From System of Record to System of Action: The Next Evolution of Laboratory Information Systems

Laboratory information systems (LIS systems) have long served as the digital system of record for labs, essentially acting as repositories that process, store, and retrieve lab data. In a traditional role, a laboratory information system functions much like a filing cabinet or database, ensuring every patient result is recorded and accessible. But today, a profound shift is underway. 

Modern laboratory information system software platforms are transforming into systems of action, leveraging automation and artificial intelligence (AI) to not only record data but also to actively use that data to drive decisions and streamline lab operations. This evolution marks a move from passive data management to proactive, intelligent lab workflow orchestration. New technology enables laboratories to unlock the potential of their data rather than just storing it, shifting LIS pathology platforms from systems of record to systems of action.

For lab managers, this shift promises significant benefits. An AI-powered, “next-gen” LIS as a system of action can automate lab workflows, improve decision-making with real-time intelligence, and turn raw data into actionable insights. In this article, we’ll explain what this transformation means in the context of laboratory operations, the advantages it brings, and how it changes workflows, decision-making, and data use in the lab. 

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From Systems of Record to Systems of Action in the Lab

To appreciate this evolution, it’s important to understand the difference between a system of record (SoR) and a system of action. An LIS system has historically been a system of record: a central repository for lab results and patient test information. It ensures that lab data (patient demographics, test orders, results, quality control records, etc.) are safely stored and managed. 

In this role, the laboratory information system serves as the central hub for documentation and regulatory compliance, providing an authoritative record of all tests performed and their results. However, traditional legacy LIS systems function primarily as passive repositories. They capture past events but don’t actively guide or influence what actions should happen next.

By contrast, a system of action takes things a step further. A system of action not only houses information but also interprets it and initiates appropriate responses. In the lab context, a system-of-action LIS healthcare solution would not just collect test results; it would actively help specimens move through the lab workflow, flag significant data, suggest or trigger next steps, and generally ensure that information leads to action. 

In other words, the LIS system becomes an intelligent orchestrator of lab activities, not just an archive. LigoLab’s vision for AI in enterprise pathology software captures this well: AI-enabled systems can “automate much of the time-intensive data entry and repetitive tasks… putting entire laboratory processes on autopilot.” This means that routine laboratory processes, from data entry to result verification, can be handled or guided by the LIS software with minimal manual effort.

Crucially, the move to a system of action is about using data in real-time to add value, rather than just storing data. A traditional medical LIS might store thousands of test results but leave it up to humans to notice patterns or decide on follow-up actions. A next-gen and AI-infused LIS system can analyze those results on the fly and prompt humans (or other laboratory software systems) with insights or actions. The end game is an advanced lab information system that actively helps the lab run more efficiently and intelligently.

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What’s Driving the Evolution? 

Several factors are driving LIS laboratory information system platforms to evolve from mere record-keepers into active participants in lab operations:

• Advances in AI and Automation Technology: Recent breakthroughs in AI, including machine learning (ML), deep learning, and intelligent algorithms, have made it feasible for LIS software to interpret complex data and make recommendations or take actions. AI can now parse unstructured data, recognize patterns, and even generate scripts or integrations that previously required manual programming. For example, modern AI can extract information from a scanned requisition form, fax, or email (using natural language processing and optical character recognition), and then automatically enter it into the LIS system without human transcription. It can also learn from historical lab data to predict what might happen next. These capabilities erode the old limitations of legacy LIS systems. Industry observers note that incorporating AI into LIS software facilitates data analysis, automates workflows, and allows systems to predict potential issues before they escalate. In short, AI is the engine that turns static LIS system software databases into dynamic, smart assistants.
  
• Need for Greater Efficiency and Throughput: Laboratories today face pressure to do more with less. Factors like limited staffing, higher testing volumes, cost constraints, and fast turnaround expectations mean labs must optimize operations continually. A system-of-action medical LIS is a direct response to these pressures. By automating repetitive tasks and streamlining processes, the LIS system can significantly boost productivity. As one lab industry publication put it, a modern LIS system “should enable laboratories to automate workflows and optimize their operations” to cope with changing needs. The shift is a practical necessity to increase output and reduce errors when resources are tight. Lab managers are embracing automation solutions in LIS systems to handle tasks that once required manual labor, from sample sorting to result reporting, thus freeing up staff for higher-level duties.
  
• Demand for Real-Time Decision Making: In healthcare LIS systems, timing and accuracy are critical. Clinicians increasingly rely on labs not just for raw results, but for fast answers and even guidance. An LIS that remains a passive data silo doesn’t meet this modern demand. Lab managers now want real-time dashboards, alerts for exceptions, and decision support tools built into their laboratory information systems. The evolution to a system of action is fueled by this expectation that the LIS should provide instant insights, for example, immediately alerting on a critical lab result and even initiating a notification workflow to the physician. The goal is to improve patient care through quicker and smarter decisions supported by the LIS software.
  
• Interoperability and the Digital Ecosystem: Today’s labs operate in a highly connected environment of electronic health records (EHRs), laboratory instruments, reference labs, and even patient portals. The LIS system can no longer be an isolated record store; it needs to seamlessly integrate and act within this ecosystem. Improvements in standards (like HL7/FHIR for health data exchange, or DICOM for digital pathology images) and integration capabilities mean the LIS system can automatically pass data to other systems or trigger cross-system actions. For example, if a lab test result meets certain criteria, a modern LIS software might automatically send data to an EHR and also trigger an alert or follow-up order. AI can assist by transforming and routing data between systems intelligently. This interoperability push is another driver turning medical LIS platforms into active hubs of laboratory informatics rather than passive endpoints.

In summary, powerful technology combined with practical laboratory needs has set the stage for the best laboratory information system software to be more than a static database. Lab managers are looking for “next-gen LIS software” solutions - often cloud-based, AI-powered, and highly flexible - that can adapt and drive lab workflow automation while providing actionable insights. The timing is right: many labs are due for LIS system upgrades, and C-suite mandates are prioritizing AI capabilities. The convergence of need and opportunity is pushing modern LIS systems into this new role as systems of action.

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Key Features of an LIS as a System of Action

So, what does an AI-driven, action-oriented medical laboratory information system do differently? Below are some of the key characteristics and capabilities that define a “system of action” LIS in a laboratory setting:

• Intelligent Laboratory Workflow Management Automation: A system-of-action LIS healthcare solution actively automates and orchestrates lab workflows from end to end. This goes beyond basic sample tracking. An advanced LIS system can initiate actions and route tasks in real time. For example, when a test order arrives, the system might automatically print specimen labels, schedule the sample on the appropriate analyzer, and notify the technician if any special handling is needed - all without manual intervention. Many modern LIS software platforms now include robust rules engines and AI algorithms that handle such process steps. They ensure that each specimen moves through the correct steps swiftly and consistently. Repetitive manual tasks are minimized. One concrete example of this is automated data entry. Using AI-based optical character recognition (OCR), an advanced medical LIS can capture data from handwritten or faxed requisitions and input it directly, reducing transcription errors and saving time. Automatic reflex testing rules are another example - if an initial result meets certain criteria (say a positive screen), the laboratory information system can immediately trigger the follow-up confirmatory test order without waiting for a person to notice. By automating these kinds of workflows, the LIS system acts as the lab’s autopilot, handling routine decisions and actions. In essence, the LIS platform becomes the lab’s “central nervous system,” coordinating activities with speed and reliability.
  
• Adaptive Scheduling and Resource Allocation: An action-oriented lab information system doesn’t treat all samples or tasks equally - it intelligently prioritizes and allocates resources based on need. AI-driven predictive prioritization is a hallmark feature. The LIS system can analyze historical trends and real-time incoming data to forecast the urgency or complexity of cases in the queue. For instance, by looking at factors like test type, client, priority, patient location (ER or routine outpatient), and abnormal flags, the LIS can predict which samples are high-priority. It then suggests or automatically assigns those cases to the most qualified technicians or fastest instruments, ensuring critical work is done first. Less urgent cases can be batched or routed differently for efficiency. This dynamic scheduling means the lab’s workload is continuously optimized on the fly. As a result, patients with time-sensitive conditions get results faster, and overall throughput improves. Strategically aligning resources by forecasting case urgency, then routing the most critical cases to top technicians, sharply lowering turnaround times for urgent cases. In practical terms, a lab manager might come in each morning to find the LIS system software has already prioritized the day’s pending tests and even suggested an optimized worklist for each staff member.
  
• Triggering Actions Based on Data (Rules and AI Agents): A system-of-action LIS is event-driven - it doesn’t wait passively for humans to act on data. Instead, it’s configured to respond when certain data conditions occur. Traditional LIS software has long had rule-based triggers (for example, auto-verifying results within reference range, or printing a report when all tests in a panel are complete). The new evolution supercharges this with AI. Consider digital pathology: a cutting-edge LIS system can automatically launch an AI image analysis algorithm when a pathology slide is scanned and a case meets particular criteria, then incorporate the AI’s findings right into the LIS record. This happens seamlessly, without the pathologist having to manually run the AI tool. Similarly, in clinical labs, an AI-enabled LIS system could automatically run a quality control analysis or instrument self-check if data coming in shows a potential calibration drift. The system essentially watches the data and acts in real time. These agentic behaviors turn the LIS software solution into more than a passive system - it behaves like a proactive assistant. Lab managers can set up complex logic: for example, “If test X is critically high, not only flag it but also page the on-call pathologist and quarantine the sample for re-run.” An advanced pathology information system can execute such multi-step actions instantly. By ensuring consistent, immediate responses to important events, the system reduces delays and human oversight gaps.

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• Real-Time Decision Support: One of the most transformative aspects of an LIS as a system of action is its ability to provide real-time decision support for laboratory professionals. Rather than technicians and managers having to sift through data or recall detailed protocols, the modern laboratory information system can serve up guidance exactly when and where it’s needed. AI-driven decision support in the medical LIS might take the form of on-screen alerts, recommendations, or even automated decisions that the user can approve. For instance, if a patient’s results show a critical value, the LIS system might not only alert the technologist but also recommend confirmatory tests or suggest possible causes based on historical data and the patient’s profile. AI-powered LIS software can concentrate on time-sensitive cases, propose follow-up tests based on the evolution of a patient’s condition, and clarify complex medical situations, thereby helping lab staff make accurate and prompt decisions with up-to-date information. In practice, this could mean the lab information system notes that a patient’s hemoglobin has been dropping over several days and suggests a reflex test for internal bleeding indicators, or it could mean automatically providing an interpretative comment on a complex serology pattern to aid the physician. The LIS system acts like an experienced colleague looking over the shoulder of lab personnel, ensuring nothing important is missed. This kind of AI-powered advice and context at the point of decision can greatly enhance the quality of lab interpretations and troubleshooting.

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• Data-Driven Insights and Analytics: A next-gen LIS healthcare solution doesn’t just store data for compliance; it continuously mines that data to generate useful insights for running the lab better. Built-in analytics dashboards and reporting tools turn the raw data in the LIS system into meaningful metrics and trends. For example, a lab manager using a system-of-action LIS could instantly pull up trends on test turnaround times for the past month, identify bottlenecks on specific shifts, or see quality control metrics that are out of range. This analytical capability means decisions in the lab can be evidence-based and proactive. If the analytics show that Thursdays have the highest sample volume and longest delays, a manager might add an extra tech on Thursdays - or the medical LIS itself might recommend it, having detected the pattern. Predictive analytics go even further: by examining historical data, seasonal trends, and even external data, an AI-enabled LIS system can forecast future needs. It might predict, for instance, that next week’s flu testing volume will spike based on trending data, prompting the lab to stock more flu test kits or schedule overtime in advance. One report notes that AI tied into the LIS software can analyze test data and trends to “help lab management optimize lab staffing, inventory management, and instrument utilization” by forecasting resource requirements. In essence, the best laboratory information system turns into a lab operations intelligence system - not only recording what you did, but telling you what you could or should do to improve. For the lab manager, this is like having a control tower view of the lab with predictive indicators for decision-making. Data that was once locked in spreadsheets or only reviewed in retrospective meetings now actively drives day-to-day management.

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• Continuous Learning and Improvement: The combination of AI and an active LIS means the system can learn and improve over time. Machine learning models can continuously update based on new data coming through the lab. This means the LIS system might get better at, say, flagging abnormal results or predicting instrument failures as it gathers more examples. Over months and years, the system “tunes” itself to the lab’s specific patterns and constraints, becoming more accurate and reducing false alarms. This continuous learning loop ensures that the LIS’s performance doesn’t stagnate - it adapts to new testing methods, evolving quality standards, and even changes in laboratory staff behavior. Ultimately, the system of action LIS can help a lab stay on the cutting edge without constant reprogramming; it’s always self-optimizing in the background. Integrating data analytics and ML in the LIS software environment not only improves data management and quality control but also enhances decision-making processes and workflow efficiency. The lab effectively gains a partner that is always looking for ways to improve accuracy, speed, and compliance.

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• Seamless Integration and Interoperability: Finally, an LIS that operates as a system of action is typically highly connected. It integrates with analyzers, middleware, hospital information systems, public health databases, laboratory billing/lab revenue cycle management systems, and more. This integration is not new for advanced pathology LIS systems, but what’s different is the fluidity and automatic nature of interactions. A modern LIS software utilizes standard interfaces (HL7, FHIR, etc.) to push and pull data as needed without manual steps. For example, as soon as a test result is validated, the lab information system might automatically send it to the physician’s EHR inbox, trigger a lab billing event in the laboratory revenue cycle management software, and update a cumulative patient report - all within seconds. Similarly, if a physician places an electronic order, the LIS system immediately receives it and can kick off the lab workflow as described earlier. This real-time interoperability is crucial for the laboratory information system to drive actions beyond the lab’s four walls. Moreover, the LIS often acts as a central hub that merges data from various sources: it can pull demographic info from an EHR, receive instrument data from analyzers, accept orders from outreach client systems, and compile everything into one coherent stream. By eliminating data silos and delays, the best LIS software ensures that actionable information is available wherever it’s needed. This level of integration not only improves efficiency but also reduces errors (since there’s less manual data transcription between systems). Ultimately, the LIS as a system of action functions as the coordination point for the entire lab-related workflow across systems, ensuring that what needs to happen, happens quickly and correctly.

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How Lab Workflows and Operations Change with an AI-Driven LIS

With the key features outlined, let’s paint a picture of how daily life in the lab changes when the LIS evolves into a system of action. For lab managers and technicians, these changes are significant:

1. Streamlined Workflows and Less Manual Work: Perhaps the most immediate change is a dramatic reduction in routine manual tasks. In an automated clinical lab workflow, sample handling is optimized - the LIS system might tell an automated track system or a human exactly where each sample needs to go next. There’s less time wasted figuring out priorities or hunting for information. Many labs implementing these systems report that steps like data transcription, label printing, and even certain result validations happen in the background. For example, with auto-verification rules, the LIS system can automatically verify and release normal results straight to the medical record, without a technologist intervening, while diverting only the abnormal or flagged results for review. This means technologists spend their time on exceptions and problems rather than rubber-stamping routine data. One outcome is faster turnaround times (TAT) for most tests. When high-priority cases are automatically expedited and routine cases are handled efficiently, overall workflow throughput increases. AI-driven LIS software solutions shorten turnaround periods and make future lab platforms more efficient. For a lab manager, this may manifest as improved metrics such as average TAT for test suites drops, or handling more tests per tech per day without compromising quality.

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2. Improved Decision-Making and Fewer Errors: With robust decision support and automation, lab staff can make better decisions faster because advanced pathology LIS systems can now actively assist in interpreting data and enforcing protocols. For instance, if a quality control result falls outside the acceptable range during a morning run, a modern LIS system can instantly notify the lab team and automatically pause testing on the affected analyzer. This proactive response helps prevent inaccurate patient results and ensures issues are addressed before testing resumes. If a tech encounters an unusual result pattern, the LIS software could provide on-screen guidance (maybe an alert: “This pattern may indicate hemolysis; consider checking for hemolysis index or requesting a new sample”). Such guidance reduces the cognitive load on staff and helps maintain consistency, especially with less experienced personnel. Moreover, by catching anomalies and suggesting follow-ups, the lab information system helps avoid mistakes or missed diagnoses. An AI-equipped LIS in healthcare can continuously monitor instrument data and control values to catch subtle drifts or outliers, essentially functioning as a real-time quality guardian. One description of this capability explains that AI algorithms can watch lab data (results, instrument readings, QC metrics) and flag deviations from expected norms in real time, promptly alerting staff to address issues and thus “ensuring the accuracy and reliability of test results.” The result is fewer erroneous results going out and less rework due to errors caught later. When it comes to compliance and patient safety, these improvements are game-changers. Lab managers will find that an LIS system of action can help enforce standard operating procedures automatically (for instance, not allowing a test to be reported if a required QC or calibration is missing), thereby strengthening overall lab compliance and quality without constant human oversight.

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3. Proactive Management Using Analytics: In the past, a lab manager might have had to wait for monthly reports or spend hours compiling data to know how the lab is performing. In a system-of-action paradigm, the LIS system delivers actionable data daily (or on-demand) via dashboards and alerts. This means operations can be tuned in near-real-time. For instance, if the LIS system dashboard shows that the chemistry analyzer is approaching capacity by midday, the manager can proactively redistribute some tests to a backup analyzer or extend shifts, that very day. If test volume is trending 20 percent higher this week, the lab information system might suggest ordering additional reagents or kits now to avoid shortages. Essentially, decision-making shifts from reactive to proactive. The lab can anticipate problems (backlogs, reagent stockouts, instrument downtime) before they happen and act to mitigate them. Moreover, the LIS software can highlight positive opportunities, such as identifying a test that has grown in volume by 50 percent over the last quarter, which might justify investing in a higher-throughput instrument or automation for that area. By leveraging data, lab managers make evidence-based decisions rather than relying on gut feeling. One LIS company vendor analysis noted that by reading historical data and trends, the LIS’s analytics provide “valuable insights that assist strategic decision-making” and help optimize workflows. This leads to continual operational improvement. Over time, the lab becomes more agile and can handle growth or changing testing demands without the wheels coming off, because the decisions guiding those changes were data-driven and timely.

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4. Enhanced Collaboration and Communication: When an LIS system is tightly integrated and proactive, it also improves how the lab communicates with the rest of the healthcare ecosystem. Automated notifications and comprehensive, real-time data sharing mean that clinicians, lab managers, and even patients stay more informed. For example, a physician might get an instant alert (via EHR or text) that a critical result was generated, because the LIS triggered that action upon result validation. Or consider that a lab client (like a clinic sending out tests) can log into a portal and see status updates because the LIS system continuously feeds the portal with up-to-the-minute information. This level of transparency can improve trust and satisfaction with lab services. Internally, staff in different sections of the lab (say, microbiology vs. chemistry) can coordinate better when the laboratory information system provides an integrated view of all pending work and flags dependencies (such as a microbiology culture that, once a pathogen is identified, should trigger a chemistry test for toxin assay - the LIS could alert the chemistry section). The system of action LIS essentially acts as a communication hub, ensuring that information flows to the right people at the right time. This reduces the chances of things “falling through the cracks” during shift changes or across departments. Lab managers will notice smoother hand-offs and fewer status inquiries because the LIS system has already kept everyone in the loop.

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5. Empowered Laboratory Staff: There can be an initial fear that more automation and AI might diminish the role of lab professionals. In practice, labs report the opposite - when mundane tasks are automated and decision support is readily available, staff are empowered to work at the top of their skillset. Technologists and pathologists can focus on complex analytical problems, troubleshooting, and interpretation, rather than clerical work. This can lead to higher job satisfaction and better utilization of expertise. For example, instead of spending an hour cross-checking test requisitions with manual entries, a tech can trust the LIS’s automated data entry and use that time to validate a new assay or review abnormal results in depth. The LIS system may also serve as a training aid: less experienced staff benefit from built-in guidance and safety nets that help them learn faster. Overall, the lab operates more like a high-performance team supported by a smart infrastructure. Each member can do more, with less stress, because the LIS system handles the drudgery and assists with the heavy cognitive lifting. In times of staffing shortages, this is especially crucial - an efficient system-of-action LIS can help a small team manage a large volume of work by amplifying their capabilities.

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Benefits of the LIS Shift from Record to Action

Adopting an LIS that functions as a system of action yields a variety of benefits for laboratory operations and beyond. Here’s a summary of the key advantages:

• Greater Efficiency and Productivity: Automation of workflows means labs can process more tests in less time. By eliminating manual bottlenecks and idle time, an AI-driven medical LIS improves overall throughput. Routine tasks (data entry, result verification, report distribution) happen faster, and staff can handle higher volumes without burning out. Ultimately, this boosts lab productivity and can reduce operational costs per test.
  
• Faster Turnaround Times (Improved TAT): Intelligent prioritization and rapid routing of specimens ensure that critical tests are completed as quickly as possible. Even routine tests benefit from streamlined processes. The net effect is that patients and physicians get results sooner. Faster TAT can be a competitive advantage for labs and directly contributes to better patient care (like quicker diagnosis and treatment). As one AI in LIS review noted, these technologies are “pushing laboratories into a new era of accuracy and efficiency” where diagnoses occur much faster.
  
• Enhanced Accuracy and Reduced Errors: By automating data handling and applying consistent rules, a system-of-action LIS cuts down human error. Fewer transcription mistakes, lost samples, or overlooked critical values mean higher accuracy in results. Moreover, real-time quality control monitoring and AI anomaly detection catch issues early, preventing errors from propagating. The overall quality of lab results improves, which means better patient safety and fewer corrected reports.
  
• Data-Driven Decision Making: Lab managers and directors gain a wealth of actionable information at their fingertips. Decisions about staffing, purchasing, and process changes can be made based on hard data and predictive analytics rather than guesswork. This leads to smarter strategic planning and resource utilization. When an advanced LIS software offers insights (like which tests are unprofitable, or which times of day see peak demand), lab management can make informed changes that positively impact the lab’s efficiency and financial health.
  
• Improved Workflow Visibility and Control: A system-of-action LIS often provides real-time dashboards and audit trails that make the lab’s operations highly visible. Managers can see exactly what’s happening in the lab at any moment - pending tests, turnaround stats, instrument statuses, etc. This visibility allows for better control. If something is going awry (like a sudden spike in TAT for a particular instrument), it’s immediately apparent and can be addressed. Essentially, the lab becomes a well-monitored environment, which helps maintain high service levels.
  
• Better Compliance and Audit Readiness: Automated processes and detailed electronic records also make compliance with regulations (CLIA, CAP, HIPAA, etc.) easier. Every action in the lab information system can be logged and is traceable. The system can enforce required checks (like not allowing result approval if QC fails) and document them. When audits occur or quality reports are needed, the lab can readily demonstrate compliance. This reduces the risk of non-conformities and the effort in preparing compliance documentation.
  
• Scalability and Future-Proofing: Next-gen LIS software platforms are often built on modern, scalable architectures (for example, cloud-based systems, as noted in market trends). Combined with AI-driven optimization, this means labs can scale up testing volumes or add new testing modalities without a linear increase in workload or turnaround times. The LIS system adapts to growth, allowing a lab to expand services (like adding molecular or genetic tests) while maintaining efficiency. It also means the lab is ready to integrate future technologies (new analyzers, digital pathology, at-home test data, etc.) more easily. In a rapidly evolving field, having a flexible and intelligent LIS lab solution acts as a hedge against obsolescence.
  
• Improved Patient Care and Satisfaction: Although patients may not directly interact with the laboratory information system, they do experience the downstream benefits. Faster and more accurate results lead to quicker diagnoses and treatments. Fewer errors mean greater trust in lab results. Some advanced LIS lab setups even facilitate personalized reports or patient portals that engage patients with their lab results directly. Ultimately, an efficient lab contributes to better clinical outcomes. As one expert summarized, the use of AI in LIS leads to patients “whose diagnoses will occur much faster and more accurately,” improving healthcare delivery overall. Clinicians also enjoy more timely consultations with lab specialists and receive more insightful lab reports (with interpretive comments or suggestions), enhancing the clinician-lab collaboration in patient care.
  
• Lab Staff Satisfaction and Development: When mundane work is offloaded to the LIS system, lab professionals can focus on what they trained for - the scientific and analytical aspects of lab medicine. This not only improves morale but also allows staff to develop skills in interpreting data and managing complex scenarios (with AI as a guide). The LIS can even serve as a teaching tool, as mentioned earlier, by providing decision support and ensuring protocols are followed. In the long run, this can help with staff retention and recruitment, as the lab is seen as a high-tech environment where professionals can practice at a high level, rather than being buried in paperwork.

In summary, moving to a system-of-action approach with a modern medical laboratory information system isn’t just a tech upgrade - it fundamentally improves how the lab operates, from day-to-day efficiency to strategic planning and service quality. Labs become more agile, reliable, and valuable to the healthcare organizations they serve.

LigoLab has been at the forefront of LIS systems and pathology lab software innovation. LigoLab’s advanced all-in-one informatics platform exemplifies the next-gen medical LIS approach by integrating LIS functionality with automation and AI-driven features in a unified system. For instance, LigoLab has publicly discussed leveraging AI for tasks like natural language processing (NLP) to automate coding of test orders (extracting information to assign the correct lab billing and diagnostic codes) and using predictive analytics on lab data to assist with personalized patient insights. These kinds of features align with the “system of action” model - the system actively processes language data and generates recommendations, reducing manual coding work and potentially identifying patient risk patterns that prompt proactive healthcare measures. By building such capabilities into their innovative LIS software, LigoLab and similar laboratory information system vendors are turning the concept of AI-powered, action-oriented LIS into a practical reality for labs.

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Conclusion: Embracing the Future of Lab Informatics

The evolution from LIS as a simple system of record to a sophisticated system of action represents a leap forward in laboratory operations. What was once essentially a digital filing cabinet for test results has become a proactive command center for the lab. This transformation is driven by AI, machine learning, and automation technologies that empower the LIS software to take on tasks, make recommendations, and ensure information leads to outcomes. For lab managers, this means a chance to greatly enhance efficiency, reduce errors, and gain deeper insight into their operations. It enables labs to handle growing testing demands and complexity without commensurate increases in workload or turnaround time.

Embracing a system-of-action mindset for your laboratory information system is increasingly becoming not just an option but a necessity. Labs sticking with purely manual or record-only LIS systems may find themselves struggling to keep up with the pace of modern healthcare and diagnostics. In contrast, labs that invest in AI-enabled, next-generation LIS software platforms are positioning themselves to deliver faster service, adapt quickly to new challenges (like surges in testing or new types of assays), and provide greater value to physicians and patients through data-driven insights.

In practical terms, transitioning to a system-of-action LIS should be done thoughtfully. It involves not just new pathology lab software, but also training staff, updating workflows, and continuously refining the AI rules/algorithms based on feedback. However, the effort is well worth the payoff. The laboratory of the future - and increasingly of the present - is one where information seamlessly turns into action. By leveraging an advanced LIS system that can analyze, decide, and even act, lab managers can ensure their operations are efficient, resilient, and ready for whatever comes next. As we move forward, the laboratories that harness these tools will likely set the standard for operational excellence, much like early adopters of LIS systems once led the move away from paper. Now, the journey is toward intelligent automation and actionable insights - a journey that promises to redefine how labs contribute to healthcare delivery.