new capabilities. On the rare occasion in which both the vendor and laboratory are ready to proceed, they often find themselves juggling between innovation and compliance.
And that creates an operational gridlock. Want to read more about how to achieve full compliance in your medical lab? Check out our extensive guide – right here.
6. When LIS Technology Undermines Care Coordination
Data fragmentation remains uncontrollable, no matter how much is invested into it. And we’re talking endless budgets poured into IT departments. A single healthcare facility will end up using several health IT systems from multiple vendors, each with its own database, software, and architecture. This cannot go on much further.
Studies show that incomplete or inconsistent data transmission between systems leads to errors, unnecessary repeat testing, and treatment delays. And this becomes even worse when you think of multi-site operations: multi-site laboratories face specific challenges when different facilities use different LIS platforms. It can be utter chaos – unconsolidated data, misstandardized reporting, and a lack of unified views of testing across locations become a jumbled integration project rather than a simple configuration task.
However, when combined with comprehensive interoperability capabilities, a single LIS platform can become the central nervous system for lab data across an entire health system. That is the only logical (and practical) solution to this challenge, which is far more common than the healthcare industry would care to admit.
7. Can We Close the AI Implementation Gap?
Artificial intelligence and machine learning are currently revolutionizing laboratory medicine – but only in theory. While 90% of healthcare organizations plan to incorporate AI tools by the end of this year, and while AI is promising to change automated image analysis and predictive diagnostics… it seems like deploying these capabilities in real-life production environments is more difficult than promised.
AI adoption relies on structured and standardized data, but laboratory data often lacks the standardization needed. If we talk about Interpretability being a major challenge on its own, it turns out that it’s even a bigger challenge when trying to implement AI and MOL tools. Complex AI models may lack transparency in their decision-making, making it difficult for healthcare professionals to trust their recommendations.
Additionally, Integration with existing laboratory workflows requires more than just technical solutions, but also rethinking processes. As it turns out, many medical labs simply lack the infrastructure necessary to support AI applications. So, what’s actually happening in healthcare (as in other industries) is that people talk about implementing AI into their lab workflows and turning their lab “AI-friendly”, when the day-to-day reality proves it can’t be done just yet.
The solution to this challenge is solely on the LIS platforms. Those with native support for AI integration will provide standardized data outputs, FHIR-compliant APIs, and built-in data quality management. Such platforms will soon dramatically simplify AI deployment, for the AI tools to focus on analysis rather than data wrangling. That’s when AI will start delivering on the promise, and the good news is that some LIS platforms are already doing it.
8. Is the Automation Adoption Technology Challenge a Paradox?
Total laboratory automation (TLA) promises to address workforce shortages, improve accuracy, and accelerate turnaround times. However, reality proves otherwise: automation systems can cost hundreds of thousands or millions of dollars, while implementation requires facility modifications, extensive staff training, and ongoing (sometimes, never-ending) maintenance. Organizations typically spend
What remains a challenge is that automated systems must connect not just to laboratory instruments but to LIS platforms, with each connection requiring custom development. But when you try establishing said connection with legacy LIS systems, the implementation timeline stretches, and costs escalate. How much of an escalation are we talking about here? Well, far beyond the 15-20% of IT budgets dedicated to maintaining custom interfaces between systems.
Get the full breakdown of lab TLA and what it actually means (and how to achieve it) right here.
9. Usability is The Productivity Killer
Here’s an uncomfortable truth: laboratory professionals don’t enjoy using their LIS. And here’s the proof, right here. And the sad part is that you already know that. Because when systems require excessive clicks and force users into convoluted workflows, every task takes longer.
Imagine anatomic pathology, where sign-out workflows can be complex. We’ve seen and heard so many pathologists who fully admit to avoiding signing out cases directly in their legacy LIS, preferring to work in separate digital pathology systems and then return to the LIS only for final reporting. Does that sound like a productive workflow to you?
We didn’t think so.
Every minute wasted on clunky interfaces and complex workflows is a minute not spent on patient care. And worse for your lab staff – it’s a minute of frustration. When laboratory professionals can perform tasks efficiently and intuitively, they’re more productive, make fewer errors, and experience less job-related stress. Thus, this challenge can only be solved with modern LIS platforms that are built to work for your staff, and not against them.
10. Scalability and Flexibility are Still an LIS Technology Challenge
But what seems to remain the most fundamental challenge facing laboratories, even nowadays, is the pace. It seems medical labs simply can’t keep up with the ever-growing needs of the world around them:
- New tests emerge constantly
- Regulations shift
- Monetary models evolve
- Practice patterns change
- Ownership consolidates
- Technology capabilities expand
- AI is a thing
- And by the time you read this article, something new will probably come up
Traditional LIS implementations struggle to keep pace. Labs often find themselves in a constant cycle of IT projects that require vendor involvement and extended timelines. This endless cycle is unsustainable. As health systems merge and medical laboratory networks grow, the LIS must support diverse operational models catering to multiple facility types.
At the end of the day, a platform optimized for a single hospital laboratory will not handle the pressure and requirements of a regional reference laboratory network. However, an agile LIS platform that was designed with flexible architecture, configurable workflows, extensible data models, and modern integration capabilities…? That platform will allow laboratories to adapt without custom development.
Now, labs must realize that this practical challenge is not going away anytime soon, and sometimes, the hardest step in solving a problem is simply admitting you have one. This is that moment, this year, for many labs worldwide. Is your lab one of them?
Bottom Line?
While these 10 challenges paint a daunting picture, each challenge is an opportunity. The laboratories that thrive in 2026 and beyond will be those that choose LIS technology platforms designed for this complex reality. Because the question isn’t whether medical labs can meet these challenges. No, the real question is, will they allow their LIS/LIMS platforms to enable their efforts to do so? It’s not a leap of faith – it’s the first step into optimizing your overall lab performance, on all fronts.
What do you say?
➡️ READY TO START WALKING AGAIN?
