How AI Can Propel Medical Laboratories into a New Era of Growth

How AI Can Propel Medical Laboratories into a New Era of Growth

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What was predicted for years has now come true. The age of artificial intelligence (AI) has arrived. 

AI is already driving significant changes within various industries including healthcare, and the sky’s the limit in terms of new opportunities that are now possible thanks to this amazing new tool.

Let’s take a moment to explore how advances in AI are enhancing all aspects of our lives before taking a deep dive into what AI can specifically do to propel clinical laboratories and pathology groups into a new era of innovation and growth. 

The doctor examines the results of a laboratory study

Defining AI and its Various Applications

Artificial Intelligence refers to using computer systems to accomplish tasks that previously would’ve required human intelligence. Tasks like learning, reasoning, and problem-solving. AI systems are advancing rapidly and are now capable of carrying out these types of tasks without human assistance. 

AI systems can now replicate, supplement, and even enhance human cognitive abilities to perform specific tasks, doing so with even more speed, efficiency, and accuracy than a human being is capable of.  

Next, let’s take a quick look at AI, its subsets, and its application across different industries.

Symbolic or Rule-Based: This approach involves encoding human knowledge and expertise into rules and symbols that the AI system in turn uses for reasoning and decision-making.

Machine Learning (ML): ML is a subset of AI that involves developing algorithms and statistical models that allow AI computers to learn from data and make predictions or decisions based on that data. ML algorithms improve their performance over time as they are exposed to more and more data.

Deep Learning: Deep learning is a subset of ML that uses artificial neural networks inspired by the structure and functioning of the human brain to learn complex patterns and representations from available data. Deep learning has achieved remarkable success by being applied in areas such as image recognition, natural language processing, and speech recognition.

Now let's take a look at examples of how AI technology in its various forms is being deployed to improve industries as diverse as finance, manufacturing, education, and entertainment.

Finance: AI algorithms are used to analyze large volumes of financial data to better predict both market trends and potential investment opportunities. Additionally, chatbots and virtual assistants are also used to administer customer service and financial advice.

Process Improvement: Through predictive maintenance, quality control, and supply chain management AI is used to improve efficiency and productivity. AI-powered robots and cobots (also known as collaborative robots) are used to automate repetitive tasks and work alongside human workers.

Education: AI is used to personalize learning platforms and virtual classrooms. AI algorithms are used to analyze student data to generate personalized recommendations and feedback that help students learn at their own pace and style. Virtual reality (VR) and augmented reality (AR) technologies are used to create active and engaging learning experiences.

Entertainment: AI is used for content recommendation algorithms, personalized streaming services, and AI-generated content. AI is used by streaming platforms to analyze user preferences and behavior to recommend movies, TV shows, music, and other content that’s tailored to an individual’s tastes.

Applying AI in Tandem with Modern Laboratory Information Systems  

Now let’s focus on how AI applied within a medical laboratory can drive both innovation and scalability by working in tandem with a modern laboratory information system (LIS) to improve lab workflow processes, disease detection, and most importantly, personalized patient care and better patient outcomes. 

Here are examples that highlight how the addition of AI technology within clinical lab workflow can make a huge operational difference.

Automated Data Entry: Optical character recognition (OCR) powered by AI technology can automatically extract data from handwritten or printed laboratory requisition forms, test orders, and patient records, thereby reducing the need for manual data entry into the laboratory information system by LIS staffing. This automation minimizes costly errors associated with manual transcription and speeds up the turnaround time (TAT) of laboratory orders. 

Workflow and Process Optimization: AI algorithms can be used to analyze lab workflow and specimen processing protocols to identify bottlenecks and suggest optimizations that result in faster turnaround times, decreased labor costs, and enhanced overall productivity. Through examination, for instance, these algorithms can help the lab run much more efficiently by identifying manual repetitive tasks that could be replaced by laboratory information system software automation. AI can also be used to integrate data from disparate sources, providing a holistic view and bringing to the surface correlations that might otherwise be missed in siloed data sets.

Workload Prioritization: LIS systems with integrated AI algorithms can analyze data like incoming test orders, patient demographics, and case urgency to prioritize lab workflow and efficiently allocate resources. By identifying high-priority cases and critical patient samples, AI can be used as a tool that ensures timely processing and reporting of test results.

Diagnostic Accuracy: Deep learning AI algorithms can be trained on vast datasets to recognize patterns that might be challenging for humans to recognize. For instance, in pathology, AI can assist in detecting anomalies in histopathological slides, thus ensuring early and accurate disease detection.

Predictive Analytics: AI algorithms tied to the lab information system can analyze historical test data along with trends and patterns over time to predict future outcomes. For instance, AI can analyze genetic information to predict how individual patients will respond to specific treatments. AI can also guide lab professionals by analyzing test demand and the accompanying resource requirements. With this type of forecasting, AI can help lab management optimize lab staffing, inventory management, and instrument utilization. 

Quality Control: AI-powered algorithms focused on anomaly detection can continuously monitor the laboratory data managed by the LIS software (test results, instrument readings, quality control metrics, etc.) to identify deviations from expected norms or trends. By flagging potential errors or outliers in real-time, AI can promptly alert laboratory staff to investigate and address detected issues, ensuring the accuracy and reliability of test results.

Decision Support: AI-based decision support systems can assist laboratory professionals with real-time analytics to help with the interpretation of complex test results. By analyzing large lab information system datasets and clinical guidelines, and then identifying patterns and trends, AI can assist with both diagnosis and treatment recommendations. 

Continuous Learning and Improvement: AI systems can leverage machine learning techniques that are capable of adapting and improving over time based on new data fed into the system. Through continuous learning, AI can constantly refine its algorithms to optimize performance and improve the efficiency and accuracy of laboratory operations.

Training and Education: AI-powered simulations and virtual labs can offer laboratory personnel convenient and advanced training, ensuring they're always up-to-date with the latest lab workflows and protocols.

A laboratory assistant conducts laboratory tests

How LigoLab is Integrating AI into its Informatics Platform

For nearly two decades, LigoLab has made a name for itself among laboratory information system companies for leading the way in LIS system innovation. Knowing this, it’s no surprise that the LIS company already has plans in place to leverage AI functionality that will benefit its LIS software client base.

Here are two examples of what LigoLab customers can look forward to shortly thanks to AI-LIS system integration.

Natural Language Processing: AI-powered algorithms will be deployed within LigoLab’s platform to first understand and interpret human language, and then analyze medical records and any other relevant data to extract information and accurately assign the appropriate ICD and CPT codes. 

By automating ICD and CPT coding, AI can greatly improve the overall accuracy and efficiency of this very important lab workflow process, first by reducing errors and inconsistencies, and second, by improving TAT. Additionally, coding handled with AI and automation can also free up valuable time for pathologists and coders to focus on other critical tasks.

Recommendations and Personalized Reporting: Similar to the predictive analytics mentioned earlier, AI algorithms supported by the LigoLab platform will analyze historical laboratory data along with other patient-specific information (such as demographics, medical history, and genetic information) to predict future health risks and assist with disease management. 

By taking into account a patient’s characteristics and risk factors, LigoLab will not only be able to assist providers with personalized recommendations based on the AI analysis but also assist lab staff in the generation of personalized laboratory reports that are tailored to meet the individual patient's needs and preferences. This level of personalization may include the presentation of test results in a format that is easier to understand, providing additional context or explanations to help with test results comprehension, and highlighting relevant trends or changes that the patient should be aware of over time.

Make Sure Your Medical Laboratory is Ready for the Age of AI

The impact of AI has the potential to be a game-changer for medical laboratories in search of exponential growth. As AI becomes more sophisticated and computers gain capacity, the interaction between AI and laboratory operations will only become more integral. 

Clinical laboratories and pathology groups that embrace both AI and modern laboratory information systems will witness enhanced efficiency and accuracy and also be better positioned to be market leaders in terms of innovation and growth. 

If you’d like to learn more about LigoLab’s approach to laboratory information systems (LIS abbreviation medical) and the importance of remaining modern by partnering with laboratory information system companies that offer the latest technology, here are a few more links that may interest you.

Cost of Inaction: The Dire Consequences of Neglecting Modernization in Laboratory Operations and Informatics

Is Your Laboratory Information System Able to Support the Latest LIS System Technology?

The Best Laboratory Information System Companies (2024) and the Advantages They Offer to Their Laboratory Clients

How to Turn Your Mid-Sized Medical Laboratory into a Thriving Large-Scale Operation

How to Prepare for Tomorrow’s Tech-Enabled Clinical Lab

Michael Kalinowski
Michael Handles Marketing and Communications for LigoLab

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