Automation in clinical biochemistry

Table of contents

Introduction

Automation in clinical biochemistry refers to using advanced technologies, robotic systems, and computerized equipment to perform routine and specialized laboratory tasks. By minimizing manual interventions, automation enhances accuracy, speed, and efficiency in processing biochemical samples, which is crucial in handling high sample volumes and maintaining quality standards in diagnostics.

Importance and Benefits of Automation

Increased Throughput: Automation enables laboratories to process large samples in a shorter time, meeting the demands of modern healthcare facilities.
Standardization: Consistent and precise application of protocols ensures uniformity in results, reducing variability.
Error Reduction: Automated systems minimize human errors in sample handling, pipetting, and data entry.
Improved Turnaround Time (TAT): Faster processing ensures timely reporting of results, critical for patient care.
Cost Efficiency: While the initial investment is high, long-term operational costs are reduced due to optimized reagent use and reduced labor dependency.
Better Quality Control (QC): Automated systems perform real-time monitoring, calibration, and validation to maintain analytical performance.

Workflow in Automated Biochemistry Laboratories

Pre-Analytical Phase

This phase involves the preparation of samples before testing. Tasks include:

Sample Identification:
- Barcoding and Radio Frequency Identification (RFID) systems ensure accurate sample tracking and reduce mix-ups.
Sample Sorting:
- Robotic sorters categorize samples based on test type, urgency (e.g., STAT samples), or department.
Centrifugation:
- Automated centrifuges separate serum or plasma from blood samples. Sensors detect issues like tube imbalance.
Aliquoting:
- Automated systems divide samples into smaller aliquots for different tests without contamination risks.
Pre-Analytical Quality Checks:
- Detection of sample anomalies such as:
  - Hemolysis: Red cell rupture affecting results of tests like potassium.
  - Lipemia: Excess fat in the sample affecting optical readings.
  - Insufficient Volume: Flagging samples with inadequate quantity for testing.

Analytical Phase

The analytical phase is the core of automation, where tests are performed. Automated analyzers perform various biochemical assays with high precision.

Technologies Involved in Analytical Automation:

Spectrophotometry:
- Measures light absorbance at specific wavelengths to quantify analytes like glucose, urea, and bilirubin.
Immunoassays:
- Detect specific antigens or antibodies using labeled reagents (e.g., chemiluminescence for thyroid function tests).
Electrochemistry:
- Using ion-selective electrodes measures ion concentrations such as sodium, potassium, and chloride.
Enzymatic Assays:
- Detect enzymes (e.g., ALT, AST) to evaluate liver function.
Mass Spectrometry:
- High-resolution technique for identifying and quantifying complex molecules such as hormones, drugs, or metabolites.

Features of Analytical Automation:

Random Access Testing: Allows prioritized processing of urgent samples.
Multi-Analyte Platforms: Capable of testing multiple analytes simultaneously.
Reagent Management: Systems monitor reagent levels and automatically reorder when low.

Post-Analytical Phase

This phase involves processing and reporting results after analysis.

Result Validation:
- Automated systems use predefined criteria to flag abnormal or critical results for manual review.
Data Transmission:
- Results are directly integrated into Laboratory Information Systems (LIS) and Electronic Medical Records (EMRs) for easy access by clinicians.
Reflex and Reflective Testing:
- Reflex Testing: Automatically orders additional tests based on initial results (e.g., confirmatory tests for elevated glucose).
- Reflective Testing: Allows clinicians to request further tests after reviewing the initial report.
Archiving:
- Digital storage of data for future reference and compliance with regulatory standards.

Total Laboratory Automation (TLA)

Definition: TLA integrates pre-analytical, analytical, and post-analytical workflows into a unified system.

Components:

Robotic Sample Transport:
- Conveyor belts or robotic arms move samples between workstations seamlessly.
Modular Systems:
- Flexible design allows laboratories to customize workflows based on their needs.
Centralized Control Systems:
- Dashboards monitor real-time operations, sample status, and error notifications.

Advantages:

Eliminates manual intervention across all phases.
Handles high-volume testing efficiently.
Reduces sample handling errors.

Challenges in Automation

High Initial Costs:
- The purchase and installation of automated systems require significant capital investment, especially for smaller labs.
Maintenance Requirements:
- Regular calibration, software updates, and troubleshooting require skilled technical staff.
Integration Issues:
- Compatibility with existing Laboratory Information Systems (LIS) and adapting workflows to automation can be challenging.
Sample Variability:
- Handling non-standard or atypical samples (e.g., pediatric samples or those with high viscosity) may still require manual processing.

Examples of Automated Systems

Roche Cobas Series: Modular systems with high-throughput capabilities.
Abbott Architect: Integrated analyzers for clinical chemistry and immunoassays.
Siemens ADVIA: Comprehensive solutions for routine and specialized testing.
BD Kiestra: Total automation for microbiology workflows.
Sysmex TLA: Robotic track systems for seamless sample movement.

Future Trends in Automation

Artificial Intelligence (AI):
- AI-driven algorithms for quality control, predictive maintenance, and result interpretation.
Lab-on-a-Chip:
- Microfluidic devices can perform multiple tests on a single chip, reducing sample and reagent requirements.
Point-of-Care Testing (POCT):
- Portable, automated analyzers enable bedside testing and faster results.
Internet of Things (IoT):
- Connected systems for real-time monitoring and remote diagnostics.
Miniaturization:
- Compact analyzers for smaller laboratories, making automation accessible to a wider range of facilities.