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

Clinical laboratories are vital in providing timely and crucial diagnostic information for physicians and their patients. They accomplish this with a dedicated team and the aid of a Laboratory Information System, a software solution that centralizes, manages, and secures patient and specimen data, simplifies and standardizes clinical workflows, tracks and secures all specimens, generates and distributes patient reports, improves quality control, provides insights into the lab operations, and helps labs meet regulatory compliance requirements. 

Just like the labs they serve, no two LIS are alike. For the best LIS systems, the above description serves as a functionality baseline. More should be expected if the LIS system is to help lab operators navigate through turbulent times and prepare for tomorrow’s clinical lab. 

In the near future, the most successful clinical labs will be driven by technology. These labs will be adopters of innovation and new tech tools as they become available. They’ll operate both as clinical labs and tech companies, providing what’s needed for the most accurate testing and the best patient outcomes. They'll also allow the lab to become a better business by lowering operational costs, boosting productivity, attracting and retaining more customers, and raising more revenue. 

Besides its primary function of diagnostics, future labs will also be technology and data companies. The most successful ones will work with a technology partner that focuses on building enterprise-level software solutions that are capable of supporting advancements in technology. In essence, this partnership will ensure that the lab is not distracted from its core mission of diagnostics and that it will be able to focus on what it does best - and that’s serving the physician and the patient - while the informatics partner does the tech heavy lifting to drive maximum efficiency and market differentiation for the organization.

The Importance of Automation

So what does the future hold? One of the most significant changes will be the addition of more automation and laboratory robotics that improve efficiency and compliance. Automation in clinical laboratories will support advanced technology and robotics to perform laboratory tasks that were traditionally done manually. This includes processes such as sample preparation, data entry, testing, and result analysis. The use of automation has several benefits, including improved accuracy, increased speed, and reduced costs.

Laboratory tests can be complex and involve multiple steps, making them prone to mistakes when done manually. Automation eliminates the risk of human errors by performing tasks with a high degree of precision and consistency. A comprehensive laboratory software solution can support the entire lifecycle of a case to assure proper sample handling and reduce the misplacement of specimens by introducing tighter controls and validation steps.  

By automating repetitive tasks, such as sample preparation and testing, laboratory technicians can focus on more complex tasks that require human expertise. This leads to increased productivity and faster turnaround times for test results. Automated laboratory systems can also work 24/7, reducing the turnaround time needed for test completion and allowing for quicker diagnoses and treatments.

Another benefit of automation is its ability to reduce costs. Automated laboratory systems require less labor and can process more samples in less time, leading to significant cost savings for clinical laboratories. In addition, automation can reduce the number of reagents and consumables used in laboratory tests, further reducing costs. As automation continues to evolve, it is likely to become even more integral to the future of clinical laboratories.

The Role of Artificial Intelligence

Integrations with artificial intelligence (AI) and machine learning (ML) technology will help with sample processing and data analysis. Through image analysis AI can help pathologists improve the accuracy and speed of diagnoses, allowing for earlier detection of disease and better patient outcomes. AI algorithms are trained to analyze images of tissue samples and identify patterns that may be difficult for pathologists to detect. For example, AI can be used to detect the presence of cancer cells in tissue samples, even if they are in small or in hard-to-see areas. This can lead to earlier detection of cancer and more accurate diagnoses.

AI can also be used to assist pathologists in identifying rare or difficult-to-diagnose diseases. Pathologists may not encounter these diseases frequently enough to develop expertise in their diagnosis. AI algorithms can be trained to recognize patterns in tissue samples that indicate rare diseases, providing pathologists with the information they need to make accurate diagnoses. The technology can be applied to sort the cases for the pathologist to review, and as a function of quality assurance to get concurrence from humans and machines.

In addition to image analysis, AI can also be used to assist pathologists in other aspects of their work. For example, AI can be used to analyze patient data, such as medical histories and test results, to help pathologists make more informed diagnoses and free them up to focus on more complex tasks.

Another potential benefit of AI for pathologists is its ability to facilitate collaboration and knowledge sharing. AI algorithms can be used to analyze large datasets of tissue samples and identify patterns that may be missed by individual pathologists. This information can be shared among pathologists, helping to improve the accuracy and speed of diagnoses.

Another area where AI can be useful in clinical laboratories is in the interpretation of complex data. Many laboratory tests produce large amounts of data that can be difficult to interpret. AI algorithms can analyze this data to provide more accurate and reliable results. For example, AI algorithms can be used to analyze microbiology data, helping to identify the presence of bacteria and other microorganisms that may be difficult to detect using traditional methods.

AI can also be used to analyze data from laboratory tests. AI algorithms can identify patterns and anomalies in large datasets, helping to identify new biomarkers and predictors of disease. This can lead to the development of new diagnostic tests and personalized treatment plans. For example, AI algorithms can analyze genetic data to identify genetic markers associated with certain diseases. This information can be used to develop personalized treatment plans that are tailored to the patient's genetic makeup.

The Growth of Personalization

Personalized medicine will continue to grow as the focus shifts to healthcare that’s tailored toward the specific needs of an individual with a greater emphasis on analyzing and interpreting the data. The use of telemedicine and remote monitoring devices will help in this regard. Additionally, clinical labs will be shaped by the development of new diagnostic tests and therapies to support other growing fields of study such as genomics and proteomics. 

Laboratories can develop customized reporting templates that take into account the patient's specific health conditions, genetic makeup, and lifestyle factors. These templates can be tailored to provide personalized recommendations based on the patient's individual needs.

Reports can be further personalized with an interpretation of the results, highlighting the significance of the findings for the individual patient. This can be done by providing additional information on the patient's medical history, lifestyle, and other factors that may impact the interpretation of the results. The reports can include recommendations for dietary changes, exercise regimes, or other lifestyle modifications that can help improve the patient's overall health.

Laboratories can provide patient education materials that are tailored to the individual patient's health condition and medical history. These materials can help patients better understand their health conditions and the implications of the laboratory test results.

Overall, providing personalization in laboratory reports requires a combination of customized reporting templates, personalized interpretation of results, contextualized recommendations, and patient education materials. By providing personalized and actionable information, laboratories can help patients better understand their health conditions and make informed decisions about their health.

The 4 Keys that Separate Modern LIS Systems from Legacy Systems

To run a better business today and be prepared for what’s coming tomorrow, clinical labs will need to sunset their rigid and outdated legacy LIS systems and invest in modern rule-based, highly configurable, and customizable LIS platforms that maximize both efficiency and interoperability. 

So how can you be assured that the system and the partner you’re investing in is the right one? First, modern LIS platforms are built on flexible software infrastructure that’s scalable, flexible, and capable of supporting the newest technology as it becomes available to the lab. Second, they are backed by engineers and informaticians that are continually investing in the solution with new functionality and enhancements added daily. Third, comprehensive training and unlimited software support and maintenance. This is important because as the lab adds technology and volume, more and more support will be needed to break through barriers of growth and bottlenecks. Fourth, the LIS vendor is a true partner that genuinely cares, aligns, and has a vested interest in the laboratory’s success. 

In closing, tomorrow’s clinical lab will be tech-enabled and prepared for continued growth and innovation as the industry evolves. By embracing new technology and new approaches to healthcare, they’ll be properly positioned to be profitable industry leaders and play an increasingly important role in advancing both medicine and patient safety.