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Understanding Reference Ranges: How Are They Established and Why Do They Matter?

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When you run testing for a patient, you get results or numbers. How do you interpret those numbers? You use reference ranges to help define normal versus abnormal results. This is why reference ranges are so important. The way reference ranges are established is even more important. Have you ever wondered where these numbers actually come from? Why might the same test have different reference ranges at different laboratories? Does a result outside the reference range automatically mean something is wrong with the patient? Reference ranges are more than just numbers printed beside a patient’s results. They are an important part of laboratory testing, helping healthcare providers interpret results and make informed decisions about patient care. What Are Reference Ranges? A reference range, also known as a reference interval, is a set of values used to help determine whether a patient’s laboratory result falls within an expected range. Reference intervals are typically established by collecting and analyzing samples from a group of individuals considered to be healthy. The results are then evaluated statistically to determine the range of values expected for that population. Most conventional reference intervals represent the central 95% of values from a selected reference population (ASCLS, 2019; Centra Diagnostics, n.d.). What does that actually mean? It means that approximately 95% of healthy individuals in that population are expected to have results within the reference interval. The remaining 5% may have results outside the interval, even though they are healthy. Reference intervals only provide a starting point for interpreting results. They should always be considered alongside the patient’s clinical history, symptoms, and other laboratory findings (Centra Diagnostics, n.d.). How Are Reference Ranges Established? Reference intervals are not randomly selected. Establishing them involves several steps, including selecting an appropriate reference population, collecting and analyzing specimens, and using statistical methods to determine the expected distribution of results (Higgins, 2009). Step 1: Selecting a Reference Population The first step is selecting individuals who represent the population for which the reference interval will be used. These individuals are generally considered healthy based on predetermined inclusion and exclusion criteria (NIH/PMC, n.d.). Age, sex, pregnancy status, and other physiological characteristics may influence the results. Depending on the analyte, separate reference intervals may be necessary for different patient populations. For example, think about hemoglobin. The expected values for a newborn differ from those for an adult. Using the same reference interval for both populations could lead to incorrect interpretation (Centra Diagnostics, n.d.). This is why selecting the appropriate reference population is such an important part of the process. Step 2: Collecting and Testing Samples Once the reference population has been selected, specimens are collected and analyzed. For the traditional nonparametric method, guidelines generally recommend collecting samples from at least 120 reference individuals to establish a reference interval. This sample size allows laboratories to estimate the central 95% of results and calculate confidence limits around the upper and lower reference limits (Higgins, 2009; NIH/PMC, n.d.). Preanalytical variables must also be controlled. These include: For example, cortisol concentrations naturally fluctuate throughout the day. If specimens are collected at inconsistent times, those differences could influence the distribution of results. Standardizing collection and testing conditions helps ensure that the resulting reference interval reflects the intended population rather than avoidable variations in specimen collection or handling. Step 3: Using Statistics to Establish the Reference Interval After the specimens have been analyzed, the laboratory uses statistical methods to determine the reference interval. One commonly recommended approach is the nonparametric method. With this approach, the laboratory arranges the results from lowest to highest and identifies the values corresponding to the 2.5th and 97.5th percentiles. These two values become the lower and upper reference limits (Higgins, 2009; NIH/PMC, n.d.). Imagine you have laboratory results from 120 healthy individuals. After arranging the results from lowest to highest, you identify the values that represent the central 95% of the population. Approximately 2.5% of the values fall below the lower limit, and another 2.5% fall above the upper limit. The values between those limits form the reference interval. It is important to remember that this statistical process identifies what is typical for the selected population, not necessarily what is ideal for every patient’s health. Establishing vs. Verifying Reference Intervals: What’s the Difference? Not every laboratory establishes its own reference intervals. Establishing reference intervals requires considerable time, resources, and access to an appropriate reference population. Many laboratories adopt reference intervals provided by manufacturers or established through published studies. This does not mean laboratories should automatically accept those intervals without proper evaluation. This is where reference interval verification comes in. Establishing a reference interval means generating the data needed to determine the limits. Verifying a reference interval means evaluating whether an existing interval is appropriate for the laboratory’s method and patient population. According to the CLSI approach discussed by NIH, a laboratory can use a verification procedure involving 20 appropriately selected reference individuals. If no more than two of the 20 results fall outside the proposed reference interval, the interval may be considered acceptable under that procedure. If three or more results fall outside the interval, further investigation and additional data may be necessary. This is a more practical way to evaluate existing reference intervals without conducting a new study. Why Do Reference Ranges Differ Between Laboratories? Have you ever noticed that two laboratories may report slightly different reference intervals for the same test? This does not mean one laboratory is wrong. Reference intervals can vary for several reasons. 1. Testing methods and instrumentation Different analyzers, reagents, and analytical methods may produce slightly different results. Therefore, a reference interval established using one method may not always be appropriate for another. 2. Patient populations The population used to establish a reference interval matters. Age, sex, pregnancy, and other physiological characteristics may influence expected laboratory values. 3. Specimen type Serum, plasma, and whole blood are not always interchangeable. Depending on the analyte, the specimen type can influence the measured result and the reference interval. 4. Preanalytical variables Fasting status,

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A Root Cause Analysis Discussion

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Author: Darryl Elzie, PsyD, MHA, MLS(ASCP), CQA(ASQ) Errors Are Technical Events and Organizational Signals Clinical laboratory errors are often described as technical failures: a specimen was mislabeled,quality control was not reviewed, an instrument alarm was overlooked, or an incorrect resultwas released. For laboratory management, however, each event also represents informationabout the system in which employees work. Workload, staffing, communication, technology,procedure design, supervision, competing priorities, and workplace culture all influencewhether a process is performed reliably. Laboratory medicine literature has long shown that risk extends across the entire testingprocess rather than remaining confined to the analytical phase. Preanalytical and postanalyticalvulnerabilities frequently involve handoffs, communication, identification, data entry, resultinterpretation, and other human-dependent processes. These are precisely the areas in whichmanagement systems and behavioral conditions have substantial influence. 1 A developed corrective-action program, therefore, does more than restore normal operations.It identifies why the event occurred, why existing controls did not prevent or detect it, andwhat changes will make the process more reliable over time. This expectation is also reflectedin CLIA requirements for corrective-action procedures and for evaluating whether correctiveactions were effective and whether policies must be revised to prevent recurrence. 2,3 This isthe core of truly continuous quality improvement. Correction Is Not Corrective Action A correction addresses the immediate condition. The laboratory may recollect a specimen,amend a result, repeat a quality-control run, remove expired material, or quarantine anaffected product. These actions may be necessary to protect patients/donors and restoreoperations, but they do not necessarily reduce the probability of recurrence. Corrective action addresses the cause of the nonconformity. Root cause analysis is thestructured process used to identify the technical, behavioral, environmental, and organizationalfactors that permitted the event to occur. The distinction matters because laboratories areoften under pressure to resume testing quickly. Once the immediate problem has beenresolved, leaders may experience a premature sense of closure. Without disciplined follow-through, the investigation can become a documentation exercise rather than a mechanism fororganizational learning. (However, it should be noted that accurate and completedocumentation is always required.) Why “Human Error” Is an Incomplete Root Cause Human error is often presented as a causal factor, but it usually describes what happenedrather than why. Reason’s systems approach distinguishes between a person-focused response,which concentrates on lapses, inattention, or carelessness, and a system-focused response,which examines the conditions under which people work. The systems approach does notremove accountability; it recognizes that predictable human limitations must be consideredwhen designing safe processes. 4 Attention declines during repetitive work. Working memory is disrupted by interruptions.Fatigue impairs judgment. Confirmation bias can cause staff to interpret information in a waythat supports an initial assumption. Poorly designed screens, excessive alarms, similar labels,and complex procedures increase cognitive load. When a critical process depends primarily onvigilance, memory, and perfect compliance, the system has been designed with limitedtolerance for normal human variability. For this reason, instructions to “be more careful” are weak corrective actions. A strongerresponse will include real change, such as barcode verification, bedside label printing,automated quality-control lockouts, hard stops, simplified workflows, physical separation oflook-alike materials, or independent review of high-risk steps. Training is appropriate when agenuine knowledge or competency deficit is demonstrated, but it should not be used tocompensate for poor process design. Psychological Safety Determines What Leaders Learn Root cause analysis is only as strong as the information available to the investigation team. Staffare more likely to report errors, near misses, uncertainty, and unsafe workarounds when theybelieve they can speak honestly without automatic humiliation, blame, or punishment.Psychological safety is the shared belief that interpersonal risk-taking, such as admitting amistake, challenging a decision, or raising a concern, is acceptable within the team. 5 Research in healthcare has associated psychological safety with speaking up, learning behavior,and willingness to report errors, although recent reviews also reveal significant complexity inthe relationships between reporting rates and safety outcomes. A higher number of reportsmay reflect a less safe environment, or a healthier reporting culture that makes existing risksvisible. Laboratory managers should therefore avoid judging culture solely by the number ofevents reported. 6-9 Leadership response is pivotal. When managers react with anger, public criticism, or immediatedisciplinary action, staff learn that reporting carries personal risk, leading to events beingminimized, reframed, or entirely unreported. Near misses may often completely disappear. Conversely, leaders who begin with curiosity are more likely to obtain an accurate account ofwhat really happened. Use Just Culture Without Eliminating Accountability A just-culture framework helps managers differentiate human error, at-risk behavior, andreckless behavior. Human error may require process redesign, coaching, or stronger barriers.At-risk behavior requires understanding why a shortcut appeared reasonable, efficient, andacceptable. Reckless behavior requires disciplinary action when an individual knowinglydisregards a substantial and unjustifiable risk. The distinction prevents two management failures: punishing every deviation as misconduct orexcusing every action as a system problem. Effective accountability examines the behavior, theavailable choices, the staff’s understanding of risk, and how the organization has responded tocomparable behavior in the past. Consistency is essential. Staff need to believe consequencesare not dependent on hierarchy, popularity, or the outcome of the event. Conduct Investigations as Learning Processes The immediate management priority is containment. Potentially affected specimens, reagents,records, results, or blood products should be identified and controlled. The laboratory mustevaluate patient or product impact, preserve electronic and paper evidence, notify appropriateleadership, and define the preliminary scope. The problem statement should describe what occurred without assuming a cause. “Staff failedto follow the procedure,” prematurely closes the inquiry. A stronger statement identifies whathappened, what should have happened, when and where the event occurred, how it wasdetected, and the actual or potential impact. Investigation teams should review procedures, training records, competency assessments,instrument data, audit trails, staffing, workload, environmental conditions, previous events, andsystem interfaces. Direct observation is particularly important because written proceduresdescribe work as imagined, while staff may perform work differently in response to realoperating conditions. Frontline staff should be included as subject-matter experts. Interviews should be structured asinformation-gathering conversations rather than interrogations. Questions such as “Whatnormally happens at this step?”, “What made the correct action difficult?” and “How is thiswork completed during high-volume periods?” are more likely to expose meaningful systemweaknesses than asking, “Why didn’t

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The Science Behind Delta Checks: How Laboratories Detect Errors Before They Reach the Patient

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By Amari Henderson, MBA, MLS (ASCP) Imagine you’re reviewing the morning laboratory results when one patient’s potassium immediately catches your attention. Yesterday, their potassium was 4.1 mmol/L. Today, it’s 7.0 mmol/L. Your first thought probably isn’t to report the result right away. You wonder if the patient suddenly developed severe hyperkalemia overnight. Was the specimen mislabeled? Could the sample be hemolyzed? Was there an issue during collection? Situations like this are exactly why delta checks were created. Before laboratory results are released to healthcare providers, clinical laboratories use verification tools to determine whether an unexpected change deserves a closer look. A delta check is defined as a post-analytical verification tool used to compare the difference between a patient’s current and previous laboratory results against a predefined limit. This patient-specific quality tool helps identify potential errors at the individual patient level. If the difference exceeds the laboratory’s established threshold, the result is flagged for review before it is reported. The concept of delta checks was first introduced by Dr. Donald A.B. Lindberg in 1967 as laboratory computer systems and informatics began to evolve. By 1974, delta checks had become an established patient-based quality control tool and have remained an important safeguard for detecting potential errors before results are reported. Today, most laboratory information systems (LIS) and autoverification workflows incorporate delta check rules to automatically identify results that may require additional investigation before they are released. Not only are delta checks associated with specimen misidentification, they also help identify specimen integrity issues such as intravenous (IV) fluid contamination, incorrect collection tubes, or delayed specimen processing. Selecting effective delta check rules has become more important for maintaining patient safety as well as operational efficiency. Delta checks are based on the simple fact that most lab values do not change dramatically over a short period of time unless there is a biological or clinical reason. The concept of homeostasis proves that the body strives to maintain a stable internal environment. This is why many analytes remain stable within the same individual from day to day. One of the main scientific concepts behind delta checks is biological variation. The CLSI EP33 guideline describes biological variation as having two components. Within-subject biological variation (CVI) represents the normal day-to-day fluctuations around an individual’s typical laboratory value. Between-subject biological variation (CVG) refers to the normal differences in laboratory values between different people. This explains why two healthy patients may have different baseline creatinine values while each person’s creatinine remains relatively stable over time. Analytes with low within-subject biological variation are excellent candidates for delta checks because changes are easier to recognize. According to the CLSI EP33 guideline, the most effective delta check analytes are also ordered frequently. Frequent testing allows for meaningful comparisons between current and previous results. Hemoglobin, hematocrit, sodium, potassium, platelet count, and white blood cell count are all examples of analytes that are perfect for delta checks. Every laboratory serves a different patient population and uses different instrumentation, workflows, and testing practices. This is why delta check limits should be established using the laboratory’s own patient data whenever possible rather than relying solely on published recommendations. Laboratories are encouraged to periodically review and change their delta check rules to improve error detection while minimizing unnecessary investigations. Thresholds that work well in one hospital could lead to excessive false alerts in another hospital or patient population. A failed delta check does not automatically mean a laboratory error has occurred. It just means that the result deserves a closer look. Laboratory professionals should review previous patient results, patient history, specimen integrity, collection information, instrument quality controls, and instrument flags. Only after this investigation is complete can the laboratory determine whether the result reflects a true clinical change or whether additional action is necessary. Delta checks are far more than just a comparison between two results. They combine biological variation, analytical performance, laboratory quality management, and clinical judgment into one patient safety tool. Delta checks provide laboratories with an additional layer of quality assurance before results are reported. More than 50 years later, delta checks remain one of the most effective patient-based quality tools for helping laboratories detect potential errors, improve result accuracy, and protect patients. Sources Asirvatham JR, Moses V, Bjornson L. Errors in potassium measurement: a laboratory perspective for the clinician. N Am J Med Sci. 2013 Apr;5(4):255-9. doi: 10.4103/1947-2714.110426. PMID: 23724399; PMCID: PMC3662091. Clinical and Laboratory Standards Institute (CLSI). Use of Delta Checks in the Medical Laboratory; Approved Guideline, 2nd ed. CLSI document EP33. Wayne: CLSI; 2023. Markus, C., Tan, R. Z., & Loh, T. P. (2021). Evidence-based approach to setting delta check rules. Critical Reviews in Clinical Laboratory Sciences, 58(1), 49–59. https://doi.org/10.1080/10408363.2020.1800585 Straseski, J. A. (n.d.). The delta check in action: Causes and consequences of discrepant laboratory results. ARUP Laboratories. https://arup.utah.edu/education/deltaChecks.php/ Yenice, S. (2017, October 22). Use of delta checks: A requisite method in quality control [Conference presentation]. Satellite Educational Workshop on Intelligent Clinical Laboratory Management: Impacts on Quality System Improvement, Durban, South Africa. International Federation of Clinical Chemistry and Laboratory Medicine (IFCC)

Press Release

Lab Symplified and Lighthouse Lab Services Announce Strategic Partnership

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A strategic alliance to help laboratories strengthen inventory operations, improve compliance, and run more efficiently Lab Symplified, a modern laboratory operations software platform built to streamline inventory, quality and compliance processes, is proud to announce a strategic partnership with Lighthouse Lab Services, a trusted provider of laboratory consulting, recruiting, and operational support solutions. Laboratories today are under growing pressure to do more with less. Teams are expected to manage rising testing demands, tighter budgets, staffing constraints, and increasing compliance expectations — all while keeping operations running smoothly. Behind the scenes, many labs still rely on manual processes to manage inventory, reagents, and quality workflows, which can lead to stockouts, expired materials, inconsistent documentation, unnecessary waste, and increased compliance risk. Through this partnership, Lab Symplified and Lighthouse Lab Services will help laboratories address these challenges more effectively by combining operational consulting expertise with modern software built specifically for laboratory workflows. Lab Symplified’s platform helps laboratories digitize and streamline inventory and reagent management with tools designed to improve visibility, accountability, and control across locations and teams. From tracking reagent inventory and expiration dates to supporting lot-level traceability, stock monitoring, and day-to-day supply management, the platform is designed to reduce manual work and help labs maintain stronger operational readiness. In addition, Lab Symplified supports quality management workflows that help laboratories strengthen compliance and standardization. Its QMS-related capabilities support important processes such as lot tracking, QC documentation, and more structured workflow controls that improve traceability and consistency. By moving these activities from paper-based or fragmented systems into a centralized digital platform, laboratories can improve documentation, strengthen audit readiness, and reduce the risk of missed or inconsistent compliance steps. Lighthouse Lab Services brings deep industry expertise in helping laboratories launch, optimize, and scale. Their team supports organizations across a wide range of operational needs, including consulting, staffing, instrument services, quality, and strategic growth initiatives. By partnering together, Lab Symplified and Lighthouse Lab Services can offer laboratories a more complete path to improving operational performance. “This partnership reflects our shared commitment to helping laboratories run more efficiently and with greater confidence,” said Pradeep Bhandari, CEO of Lab Symplified. “Inventory and quality management processes are still heavily manual in many labs, creating avoidable waste, inefficiency, and compliance challenges. By working with Lighthouse Lab Services, we can help more laboratories implement practical, high-impact solutions that strengthen both operations and quality systems.” “Lighthouse Lab Services works closely with laboratories that are looking for the right operational partners to support growth and performance,” said Elliot Labovitz, Chief Sales Officer. “Lab Symplified brings a focused and valuable solution for inventory, reagent management, and quality-related workflows, and we are excited to work together to support laboratories in these critical areas.” The partnership is intended to make it easier for laboratories to access solutions that improve operational discipline without adding unnecessary complexity. By pairing Lighthouse Lab Services’ advisory capabilities with Lab Symplified’s software, the two organizations aim to help labs build stronger infrastructure for scalable, efficient, and compliant operations. As laboratories continue to look for ways to control costs, improve traceability, and reduce administrative burden, better inventory, reagent, and quality management will remain a critical part of that effort. Lab Symplified and Lighthouse Lab Services are excited to work together to support laboratories in meeting those goals. About Lab Symplified Lab Symplified is a laboratory operations software company focused on helping laboratories replace manual processes with modern, easy-to-use digital workflows. The platform supports core operational areas such as inventory and reagent management, as well as an electronic log platform that enables laboratories to manage equipment maintenance, quality control, safety documentation, task workflows, shift reporting, proficiency testing, and compliance activities within a centralized system. About Lighthouse Lab Services Lighthouse Lab Services provides consulting, recruiting, and operational support for laboratories across the United States. The company helps labs start, optimize, and grow through services spanning staffing, laboratory consulting, instrument support, and quality-related.

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Building a More Engaged Laboratory Team in High-Stress Environments

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By Amari Henderson, MBA, MLS (ASCP) The medical laboratory profession can, at times, feel complicated and overwhelming. If we are being honest, many healthcare professions can feel this way when you connect each piece of your job to a living, breathing patient who is relying on you to produce quality work. The major question is: how do you keep a team engaged in a high-stress work environment? Engaged employees tend to have an emotional connection to their organization, which leads to higher retention and lower turnover rates. Motivation is crucial to employee engagement and overall workplace satisfaction. Motivational levels are related to job satisfaction and are influenced by factors such as leadership style, financial compensation, and career development opportunities. Studies show that extrinsic motivational factors such as pay, can have a slightly greater influence than intrinsic motivation factors. This highlights the importance of ensuring staff are fairly compensated, consistently recognized, and provided with opportunities for advancement. Leadership plays a critical role in shaping engagement. Leaders who are supportive, transparent, and actively involved in the work environment contribute to higher motivation levels among employees. Immerse yourself in each part of the work environment, perform regular rounding, and take note of what can be improved. Staff do not want to feel like leadership is placing a temporary “bandage” over deeper issues while attempting to increase engagement but ignoring the major challenges they face daily. Focus on implementing real, measurable changes. Avoid making false promises or simply telling staff what they want to hear. Be open, honest, and accountable. A lack of recognition and reward can also contribute to low motivation and disengagement. Personalized messages acknowledging staff for their accomplishments can foster a sense of purpose and belonging. Make it a priority to show up to each department regularly and recognize what is going well. Nominate staff for facility-wide awards and highlight their contributions. It also helps to present opportunities for growth such as conferences, continuing education programs, and scholarship opportunities. These opportunities can enhance both their professional development and overall quality of life. Effective communication is another key driver of engagement. Promote and practice open, honest, and clear communication. Utilizing structured communication tools such as SBAR (Situation, Background, Assessment, Recommendation) can improve clarity and efficiency. Create opportunities for regular one-on-one meetings or check-ins, even if they are brief, to allow employees to voice concerns and feel heard. At the same time, be mindful of information overload, as excessive communication without clear direction can be counterproductive. Regularly gathering feedback from employees is essential to evaluating the effectiveness of engagement strategies and making necessary adjustments. Finally, provide respect and empathy in all interactions. An employee’s emotional state is closely tied to their level of motivation and engagement. When employees begin to withdraw or feel the need to conceal their identities, ideas, or concerns, they often become disengaged and defensive. This not only impacts morale but can also negatively affect job performance and overall team dynamics. Creating a psychologically safe environment where employees feel valued, respected, and supported can significantly improve both engagement and performance outcomes. References Karaferis, D., Aletras, V., Raikou, M., & Niakas, D. (2022). Factors Influencing Motivation and Work Engagement of Healthcare Professionals. Materia socio-medica, 34(3), 216–224. https://doi.org/10.5455/msm.2022.34.216-224 Osborne, S., & Hammoud, M. (2019). Effective Employee Engagement in the Workplace. International Journal of Applied Management and Technology, 16(1), 50–67. https://doi.org/10.5590/IJAMT.2017.16.1.04 Scholarworks, S., & Treadway, J. (2021). Trust-Building Strategies Laboratory Managers Use to Cultivate Trust-Building Strategies Laboratory Managers Use to Cultivate the Engagement of Laboratory Employees the Engagement of Laboratory Employees. https://scholarworks.waldenu.edu/cgi/viewcontent.cgi?article=12703&context=dissertations

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What Laboratories Should Expect from the Latest CAP Accreditation Checklist Updates

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By Amari Henderson, MBA, MLS (ASCP) The College of American Pathologists (CAP) continues to refine its Laboratory Accreditation Program checklists to reflect the evolving realities of modern laboratory medicine. The most recent checklist updates, released in late 2025 and implemented in laboratories during 2026 inspections, emphasize patient safety, digital workflows, and regulatory clarity. For laboratories preparing for inspections or reviewing internal policies, understanding these updates is essential. While many checklist revisions focus on clarifications and modernization rather than entirely new requirements, several changes will impact daily laboratory operations, documentation practices, and inspection readiness. Below is an overview of key themes and changes laboratories should be aware of. Expansion of Digital Pathology and Remote Data Assessment One of the most notable updates involves the expansion of guidance surrounding digital pathology and remote data assessment. As laboratories increasingly adopt digital imaging, remote slide review, and telepathology workflows, CAP has expanded checklist language to address these technologies more clearly. The Laboratory General checklist now explicitly includes digital pathology within its scope, emphasizing the need for laboratories to ensure proper validation, quality monitoring, and oversight of digital systems used for interpretation. Laboratories utilizing digital workflows should ensure they have documented: Additionally, recent regulatory clarification indicates that laboratories performing remote cytology image review outside of the primary laboratory location may require an additional CLIA certificate for those locations by March 2026. Laboratories should verify whether any remote interpretation workflows fall under this requirement. New Requirements for Patient Self-Collected Specimens Another significant update addresses the growing use of patient self-collection for diagnostic testing. CAP introduced a new Phase II checklist requirement addressing specimens collected directly by patients, either independently or with caregiver assistance. This change reflects the increasing use of self-collected specimens in molecular testing, infectious disease screening, and women’s health diagnostics. Laboratories offering tests that involve patient-collected samples must ensure they provide clear written instructions for specimen collection, including information on factors that may affect specimen quality and test reliability. Examples include: Laboratories should review patient instructions, educational materials, and specimen acceptance policies to ensure they align with CAP expectations. Regulatory Clarifications and Modernized Guidance Several checklist updates also aim to simplify compliance and align with recent CMS regulatory clarifications. CAP has revised certain checklist language to reduce ambiguity around personnel requirements, documentation expectations, and operational processes. These updates are intended to help laboratories interpret requirements more consistently while maintaining compliance with CLIA and other regulatory frameworks. While many of these revisions do not introduce entirely new requirements, they may still require laboratories to review existing procedures and ensure documentation reflects the updated language. Updates to Inspection and Accreditation Processes In addition to checklist updates, laboratories may notice operational changes related to CAP accreditation activities. For example: These adjustments are intended to streamline administrative processes while preserving the integrity of the accreditation program. What Laboratories Should Do Now To prepare for upcoming inspections and maintain compliance, laboratory leaders should consider the following steps: Review customized CAP checklistsEach laboratory receives a customized checklist based on its test menu and specialties. These checklists are available through CAP’s e-LAB Solutions Suite (ELSS). Evaluate digital pathology workflowsLaboratories using digital imaging, telepathology, or remote data interpretation should confirm that validation studies, training documentation, and quality monitoring procedures are in place. Review policies for patient-collected specimensIf your laboratory performs testing involving self-collected samples, ensure that clear written instructions and specimen acceptance criteria are documented. Maintain self-inspection documentationEven though the verification form may not need to be submitted routinely, laboratories must still perform interim self-inspections and retain records. Monitor regulatory updatesCAP frequently publishes regulatory updates and inspection guidance to help laboratories stay aligned with evolving standards. Key Takeaways for Laboratory Professionals The latest CAP checklist updates reflect broader shifts occurring across laboratory medicine including digital transformation, decentralized testing models, and increasing regulatory complexity. Rather than introducing sweeping new requirements, many of these updates focus on clarifying expectations and ensuring that laboratories maintain strong quality systems as testing environments evolve. For laboratory professionals, staying informed and proactively reviewing internal processes can make the difference between a smooth inspection and a stressful one. CAP-accredited laboratories should regularly review checklist updates, regulatory announcements, and educational resources to ensure continued compliance and readiness.

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Artificial Intelligence in the Clinical Laboratory

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Darryl Elzie, PsyD, MHA, MLS (ASCP), CQA (ASQ)Amari Henderson, MBA, MLS (ASCP) Current Applications of Artificial Intelligence in the Clinical Laboratory Artificial intelligence (AI) has transitioned from an experimental concept to an operational reality in the clinical laboratory. Unlike traditional rule-based automation, AI systems utilize machine learning techniques to analyze data gathered from historical records, identifying patterns, detecting anomalies, and supporting clinical and operational decision-making (Dodig, 2025; Yahya, 2025). The primary goal of laboratory AI applications is to function as decision-support tools, augmenting professional judgment. At-a-Glance: Where AI Fits in the Clinical Laboratory Workflow Phase AI/ML Use Cases Data Inputs Operational Outcomes Pre-analytical Order checks, specimen routing, TAT prediction Orders, timestamps,logistics Reduced errors,improved TAT Analytical QC anomaly detection, drift detection, image analysis QC data, analyzer flags, images Earlier failure detection, increased throughput Post-analytical Autoverificationsupport, delta checks Results, prior values Consistent result release, proper notification Operations Workload forecasting,inventory optimization Volume and staffing data Improved throughput,waste reduction In the pre-analytical phase, AI tools are used to flag duplicate or inappropriate orders (an area of challenge for some medical providers), predict specimen transport delays, and optimize collection workflows. These systems are designed to reduce redraws and delays; however, they remain dependent on standardized front-end processes and human beings. AI cannot compensate for poor specimen identification or inconsistent acceptance criteria. Analytical applications represent one of the fastest-growing areas of AI adoption, especially in quality control. Machine learning models analyze multivariate QC data to identify subtle analyzer drift or reagent shifts that may precede traditional QC rule violations. In hematology, microbiology, and pathology, AI-driven image analysis supports cell classification, culture plate reading, and slide triage (Hanna et al., 2025; Sabelli, 2023). Analytical applications represent one of the fastest-growing areas of AI adoption, especially in quality control. Machine learning models analyze multivariate QC data to identify subtle analyzer drift or reagent shifts that may precede traditional QC rule violations. In hematology, microbiology, and pathology, AI-driven image analysis supports cell classification, culture plate reading, and slide triage (Hanna et al., 2025; Sabelli, 2023). Post-analytical AI focuses on consistency and safety. Enhanced delta-checking models incorporate patient-specific baselines, while probabilistic autoverification tools assist in prioritizing results requiring human review. These systems remain bounded by laboratory policy and regulatory expectations for oversight (FDA, 2025). Governance, Risk, and the Human Side of AI Adoption Despite measurable benefits, AI adoption introduces new categories of risk. Machine learning models reflect the data on which they are trained and may perpetuate bias related to patient populations, specimen handling practices, or historical testing patterns (NIST, 2023). Models are susceptible to cultural and systemic biases. Performance may degrade over time due to workflow changes or shifts in patient demographics, necessitating ongoing monitoring and reevaluation. Operational tradeoffs must also be acknowledged. AI systems require integration with laboratory information systems, additional validation effort, and formal change control. While AI may reduce manual review, it often redistributes workload toward exception management, monitoring, anddocumentation rather than eliminating labor entirely. It is incumbent on organizations to have adequately trained staff in problem-solving and root cause analysis. Equally important are the psychological impacts on laboratory professionals. Established change theories, such as Lewin’s Change Model and Kotter’s Eight-Step Process, demonstrate that uncertainty and perceived loss of control are primary drivers of resistance. Laboratory staff may fear deskilling (a demonstrable risk in pathology), increased surveillance, or erosion of professional autonomy if AI is poorly introduced. Prior scholarship on reshaping organizational culture in pathology emphasizes that sustainable change is rooted in trust, psychological safety, and alignment between stated values and operational behavior (Elzie, 2025). The adoption of artificial intelligence follows these same principles. When laboratory professionals understand how decisions are generated, retain authority to override AI outputs, and observe leadership modeling responsible and ethical use, technology functions as an enabler of professional practice rather than a perceived threat. Successful laboratories frame AI as a tool automating low-value, repetitive tasks and enhancing professional judgment. Transparent communication, structured training, and visible leadership engagement help normalize AI as part of continuous improvement rather than disruptive replacement. Ultimately, AI should be governed like any other critical laboratory process: with a clearly defined intended use, risk assessment, validation, change control, and ongoing performance monitoring. When aligned with a strong organizational culture, AI can enhance both operational resilience and professional satisfaction. References Alhamzi Nasser Yahya. Artificial 1Intelligence (AI) Applications in Clinical Laboratories: A Narrative Review. Biomed J Sci & Tech Res 62(5)-2025. BJSTR. MS.ID.009813. Dodig, S. (2025). Are we ready to integrate advanced artificial intelligence models into clinical laboratories? Biochemia Medica, 35(1), 010501. Elzie, D. (2025). Reshaping organizational culture in pathology. Clinics in Laboratory Medicine, 45(3), 383–396. https://pubmed.ncbi.nlm.nih.gov/40818874/ Food and Drug Administration. (2025). Artificial intelligence in software as a medical device (SaMD). U.S. Food and Drug Administration. Hanna, M. G., et al. (2025). Future of artificial intelligence—Machine learning trends in pathology and medicine. Modern Pathology. National Institute of Standards and Technology. (2023). AI risk management framework (AI RMF 1.0). National Institute of Standards and Technology. Sabelli, Chiara. (2023). An AI system helps microbiologists identify bacteria. Nature Italy. 10.1038/d43978-023-00167-9.

Blog, Laboratory General

Always Audit-Ready: Why Digital Maintenance Logs Are a Must for Modern Labs

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Understanding the Context Behind the Divide In many clinical and research laboratories, essential routines such as maintenance logs, equipment calibration records, temperature logs, daily operational checklists, quality control, and training sign-offs still live in paper binders or scattered spreadsheets. It may feel manageable, but the hidden cost of these “home-grown” systems grows daily. Whether you’re a bench technologist, supervisor, manager, or lab director, you’ve likely faced the same challenges — forgotten tasks, incomplete documentation, illegible records, and the scramble to find reports before an audit. The Real Cost of Staying Manual When laboratories rely on paper or Excel instead of a dedicated digital maintenance log platform, inefficiencies multiply. 1. Missed or delayed tasks Technologists juggle multiple responsibilities. When reminders rely on memory, things get missed. Managers often discover overdue work too late, forcing last-minute fixes and compliance stress. 2. No data validation or accuracy Paper systems can’t confirm if data was entered correctly or on time. Errors often surface during review, leading to corrective actions, deviation reports, and wasted staff hours correcting preventable mistakes. 3. Disorganization and wasted time (1981–1996)Logs stored in different folders or binders make it impossible to see the full picture. Supervisors spend hours flipping through pages or spreadsheets instead of focusing on quality oversight. 4. Lack of visibility and accountabilityWithout a centralized laboratory dashboard, leaders can’t instantly see overdue tasks, incomplete logs, or recurring quality trends. The result is reactive management instead of proactive control. 5. Compliance gaps Handwritten or scattered records can be misplaced, faded, or incomplete. During inspections, missing records or incomplete documentation can quickly trigger audit findings. 6.Operational inefficiencyFragmented systems cause duplicate data entry, manual reconciliation, and poor traceability , slowing down workflows and clouding performance insights. A Unified Platform for Every Task and Every Role Modern labs need more than a digital replica of paper logs. They need a centralized, role-based system to manage all routine tasks,  maintenance, operations, quality, training, and task assignments. That’s exactly what Lab Symplified delivers. Lab Symplified’s  cloud-based digital lab management platform allows your team to: ·  Digitise every checklist and log (routine and preventative maintenance logs, checklist, temperature logs,training sign-offs etc).·  Assign tasks with built-in reminders and escalation so nothing falls off the radar.·  Support electronic sign-off and review workflows with full traceability.·  Upload attachments (calibration reports, service reports, pictures etc) tied to each log entry.·  Instantly generate audit-ready reports.·  Move from fragmented spreadsheets to one secure, role-based platform. Why Electronic Logs Beats “Home-Grown” Systems Paper and spreadsheets are passive. They don’t remind, assign, or validate. A digital maintenance log system does: ·  Automates accountability : Set tasks once; they stay live with alerts and dashboards.·  Standardizes workflows : Every technologist and department follows the same process.·  Provides real-time insight : Managers know exactly what’s complete or overdue.·  Delivers instant audit readiness : One click shows who did what, when, and with what evidence.·  Reduces cost : Fewer audit findings, repeat tests, and wasted hours chasing records. How Lab Symplified Closes the Loop Lab Symplified unifies maintenance logging, task management, and quality oversight in one secure, cloud-based platform built specifically for labs, not generic office software. Whether you’re running a small hospital lab or a multi-site network, Lab Symplified scales with your workflow, assigns by role, and provides real-time oversight. The moment you eliminate paper, you regain control, tasks get done, records are accurate, training is documented, and audits become stress-free. If your lab still relies on binders or spreadsheets, the cost is already adding up.With Lab Symplified, you gain clarity, compliance, and efficiency, all in one electronic maintenance log system.

Blog, Laboratory General

The Silent Divide: How Generational Tensions Are Affecting Laboratory Teams

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Understanding the Context Behind the Divide By Amari Henderson, MBA, MLS (ASCP) Generational tensions can be a sensitive but very real issue within clinical laboratories today. These divides and their impact on workplace culture, mentorship, and retention are increasingly worth discussing. Clinical laboratories are facing a workforce crisis, with experienced professionals retiring and younger scientists entering the field with new expectations, communication styles, and learning preferences. Many newer MLS professionals report feeling unwelcome or undervalued by their older colleagues, while senior staff express frustration with changing work attitudes, a perceived lack of resilience, or a dependence on technology. How can laboratories acknowledge these frustrations while fostering an environment that encourages open communication, mutual respect, and adaptability? First, it’s essential to understand that context, culture, and personal experience matter far more than generational assumptions. Generalizing characteristics to large groups can backfire as some individuals feel mischaracterized or pigeonholed when labeled by their generation (Bentley University, 2025). In a lab setting, applying generational labels like “Boomers resist change” or “Gen Z doesn’t want to work” can hinder collaboration or silence valid feedback. Encouraging teams to see each other as individuals first, rather than as stereotypes, builds psychological safety and creates space for genuine communication. As Johns Hopkins University (2022) emphasizes, values shift over time. Work ethic, loyalty, communication preferences, and adaptability are shaped not only by when someone was born, but also by their lived experiences, training environments, and evolving workplace norms. Generations in the Laboratory Workforce Today’s clinical laboratories often host a mix of mainly four generations. Each generation is shaped by the era they grew up in and the rapid evolution of technology and work culture (Bentley University, 2025). While it’s helpful to understand broad traits, these are not absolutes, and individuals within each generation vary widely. Baby Boomers (1946–1964)·  Defined by strong work ethic, institutional loyalty, and face-to-face communication·  Often supervisors or senior techs·  May rely on manual processes and established routines, sometimes skeptical of rapid tech adoption Generation X (1965–1980)·  Independent, pragmatic, and adaptable, bridging analog and digital eras·  Serve as natural mediators between older and younger staff·  Value efficiency and autonomy in workflow Millennials (1981–1996)·  Purpose-driven, tech-comfortable, and collaboration-focused·  Value flexibility and feedback·  Often advocate for digital transformation and innovation in processes·  Appreciate clear communication and mentorship Generation Z (1997–2009)·  Digital natives who expect fast access to data and transparency·  Value inclusivity and work-life balance·  Quickly adapt to new lab software and automation·  Prefer streamlined workflows and visual dashboards Recognizing these characteristics helps teams understand tendencies, rather than defining individuals. A healthy workplace acknowledges diversity within generations and encourages cross-generational curiosity rather than criticism. Where the Disconnect Begins Generational tension in the lab often stems from differing approaches to learning, feedback, and technology: ·  Training and Mentorship: Senior staff may feel new hires lack patience or initiative, while newer MLS professionals may feel dismissed or micromanaged.·  Technology Adoption: Statements like “we’ve always done it this way” frustrate younger professionals accustomed to automation and real-time transparency.·  Work Ethic Perception: Older professionals may associate dedication with long hours and endurance; younger staff may associate it with innovation, precision, and efficiency. However, these are not signs of incompatibility; they’re opportunities for balance. Experience provides wisdom and context. Innovation brings efficiency and adaptability. The Hidden Cost of Division Unchecked generational division doesn’t just affect morale; it impacts retention, productivity, and compliance. Newer staff may leave when they feel undervalued or unsupported. Senior staff may experience burnout when they feel their experience isn’t respected. Training gaps widen, communication falters, and collaboration gives way to frustration. According to Bentley University (2025), stereotyping generational traits can demotivate employees, especially when individuals feel boxed in or misunderstood. This type of disengagement can result in laboratories losing valuable institutional knowledge and hindering progress toward digital transformation. Bridging the Gap: From Gatekeeping to Mentorship To strengthen laboratory culture, leadership must intentionally bridge the generational divide through communication and shared learning. Strategies to Foster Collaboration: · Structured Mentorship Programs: Pair experienced professionals with new hires to promote mutual learning rather than one-sided instruction.· Reverse Mentorship: Encourage younger staff to share technology-driven efficiencies while learning institutional best practices.· Promote Psychological Safety: Create an environment where every technologist, whether new or seasoned, can ask questions without judgment.· Shared Visibility Through Digital Tools: Platforms like Lab Symplified provide centralized, traceable maintenance logs and records, helping teams work from the same transparent source of truth. As Bentley University (2025) notes, when organizations intentionally leverage generational diversity, it becomes a strategic advantage, combining depth of experience with a fresh perspective. From Division to Legacy Generational diversity in the clinical laboratory is not a weakness; it is a strength. It reflects the field’s evolution. Experienced technologists protect legacy, accuracy, and quality. Newer professionals bring innovation, adaptability, and a forward-thinking approach. When teams replace gatekeeping with guidance and assumptions with curiosity, they build a culture that values both tradition and transformation. The future of laboratory medicine depends not on one generation, but on all of them, working together to advance patient care and uphold the integrity of the profession. References Bentley University. (2025, May 15). Today’s unprecedented six-generation workforce. Retrieved from https://www.bentley.edu/magazine/six-generation-workforce Johns Hopkins University. (2022, November 17). The changing generational values. Imagine Center for Integrative Learning and Life Design. Retrieved from https://imagine.jhu.edu/blog/2022/11/17/the-changing-generational-values/

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Recruiting Laboratory Professionals: A Psychology-Informed Playbook

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Author:  Darryl Elzie, PsyD. MHA, MLS (ASCP), CQA (ASQ) Clinical labs are hiring in a tight market.  Retirement, attrition, and burnout have combined to challenge lab managers and HR in finding qualified individuals.  Instead of changing how and where jobs are posted, a recruitment team should make some behavioral changes in how they go about getting medical technicians and technologists in the door. The following is a psychology-based strategy and current workforce data. I’ve included some specific actions in developing high school and college medical laboratory pipelines, why certification boosts marketability, and what the future looks like. Use motivational psychological science at every touchpoint High-school pipeline College pipeline Why certification is important  A 10-week framework Weeks 1–2: Publish a one-page “Lab Careers Fast Facts” (outlook, salary bands, certification routes, local programs). Add QR codes to an interest form and “shadow a scientist” sign-up. Weeks 3–4: High-school pop-ups and counselor nights; near-peer reels; launch paid summer assistant posting. Weeks 5–6: University blitz—faculty drop-ins, student org talks, Handshake ads; announce certification exam-fee coverage and bridge scholarships. Weeks 7–8: Saturday Open House with micro-experiments; same-day conditional offers for summer roles. Weeks 9–10: Kick off MLT to MLS bridge; assign mentors; send “Choose Your Path” emails (MLT, MLS, categorical, phlebotomy) with timelines. Future outlook BLS projects 5% job growth (2023–2033) for clinical laboratory technologists/technicians—about average—with ~24,200 openings annually driven by retirements, transfers, and demand from aging populations, chronic disease testing, and prenatal/genetic diagnostics. (Bureau of Labor Statistics) Across healthcare broadly, employment is set to grow “much faster than average,” so competition for talent will remain intense. (Bureau of Labor Statistics) Public-health lab surveys show recruitment and retention remain pivotal; APHL’s 2024 profile and “What Matters to New Hires” data point to work/life balance, safety, competitive pay, benefits, and flexible schedules as top attractors/retainers. Closing In an era of workforce shortages and rising healthcare demands, clinical laboratories cannot rely on traditional recruitment tactics alone. By applying psychologically informed strategies, grounded in motivation, identity formation, and career self-efficacy, laboratories can build pipelines that engage students early, empower college graduates, and position certification as both an attainable milestone and a differentiator in the job market. The future of laboratory medicine depends not only on technology and innovation, but also on our ability to attract, develop, and retain the next generation of professionals from a broader and more diverse labor pool. Forward-thinking outreach, coupled with flexible human resources practices, will ensure that laboratories remain resilient, patient-centered, and equipped with the talent necessary to advance diagnostic excellence for years to come. References U.S. Bureau of Labor Statistics. Clinical Laboratory Technologists and Technicians—Occupational Outlook Handbook. Job outlook (5% growth, ~24,200 openings/yr), licensure/certification notes. (Bureau of Labor Statistics) U.S. Bureau of Labor Statistics. Healthcare Occupations—Overview. Sector-wide growth outlook (2023–2033). (Bureau of Labor Statistics) American Society for Clinical Pathology (ASCP). Vacancy Survey Results Show Workforce Challenges Amplified After Pandemic. Difficulty hiring certified staff; some labs relaxing requirements. (ASCP) NAACLS. CEO’s Corner: Findings from the 2023 Annual Survey. Outcomes benchmarks (≥70% placement within one year), stability of outcomes, program trends. (naacls.org) Association of Public Health Laboratories (APHL). 2024 Laboratory Workforce Profile—Survey Summary Report. Recruitment/retention insights for lab leaders. APHL. What Matters to New Hires (2024 Infographic). Top factors influencing acceptance/retention (work/life balance, safety, pay, benefits, flexibility).

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