Healthcare Software Modernization: How Legacy Systems Block AI Adoption

Modern healthcare organizations may retain their legacy systems just because they are familiar and convenient to use. But what worked well a decade ago may now be restraining your digital transformation and posing a serious risk to your operations, patients, and reputation.

Today, a significant number of healthcare institutions worldwide operate on outdated software infrastructure: mainframe EMRs, COBOL-based billing systems, on-premises lab systems, and fragmented databases. 74% of healthcare organizations experience issues with real-time data integration, while over 60% of U.S. hospitals still run at least one critical application on legacy software.

Obsolete programming languages and fragile infrastructures often fail to implement modern AI modules and standardized data formats. Furthermore, legacy environments may struggle to meet security and compliance requirements, such as HIPAA or GDPR. This results in slower clinical decisions, increased operational risks, and rising maintenance costs. 

We’ll explain how healthcare organizations can adapt legacy systems to enable AI adoption, illustrating the theory with a practical example of AI-infused modernization. 

Why Legacy Systems Resist AI Implementation

The real issue with AI in healthcare software modernization is not the AI’s complexity, but the system’s inherent gaps. The old platforms were designed long before AI had entered the scene. They often lack the architectural flexibility, integration opportunities, and data consistency to enable a smooth run of AI models.

Poor integration between old and modern systems

Traditional healthcare platforms usually operate as isolated silos. They can’t easily connect with cloud-based AI modules or share data across different departments. Integration gaps, such as differences between HL7 v2 and FHIR standards, reliance on custom APIs, and a lack of interoperability between EMR and LIS systems, exacerbate the compatibility issue.

Igor Omelianchuk notes: “You never have a blank page in healthcare; you always need to work with something that’s already there.”

The existing constraints force developers to establish AI components on outdated infrastructures, which leads to inefficiencies and compatibility risks.

Data quality issues in healthcare modernization

High-quality, standardized data is fuel for AI systems. However, old healthcare databases typically utilize outdated or incompatible formats, lack metadata, and have inconsistent record structures. Therefore, it’s hard to provide clean and usable data for the appropriate training of AI models. Instead, developers have to find workarounds rather than expecting ideal conditions. Handling imperfect data increases time and costs, slowing down healthcare IT modernization and AI adoption.

Hardware and software limitations

Many legacy platforms run on outdated hardware and have monolithic architectures that prevent horizontal scaling required for machine learning workloads and cannot support resource-consuming AI processes. Due to these limitations, healthcare modernization often requires the transformation of IT infrastructure, including servers, storage, and network elements, before AI implementation.

Compliance risks 

Aging systems may fail to meet frequently altering regulatory requirements in the healthcare sector. AI solutions, as well as the other software components, must align with HIPAA, GDPR, and other privacy and security standards. With a lack of fine-grained access control, audit trails, and data encryption, legacy platforms may expose medical information and delay product launches.

Impact on medical research and diagnostics

While AI can reinvent medical approaches, legacy limitations impede innovation and diagnostics. Without seamless data flow, compatibility, and flexibility, it’s hard to train AI models to identify disease patterns, assist in lab tests, or predict patient risks. Eventually, even when the technology is ready, implementation often stalls. Not because there is a problem with technology, but because the systems around it were not constructed to accommodate it. 

Modernization isn’t about “installing AI modules,” but restructuring architecture to support data pipelines, model retraining, and monitoring.

BacToByte: Implementing AI that Accelerates Antibiotic Testing

We’ll introduce one of the most impactful AI projects we've had. We built an AI-powered system that completely reimagines bacterial testing and antibiotics’ effectiveness evaluation. What’s particularly important, the product can be applicable across sectors, far beyond healthcare.

Problem statement

Conventional antibiotic susceptibility testing is usually performed within 2-3 days, which can be critical to patient treatment results, as well as to the efficiency of internal procedures. 

The client’s goal was to reduce the testing time to a few hours without sacrificing diagnostic precision. Faster antibiotic testing would enable quicker treatment decisions, which would positively affect the recovery process.

The modernization approach

Corsac didn’t invent an entirely new system. We focused on modernizing and enriching the client’s existing laboratory process, including proven workflows, data formats, and physical equipment.

Igor Omelianchuck generalizes the specifics of healthcare legacy software modernization: “You always need to follow certain processes, operations, and requirements which can be both legislative and procedural.”

Therefore, we aimed to enhance established practices through custom AI development and modernization, backed by scientific methods.

Key technical challenges

The project’s complexity moved beyond the biological layer: we needed to adjust data engineering and model design to the specific external conditions, existing equipment, and requirements.

● Microscope automation: The microscope had to make images at varying depths of focus, while the available frequency was limited. This required adaptive algorithms and data extrapolation to create and select meaningful results.● Data scarcity: There was a lack of properly labeled samples, while we needed many of them to train AI models.
● Manual labeling: Each microscopic image contained thousands of bacterial cells to label. Hence, a need for an efficient labeling strategy.
● Real-world constraints: The data collected in real lab conditions were full of noise and gaps, far from the ideally clean datasets preferred by AI systems. 

Andrew Lychuk about the adaptability in hospital IT modernization: “Our solution was successful not because we had perfect data, but because we designed it for imperfect real-world conditions.”

AI model development

Our experts utilized computer vision to analyze bacteria and classify their states into healthy, damaged, destroyed, or clustered (indicating resistance to antibiotics). Model training was performed in two stages, employing different optimizers for higher efficiency and precision. 

The results exceeded our expectations: the AI model achieved over 95% accuracy and successfully reduced the testing time to 3-5 hours, compared to 2-3 days with conventional methods. This result can be rightfully considered a near-real-time antibiotic resistance testing.

Validation and scientific reliability

Despite the outstanding precision of the model’s results, in healthcare, you always need to ensure they are scientifically valid. We verified the product’s clinical validity in cooperation with microbiologists. 

We also proved the scientific validity through a mathematical framework that confirmed the statistical reliability of the classifier’s predictions.

This validation demonstrated that our AI model can not only be integrated into clinical laboratory environments but also be applied in similar projects, which was another significant achievement.

Lessons Learned from BacToByte

The BacToByte project goes far beyond the successful implementation of machine learning. It demonstrates the ability to handle complex healthcare challenges with AI.

Focus on the problem, not the technology

The entire project revolved around the primary point: we had to understand the clinical problem first and only then select tools and algorithms.

As Igor Omelianchuk notes, “A common mistake modern AI startups make is that they try to adjust the product to the AI technology, forgetting that technology is just a tool. Without the actual problem to solve, it is mostly useless.”

Our BacToByte team started by exploring how antibiotic testing happens in real labs and then moved on to designing the AI approach that could meaningfully improve it. This allowed us not only to build a smarter system but also a faster and more practical diagnostic workflow.

Collaborate with domain experts

Legacy system modernization for healthcare is the area where engineering and science intersect. You should be armed with knowledge from both domains to address healthcare issues with AI. Therefore, we collaborated with medical professionals who helped us understand priorities and insights to go through data labeling, model validation, and clinical interpretation. This partnership transformed technical undertakings into medically significant and scientifically reliable outcomes.

Embrace imperfect real-world data

Laboratory data is rarely ideal. At the first stages of our modernization process, we faced data scarcity, inconsistent image quality, and irregular bacterial samples. However, we didn’t try to clean this data or otherwise adjust it to the tech. We designed an AI model that could work under imperfect conditions.

Andrew Lychuck highlights: “AI isn’t just about algorithms; it’s about thoughtful data preparation. If your tech does not work with your real-life data, the problem is not the data.”

Prioritize real impact

We conducted every development stage with a single main goal: to provide reliable and quick test results that enable earlier patient treatment.  

This clear focus sorted out needless efforts, enabling us to significantly save time and budget.

The takeaway: Modernization of healthcare is not about pursuing new technology. To solve meaningful problems, you should fuse expertise, collaboration, and innovation in a real-life environment.  

Beyond Healthcare: Cross-Industry Applications

For our team, the BacToByte project became the modernization milestone, not just a case study with AI implementation. It demonstrates that the principles used in the healthcare domain can drive innovation in other industries. 

The fundamental methodology, including automation, visual data analysis, and AI-enabled decision-making, has broad potential in working processes that involve image or video interpretation.

● Manufacturing: Detecting microscopic defects on production lines to guarantee high-quality products.● Agriculture: Monitoring crop health and identifying early signs of disease or issues through AI-based image processing.● Defense & Security: Performing real-time visual analysis to support instant information and threat detection.

Andrew Lychuck summarizes: “Empowered with domain knowledge and technical expertise, we challenged the process that’s been the same for decades, and nobody tried to change it because they’ve simply got used to it.” By utilizing AI that sees, measures, and decides faster, we can build solutions that improve operational accuracy and response time across multiple data-intensive sectors. 

The Future of AI in Healthcare Modernization

AI in clinical practice

AI technologies are already helping doctors in numerous areas, like spotting fractures, examining scans, and detecting early signs of disease. However, the adoption of AI healthcare is still below average compared to other industries. The next step is integrating AI models into clinical environments as part of everyday diagnostic and decision-making processes. 

Data standardization and integration

Such integration requires data standardization to guarantee interoperability between legacy systems, laboratory equipment, and modern AI platforms. With unified data formats and reliable integration pipelines, healthcare institutions can seamlessly share insights, set more precise diagnoses, and improve treatment results.

AI-enabled decision support

Another vital direction in healthcare modernization is the development of AI-powered clinical decision support systems. They utilize both guidelines and real-time information to assist doctors in choosing treatments. By analyzing patient records, laboratory results, and imaging data, these systems expedite treatment decisions.

Smart diagnostic laboratories

All these advancements will ultimately pave the way for smart diagnostic laboratories, a future of quick and efficient healthcare services. They are interconnected for instant data exchange between software and medical advisors, enabling automated sample tracking, continuous data labeling, and cross-system orchestration.

Igor Omelianchuk highlights the bottom line: “We believe that systems like BacToByte will become a standard part of next-generation diagnostic workflows, helping doctors make better and faster decisions and saving more lives.”

From the technical angle, the BacToByte story conveys the essence of competent healthcare software modernization services: respecting what works and building innovations upon it. This approach transforms conventional medical procedures, facilitating patient care with innovation and data-empowered insights.

Book an expert consultation to modernize your legacy system with AI in an impactful and efficient manner.