Clinical Chemistry Analyzers: Selection Criteria and Workflow Integration

Clinical chemistry analyzers are automated instruments that perform biochemical tests on patient samples such as serum, plasma, urine, and other body fluids. These systems range from small benchtop units handling a few hundred tests per hour to large integrated platforms capable of thousands of tests per hour with connected preanalytical and postanalytical modules. For laboratory managers, selecting the right analyzer requires a structured evaluation of throughput, test menu breadth, cost per reportable result, and how the instrument fits into existing laboratory workflows. This article provides a decision framework for evaluating clinical chemistry analyzers, with emphasis on verification protocols, quality control integration, and practical considerations for implementation.

The Role of Clinical Chemistry Analyzers in Modern Diagnostics

Clinical chemistry analyzers perform quantitative measurements of analytes including electrolytes, enzymes, metabolites, proteins, and therapeutic drugs. These measurements support diagnosis, treatment monitoring, and risk assessment across nearly every medical specialty. The analytical core of these instruments typically employs photometric, turbidimetric, immunoturbidimetric, or ion-selective electrode technologies, with some systems now incorporating mass spectrometry capabilities.

The selection of an analyzer affects also test quality but also laboratory efficiency, staff workload, and operational costs. A poorly matched analyzer can create bottlenecks in sample processing, increase turnaround times, and elevate the cost per test. Conversely, an appropriately selected system can streamline workflows and support consistent result quality. The decision process should therefore be systematic and evidence-based, drawing on published evaluation criteria and verification data instead of vendor claims alone.

Core Principles of Analyzer Selection

Throughput and Workload Matching

Throughput is the number of tests or samples an analyzer can process per hour. Matching analyzer capacity to laboratory workload is a foundational selection criterion. An analyzer with excessive capacity represents wasted capital and maintenance expenditure, while insufficient capacity creates delays and staff overtime.

Workload assessment should consider current test volumes, projected growth, and peak demand periods. Laboratories serving emergency departments or intensive care units require rapid turnaround for critical tests such as electrolytes, glucose, and cardiac markers. High-volume reference laboratories may prioritize continuous operation and minimal operator intervention. The relationship between analyzer throughput and laboratory workflow has been recognized for decades, with early evaluations emphasizing the need for economic analysis of automated analyzers in clinical chemistry settings. The Journal of Automatic Chemistry published decision criteria for economic evaluation of automatic analyzers as early as 1980, establishing that instrument selection requires formal assessment of costs and benefits instead of informal preference.

Test Menu and Analytical Capabilities

The test menu determines which analytes a laboratory can offer without additional instrumentation. Core chemistry panels typically include glucose, creatinine, urea, electrolytes, liver enzymes, and lipid profiles. Specialized testing may include therapeutic drug monitoring, tumor markers, thyroid function tests, and specific proteins.

Laboratories should evaluate whether the analyzer supports the full range of tests required by their clinical services. Some analyzers offer integrated immunoassay capabilities, allowing both chemistry and immunochemistry testing on a single platform. Others are designed for chemistry testing only, requiring separate immunoassay instruments. The verification of examination procedures is a responsibility for clinical laboratories to guarantee that performance characteristics comply with specifications obtained during validation and are congruent with the intended scope of the assay. A 2022 study in the Scandinavian Journal of Clinical and Laboratory Investigation verified 72 biochemical parameters on Atellica CH 930 and IM 1600 analyzers, following CLSI EP10-A3 guidelines. The study collected validation data from the manufacturer, biological variation data from the European Federation of Clinical Chemistry and Laboratory Medicine database, and specifications for specific assays to meet Fetal Medicine Foundation standards. Out of 73 methods evaluated, only chloride, magnesium, and sodium failed criteria for adequate precision, trueness, and comparability. The assays did not show significant loss of linearity, linear drift, or sample carry-over.

This verification example illustrates that even well-established analyzers require formal evaluation before clinical use. Laboratories should plan for verification of every assay they intend to report, using manufacturer specifications and published biological variation data as reference points.

Cost Considerations

Cost evaluation extends beyond the initial purchase price. Total cost of ownership includes reagents, calibrators, quality control materials, consumables, maintenance contracts, service visits, and staff time. The cost per test varies substantially by analyte and by analyzer platform.

A 2026 carbon footprint assessment of a centralized clinical biochemistry platform at Cochin Hospital in Paris found that laboratory consumables accounted for nearly 80% of total emissions, with the platform emitting approximately 0.52 kgCO2e per test. The immunoassay module had the highest footprint per test at 2.89 kgCO2e per test, primarily driven by expensive consumables. While this study focused on environmental impact, the findings highlight that consumable costs dominate the operational expense structure of automated analyzers.

Economic evaluation should include projected test volumes to calculate cost per reportable result. Reagent packaging sizes, calibration frequency, and quality control consumption all affect the final cost per test. Laboratories should request detailed pricing from vendors and model costs across multiple volume scenarios.

Workflow Integration

Workflow integration considers how the analyzer fits into the broader laboratory environment. Key factors include sample transport, specimen preparation, result reporting, and interfacing with laboratory information systems. Total laboratory automation systems connect chemistry, hematology, and coagulation analyzers with preanalytical modules for sorting, centrifugation, and aliquotting.

A 2017 report in Laboratory Medicine outlined a process for selecting a total laboratory automation system. In Phase I, a committee including laboratory directors and technologists reviewed five systems based on a formal request for information process, site visits, and vendor presentations. The committee developed evaluation criteria and selected the two highest performing systems. In Phase II, they executed a detailed comparison of the two vendors based on cost, instrument layout, workflow design, and future potential. The authors emphasized that employee engagement was vital for successful selection and implementation.

This phased approach provides a practical model for analyzer selection. Involving technologists who will operate the instruments ensures that practical workflow concerns are addressed before purchase. Site visits allow the evaluation team to observe instruments in real laboratory settings and speak with operators about their experiences.

At a Glance: Analyzer Selection Decision Table

Verification and Validation Protocols

Manufacturer Validation versus Laboratory Verification

Manufacturers validate analyzer performance during instrument development, establishing analytical claims for precision, accuracy, linearity, and other performance characteristics. Laboratory verification confirms that the instrument performs according to these claims in the specific laboratory environment where it will be used. Verification is a responsibility for clinical laboratories to guarantee that their performance characteristics comply with the specifications obtained during the validation process and are congruent with the intended scope of the assay.

The 2022 verification study of Atellica analyzers followed CLSI EP10-A3 guidelines, which provide a standardized protocol for evaluating precision, bias, linearity, linear drift, sample carry-over, and comparability. The study collected 17,550 results during the EP10 verification process. This level of rigor requires dedicated staff time and careful planning. Laboratories should allocate sufficient resources for verification before the analyzer is placed into routine service.

Precision and Bias Assessment

Precision refers to the closeness of agreement between repeated measurements of the same sample. Bias refers to the difference between the measured value and the true value or reference value. Both parameters must be evaluated during verification.

The 2025 study evaluating measurement uncertainty of 11 serum proteins measured by immunoturbidimetric methods according to ISO/TS 20914 used one year of internal quality control data from Roche Cobas analyzers. Measurement uncertainty was calculated using uncertainty and calibrator uncertainty, assuming negligible deviation from external quality assessment data. Analytical performance specification models were selected according to the European Federation of Clinical Chemistry and Laboratory Medicine criteria. The study found that immunoglobulin A and rheumatoid factor were the only two analytes that met the required and minimum maximum allowable uncertainty values at both internal quality control levels. Measurement uncertainty values for C-reactive protein, ceruloplasmin, transferrin, and beta-2-microglobulin exceeded targets at both levels. The authors concluded that assay precision should be improved for C-reactive protein, immunoglobulin G, immunoglobulin M, ceruloplasmin, transferrin, and beta-2-microglobulin.

This study demonstrates that precision can vary substantially across analytes on the same analyzer platform. Laboratories should evaluate precision for each analyte they intend to report, using internal quality control data collected over an extended period.

Linearity and Reportable Range

Linearity is the ability of an assay to produce results directly proportional to the concentration of the analyte in the sample. The reportable range extends from the lower limit of quantitation to the upper limit of linearity. Samples with analyte concentrations above the upper limit require dilution and retesting.

The 2026 evaluation of a fully automated glucose-6-phosphate dehydrogenase assay on the Mindray BS-2800M analyzer demonstrated linearity from 0.37 to 20.86 U/g hemoglobin, covering very low and very high levels. The carryover rate was less than 1%. The study used 1,561 whole blood samples and established reference intervals for deficient, intermediate, and normal classifications. The automated method achieved diagnostic accuracy of 91.35% compared with genetic results, surpassing conventional single enzymatic methods.

Linearity verification should use samples spanning the full reportable range, including concentrations near the lower and upper limits. Manufacturers typically provide linearity materials or protocols, but laboratories should confirm linearity using their own sample matrices when feasible.

Sample Carry-Over

Sample carry-over occurs when material from one sample contaminates the next sample, producing erroneous results. Carry-over is particularly concerning for assays with wide concentration ranges, such as cardiac markers or therapeutic drugs. The EP10 protocol includes specific procedures for evaluating carry-over.

The G6PD study reported a carryover rate of less than 1%, which is generally considered acceptable. Laboratories should verify carry-over for high-risk assays and implement protocols to minimize its impact, such as additional wash cycles or sample ordering strategies.

Workflow Integration Strategies

Preanalytical Considerations

Preanalytical processes include sample collection, transport, centrifugation, and aliquotting. These processes significantly affect result quality and turnaround time. A 1999 study in Clinical Chemistry examined centrifuge operation in a hospital laboratory and found that the mean sample load per run was 13.6 samples, with user-selectable cycle times ranging from 1 minute 10 seconds to 12 minutes 33 seconds at a fixed g value of 1050. During the laboratory working window from 0900 to 1700, only 50% of centrifuge capacity was utilized, and more than one-third of the sample workload was delayed for more than 5 minutes because centrifuges were not emptied promptly. Additionally, 35% of the sample workload was centrifuged for less than the time prescribed in operational specifications.

These findings highlight the importance of preanalytical workflow optimization. Laboratories should evaluate centrifugation capacity, staffing patterns, and sample transport logistics when integrating a new analyzer. Automated sample preparation systems can reduce delays and improve consistency, but they add capital and maintenance costs.

Sample Integrity and Hemolysis Detection

Hemolysis is the rupture of red blood cells with release of intracellular contents into the serum or plasma. Hemolysis can falsely elevate potassium, lactate dehydrogenase, and other analytes while diluting others. Detection of hemolysis is essential for result accuracy.

A 2025 study in Clinica Chimica Acta evaluated integrated hemolysis detection by the GEM Premier 7000 blood gas analyzer. The analyzer flagged whole blood potassium results impacted by hemolysis. Hemolysis index results agreed with Abbott Alinity c hemolysis values in 70 out of 74 specimens. From February 2024 to March 2025, the analyzer detected hemolysis above 50 mg/dL free hemoglobin in 8.8% of blood gas specimens. Hemolysis rates varied from 1.5% in the operating room setting to 18.3% in the emergency department. Hemolyzed patient specimens showed increased median potassium results, and ionized calcium results were decreased at high hemolysis levels.

Laboratories should select analyzers with integrated hemolysis detection or implement manual hemolysis assessment protocols. Hemolysis flags should trigger result review and, when appropriate, sample recollection.

Ion-Selective Electrode Methodology

Electrolyte measurements use ion-selective electrode technology, which exists in two forms. Direct ion-selective electrodes measure electrolyte activity in undiluted samples, while indirect ion-selective electrodes measure diluted samples. These methods do not always produce concordant results.

A 2016 study in Critical Care Medicine compared sodium results from blood gas analyzers using direct ion-selective electrodes with core laboratory chemistry analyzers using indirect ion-selective electrodes. The study found a significant negative linear correlation between indirect and direct sodium results relative to changes in total protein concentration. For each change of 10 g/L in total protein concentration, a deviation of approximately 1.3 mmol/L was observed with the indirect result. The authors developed an adjustment equation to correct between the two methods and recommended that direct measurements on blood gas analyzers be preferentially used in hypoproteinemic patients.

Laboratories should be aware of the methodology used by their analyzers and communicate potential discrepancies to clinical staff. For patients with abnormal protein concentrations, the choice of methodology can affect clinical decisions.

Automation and Emerging Technologies

Total Laboratory Automation

Total laboratory automation connects multiple analyzers and preanalytical modules into an integrated system. These systems can include sample sorting, centrifugation, aliquotting, and transport to connected analyzers. The 2017 Laboratory Medicine report described a selection process that included formal request for information, site visits, and vendor presentations. The committee developed evaluation criteria and selected the two highest performing systems for detailed comparison.

Implementation of total laboratory automation requires careful planning and staff engagement. The authors emphasized that employee engagement was vital for successful selection and implementation. Laboratories should involve technologists, supervisors, and pathologists in the selection process and provide adequate training before go-live.

Automated Mass Spectrometry

Mass spectrometry offers analytical advantages over traditional immunoassay methods, including greater specificity and robustness. However, conventional liquid chromatography-tandem mass spectrometry methods are labor-intensive and require highly trained, dedicated laboratory staff. Automated mass spectrometry systems aim to improve workflows and reduce turnaround times.

A 2026 study in Clinical Chemistry and Laboratory Medicine evaluated turnaround times for the Cobas i 601 analyzer, an automated random-access mass spectrometry system, compared with routine liquid chromatography-tandem mass spectrometry using batch processing. The study analyzed six sets of analytes including mycophenolic acid, antibiotics, antiepileptics, voriconazole, testosterone, and 25-hydroxyvitamin D across seven laboratories. Median time from batch start to final result availability was 6.8-fold shorter with the automated system compared with routine methods, at 1 hour 34 minutes versus 10 hours 37 minutes. Median total hands-on time was 19-fold shorter at 5 minutes versus 93 minutes.

Automated mass spectrometry systems represent a significant workflow improvement for laboratories performing therapeutic drug monitoring and other specialized testing. However, these systems carry substantial capital costs and require specialized expertise for method development and troubleshooting.

Intelligent Workflow Automation

Emerging technologies include intelligent workflow automation that uses algorithms to reflex testing and reduce manual intervention. A 2025 study in Clinica Chimica Acta validated an intelligent platelet counting workflow using the Mindray BC-6800Plus analyzer. The workflow automatically provided accurate platelet values using impedance and fluorescent techniques, with blood smear examination by an automated morphology analyzer. Among 299 abnormal samples, the workflow accurately processed over 99% of samples, minimizing the need for fluorescent counting and blood smear reviews.

Similar intelligent workflows are being developed for clinical chemistry applications. These systems can reduce manual review requirements and improve laboratory efficiency. However, laboratories should validate these workflows against their own sample populations and establish clear criteria for manual review.

Quality Control and Assurance

Internal Quality Control

Internal quality control involves testing control materials with known concentrations at regular intervals to monitor assay performance. Control results are plotted on Levey-Jennings charts and evaluated against Westgard rules or similar multi-rule procedures. The frequency of quality control testing depends on assay stability, laboratory accreditation requirements, and risk assessment.

The 2025 measurement uncertainty study used one year of internal quality control data from Roche Cobas analyzers to estimate measurement uncertainty for 11 serum proteins. The study highlighted that maintaining acceptable precision over a long period remains a challenge for serum proteins analyzed by immunoturbidimetry, emphasizing the need for methodological improvements and stricter quality monitoring.

Laboratories should establish quality control schedules that detect performance changes before they affect patient results. Control materials should be appropriate for the assay methodology and cover clinically relevant concentration ranges.

External Quality Assessment

External quality assessment, also known as proficiency testing, involves analyzing samples provided by an external organization and comparing results with other laboratories using the same or different methods. External quality assessment identifies systematic bias and interlaboratory variability.

The World Health Organization Laboratory Quality Management System Handbook provides guidance on establishing and maintaining quality management systems in laboratories. External quality assessment is a component of laboratory quality assurance and should be integrated into the analyzer selection and ongoing monitoring process.

Measurement Uncertainty

Measurement uncertainty quantifies the dispersion of values that could reasonably be attributed to a measured quantity. Measurement uncertainty includes contributions from imprecision, bias, calibrator uncertainty, and other sources. The ISO/TS 20914 standard provides a framework for estimating measurement uncertainty in medical laboratories.

The 2025 study of serum proteins demonstrated that measurement uncertainty varies substantially across analytes and quality control levels. Some analytes met maximum allowable uncertainty targets while others exceeded them. Laboratories should estimate measurement uncertainty for their assays and use this information when interpreting results and communicating with clinical staff.

Common Failure Patterns and Troubleshooting

Precision Failures

Precision failures occur when repeated measurements of the same sample produce variable results. Common causes include reagent deterioration, calibrator instability, temperature fluctuations, and photometer malfunction. The 2022 Atellica verification study identified precision failures for chloride, magnesium, and sodium, demonstrating that even established analyzers can have method-specific issues.

When precision failures occur, laboratories should review quality control data, check reagent and calibrator expiration dates, verify instrument maintenance, and contact the manufacturer for technical support. Persistent precision failures may require method replacement or instrument repair.

Carry-Over Contamination

Carry-over contamination produces falsely elevated results in samples analyzed after high-concentration samples. This is particularly problematic for assays with wide concentration ranges. The G6PD study reported a carryover rate of less than 1%, which is generally acceptable. However, laboratories should verify carry-over for their specific assay menu and sample population.

Strategies to minimize carry-over include increasing wash volumes, adding extra wash cycles after high-concentration samples, and implementing sample ordering protocols that separate high and low concentration samples.

Hemolysis Interference

Hemolysis interference affects multiple analytes, with potassium being the most clinically significant. The 2025 hemolysis study found that hemolysis rates varied from 1.5% in the operating room to 18.3% in the emergency department. High hemolysis levels affected also potassium results but also ionized calcium.

Laboratories should implement hemolysis detection and establish policies for reporting results from hemolyzed samples. For critical results from hemolyzed samples, recollection should be considered.

Calibration Drift

Calibration drift occurs when the relationship between measured signal and analyte concentration changes over time. This can result from reagent degradation, photometer drift, or changes in environmental conditions. Regular calibration and quality control testing detect drift before it affects patient results.

The 2026 chromogenic anti-Xa assay protocol described a laboratory-developed assay for rivaroxaban monitoring with dual-range quantification validated for routine therapeutic levels from 30 to 500 ng/mL and an extended range from 500 to 1500 ng/mL via automated dilution. The assay employed a specific 180-second incubation and a stabilized measurement window of 20 to 80 seconds to maximize linearity. The assay demonstrated high correlation with liquid chromatography-tandem mass spectrometry at r = 0.997 and met CLSI precision requirements.

Laboratories developing or modifying assays should follow structured validation protocols similar to those described in the FDA Bioanalytical Method Validation Guidance. This guidance provides a framework for validating analytical methods used in clinical studies.

Records and Documentation

Verification Records

Verification records document the experiments performed to confirm analyzer performance. These records should include the verification protocol, raw data, statistical analysis, and conclusions. The 2022 Atellica verification study collected 17,550 results during the EP10 verification process, demonstrating the scale of documentation required.

Verification records should be retained according to laboratory policy and accreditation requirements. They provide evidence of analytical performance and support ongoing quality assurance activities.

Quality Control Records

Quality control records document the results of control testing over time. These records support trend analysis, troubleshooting, and audit activities. The 2025 measurement uncertainty study used one year of internal quality control data, demonstrating the value of long-term quality control records.

Laboratories should maintain quality control records for each assay and review them regularly for trends or shifts. Electronic quality control systems can automate data collection and analysis.

Maintenance Records

Maintenance records document scheduled and unscheduled maintenance activities. These records support instrument reliability and troubleshooting. Laboratories should follow manufacturer recommendations for preventive maintenance and document all service activities.

The National Center for Advancing Translational Sciences Assay Guidance Manual provides guidance on assay development and validation, including considerations for automation and quality control. While focused on research applications, the principles apply to clinical laboratory testing.

Safety and Regulatory Context

Biosafety Considerations

Clinical chemistry analyzers process patient samples that may contain infectious agents. The World Health Organization Laboratory Biosafety Manual provides guidance on safe handling of biological materials. Laboratories should implement standard precautions, including the use of personal protective equipment, proper sample handling, and appropriate waste disposal.

Automated analyzers reduce direct sample handling but introduce new safety considerations, including moving parts, chemical hazards from reagents, and sharps from sample probes. Laboratory staff should receive safety training specific to the analyzers they operate.

Regulatory Requirements

Clinical chemistry analyzers are regulated medical devices in most jurisdictions. Laboratories must comply with applicable regulations for device registration, quality systems, and reporting. The FDA Bioanalytical Method Validation Guidance provides a framework for validating analytical methods used in clinical studies, which may apply to laboratory-developed tests.

Laboratories should maintain documentation of regulatory compliance, including device registration, calibration certificates, and quality system records. Accreditation bodies may require specific documentation for analyzer selection, verification, and ongoing quality assurance.

Environmental Considerations

Clinical biochemistry laboratories are resource-intensive, and laboratory consumables represent a significant source of greenhouse gas emissions. The 2026 carbon footprint study found that laboratory consumables accounted for nearly 80% of total emissions from a centralized analytical platform. The authors suggested that decarbonization efforts should focus on reducing consumable waste and promoting appropriate test ordering practices.

Laboratories should consider environmental impact when selecting analyzers and developing workflows. Strategies to reduce environmental impact include optimizing reagent packaging, reducing consumable waste, and implementing appropriate test ordering protocols.

Professional Escalation Criteria

Laboratory staff should escalate analyzer issues to supervisors, pathologists, or manufacturers under specific circumstances. Escalation is appropriate when:

• Quality control results exceed acceptable limits and cannot be resolved through routine troubleshooting
• Patient results are suspected to be inaccurate based on clinical correlation or delta checks
• Instrument malfunctions affect patient testing and cannot be resolved within expected timeframes
• Verification or validation results do not meet acceptance criteria
• Safety incidents occur involving the analyzer or its components

The 2022 Atellica verification study identified operational issues during initial training and familiarization with the instruments. The authors advised professionals to adequately standardize and protocolize their verification processes to ensure laboratory competence and patient safety. This advice applies to all aspects of analyzer implementation and ongoing operation.

Frequently Asked Questions

What is the difference between an automated clinical chemistry analyzer and a manual testing method?

Automated clinical chemistry analyzers perform sample pipetting, reagent addition, mixing, incubation, measurement, and result calculation without operator intervention for each step. Manual methods require the operator to perform each step separately, which increases the risk of error and limits throughput. Automated analyzers also include features such as barcode reading, sample dilution, and reflex testing that are not feasible with manual methods. The degree of automation varies by instrument, with some systems requiring operator loading of samples and reagents while others integrate with total laboratory automation systems.

How do I determine the throughput requirements for my laboratory?

Throughput requirements are determined by analyzing current test volumes, projected growth, and peak demand periods. Start by reviewing monthly and daily test volumes for each analyte or test group. Identify the busiest hours of the day and calculate the maximum number of tests that must be completed per hour. Add a buffer of at least 20% to account for reruns, dilutions, and quality control testing. Consider whether the analyzer will be the sole platform for specific tests or whether testing will be distributed across multiple instruments. The workload assessment should also consider sample types, since urine and other fluids may require different processing than serum or plasma.

What verification tests should I perform before placing an analyzer into routine service?

Verification should include precision, bias, linearity, linear drift, sample carry-over, and comparability studies. The CLSI EP10-A3 guidelines provide a standardized protocol for these evaluations. Precision is assessed by repeated measurements of control materials at multiple concentration levels. Bias is assessed by comparing results with reference materials or an established method. Linearity is assessed using samples spanning the reportable range. Carry-over is assessed by analyzing high-concentration samples followed by low-concentration samples. Comparability is assessed by testing patient samples on the new analyzer and a reference method. The 2022 Atellica verification study collected 17,550 results during the EP10 verification process, demonstrating the scale of data collection required.

How often should I run quality control materials?

Quality control frequency depends on assay stability, laboratory accreditation requirements, and risk assessment. Most laboratories run quality control at least once per day for each assay, with additional runs after calibration, reagent lot changes, and instrument maintenance. Some assays require more frequent quality control due to instability or clinical risk. The 2025 measurement uncertainty study used one year of internal quality control data, demonstrating that long-term quality control data support measurement uncertainty estimation and quality monitoring. Laboratories should establish quality control schedules based on manufacturer recommendations, regulatory requirements, and their own quality improvement data.

What is measurement uncertainty and why does it matter?

Measurement uncertainty quantifies the dispersion of values that could reasonably be attributed to a measured quantity. It includes contributions from imprecision, bias, calibrator uncertainty, and other sources. Measurement uncertainty matters because it affects the interpretation of laboratory results. A result with high uncertainty may not be distinguishable from a neighboring value, which can affect clinical decisions. The ISO/TS 20914 standard provides a framework for estimating measurement uncertainty in medical laboratories. The 2025 study of serum proteins found that measurement uncertainty varied substantially across analytes, with some exceeding maximum allowable uncertainty targets. Laboratories should estimate measurement uncertainty for their assays and use this information when interpreting results.

How do I evaluate the cost per test for different analyzers?

Cost per test is calculated by dividing total assay costs by the number of reportable results. Total assay costs include reagents, calibrators, quality control materials, consumables, maintenance, and staff time. Reagent costs vary by analyte and packaging size. Calibrator and quality control costs depend on frequency of use. Consumables include cuvettes, sample tips, and waste containers. Maintenance costs include preventive maintenance, service contracts, and repair parts. Staff time includes sample loading, quality control, troubleshooting, and result review. The 2026 carbon footprint study found that consumables accounted for nearly 80% of total platform emissions, highlighting the dominance of consumable costs in analyzer operations. Laboratories should model costs across multiple volume scenarios to understand how cost per test changes with workload.

What is the difference between direct and indirect ion-selective electrode methods?

Direct ion-selective electrode methods measure electrolyte activity in undiluted samples, while indirect methods measure diluted samples. Direct methods are used on blood gas analyzers and some point-of-care devices. Indirect methods are used on most core laboratory chemistry analyzers. The methods can produce different results in patients with abnormal protein or lipid concentrations. The 2016 study in Critical Care Medicine found that sodium results from indirect methods deviated by approximately 1.3 mmol/L for each change of 10 g/L in total protein concentration. Direct methods are not influenced by total protein concentration and should be preferentially used in hypoproteinemic patients. Laboratories should be aware of the methodology used by their analyzers and communicate potential discrepancies to clinical staff.

How do I integrate a new analyzer into my existing laboratory workflow?

Integration begins with a workflow assessment that maps sample flow from collection to result reporting. Identify bottlenecks and opportunities for automation. The 2017 Laboratory Medicine report described a phased selection process that included a formal request for information, site visits, and vendor presentations. The committee developed evaluation criteria and selected the two highest performing systems for detailed comparison. Employee engagement was vital for successful selection and implementation. Involve technologists, supervisors, and pathologists in the selection process and provide adequate training before go-live. Develop standard operating procedures for sample handling, quality control, maintenance, and troubleshooting. Plan for validation of interfaces with the laboratory information system and middleware.

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References and Further Reading

• Laboratory Quality Management System Handbook. World Health Organization.
• Laboratory Biosafety Manual. World Health Organization.
• Assay Guidance Manual. National Center for Advancing Translational Sciences.
• Bioanalytical Method Validation Guidance. U.S. Food and Drug Administration.
• NCBI Literature Resources. National Center for Biotechnology Information.
• Decision criteria for the selection of analytical instruments used in clinical chemistry: VI Techniques for the economic evaluation of automatic analysers.. The Journal of automatic chemistry, 1980.
• Verification of examination procedures for 72 biochemical parameters on the atellica(®) clinical chemistry and immunoassay analyzers.. Scandinavian journal of clinical and laboratory investigation, 2022.
• Objective vs subjective analyses of arch form and preformed archwire selection.. American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics, 2016.
• Employee Engagement Is Vital for the Successful Selection of a Total Laboratory Automation System.. Laboratory medicine, 2017.
• Evaluation of measurement uncertainty of 11 serum proteins measured by immunoturbidimetric methods according to ISO/TS 20914: a 1-year laboratory data analysis.. Clinical chemistry and laboratory medicine, 2025.
• Validation of immunoassay for protein biomarkers: bioanalytical study plan implementation to support pre-clinical and clinical studies.. Journal of pharmaceutical and biomedical analysis, 2011.
• Observations on centrifugation: application to centrifuge development.. Clinical chemistry, 1999.
• How to Solve the Underestimated Problem of Overestimated Sodium Results in the Hypoproteinemic Patient.. Critical care medicine, 2016.
• Performance evaluation of an automated method for accurate identification of glucose-6-phosphate dehydrogenase deficiency on the BS-2800M analyzer.. 2026.
• Comparison of six serum analyte workflows using routine liquid chromatography-tandem mass spectrometry methods at multiple laboratories with the Cobas i 601 analyzer, an automated mass spectrometry system.. 2026.
• Carbon footprint of a clinical biochemistry platform in a French university hospital: Identifying the main greenhouse gas emission sources.. 2026.
• Protocol for a laboratory-developed chromogenic anti-Xa assay for rivaroxaban monitoring.. 2026.
• Evaluation of integrated hemolysis detection by a blood gas analyzer and hemolysis effect on blood gas test results.. Clinica chimica acta, international journal of clinical chemistry, 2025.
• Validation of an intelligent automated workflow for accurate platelet counting in hematology laboratory.. Clinica chimica acta, international journal of clinical chemistry, 2025.
• Selection and evaluation of laboratory instrumentation in clinical chemistry: I. Survey of major analysers in the United Kingdom. Medical Laboratory Sciences, 1986.
• Automation in clinical chemistry. Medical Progress Through Technology, 1975.

This article is educational and does not replace validated laboratory procedures, institutional biosafety review, manufacturer instructions, or professional interpretation.