Informatics, digital & computational pathology

Laboratory information systems

Autoverification in the core clinical laboratory



Last author update: 18 October 2023
Last staff update: 18 October 2023

Copyright: 2022-2024, PathologyOutlines.com, Inc.

PubMed Search: Autoverification

Patrick Vanderboom, Ph.D.
Darci R. Block, Ph.D.
Page views in 2023: 191
Page views in 2024 to date: 121
Cite this page: Vanderboom P, Block DR. Autoverification in the core clinical laboratory. PathologyOutlines.com website. https://www.pathologyoutlines.com/topic/informaticsautoverificationincoreclinicallab.html. Accessed March 28th, 2024.
Definition / general
  • Autoverification (AV) is the process of using computer based rules to evaluate and validate laboratory results without human interaction
  • AV accomplishes 2 things
    1. Allows patient results to automatically flow into downstream systems that do not require intervention
    2. Identifies results or samples that require manual review and possible intervention by a medical laboratory technologist (MLT) (Clin Lab Med 2023;43:47)
  • AV applies standard evaluation criteria with predefined rules to all results, thereby improving error rate detection and turnaround time as well as allowing staff to work at the top of their licensure and promoting operational efficiency
Essential features
  • Verification of laboratory test results occurs in the postanalytical phase and represents the last quality assurance measure performed before results are released into a patient data repository (Clin Biochem 2019;73:11)
  • Historically, result verification was performed manually on a sample by sample and result by result basis, resulting in a tedious, repetitive, time consuming and error prone task that depends upon the experience and education of the laboratory staff
  • AV is a postanalytical workflow improvement process that limits the number of test results that need to be manually reviewed by a MLT by utilizing a system of computer based rules to evaluate and validate laboratory results without human interaction
  • Designing and implementing an AV system is a demanding task that requires input from several knowledge domains
  • Properly designed and implemented AV systems can improve turnaround time, staff utilization, operational efficiency and error detection rates while allowing MLTs to focus on samples requiring greater human scrutiny (Clin Biochem 2019;73:11)
Terminology
  • Verification: method to detect and prevent reporting of errors that occur in the preanalytical, analytical or postanalytical phase
  • Autoverification (AV): an automated, computer based process by which individual laboratory results are accepted or rejected for release into a patient's electronic medical record without human interaction
  • AV pass rate: the percentage of analyte results that are released to the patient medical record without human intervention
  • Delta checks: the process of comparing a patient's current and previous laboratory results, with specified criteria, as a quality improvement and error detection tool
  • Limit checks: a predefined concentration limit established to identify samples with improbable or unlikely concentrations, at which a notification is triggered to review the result
  • Consistency check: cross check of 2 or more analytes with a known correlation to identify unlikely result patterns
  • Analytical measuring range (AMR)
  • Laboratory information system (LIS)
Applications
Common evaluations / rules
  • Quality control
    • Calibration validity
    • QC validity
  • Instrument flags
    • Flag information associated with results from the instrument
  • Numerical verification intervals
    • Limit checks
      • Analyte specific concentration limits to identify results with improbable or unlikely concentrations
    • Critical values
      • Analyte specific concentration limits considered life threatening communicated via phone call from lab staff
    • Analytical measuring range
      • Analyte concentrations that may prompt repeat testing (low) or manual dilution (high)
  • Previous patient results
    • Delta checks
      • Consistency between 2 consecutive patient results evaluated for a predefined acceptable amount of change and time window between results
    • Consistency checks
      • Cross check evaluation of 2 or more analytes with a known correlation to identify unlikely patterns
  • Specimen quality
    • Hemolysis check
    • Icterus check
    • Lipemia check
  • Reference: Clin Biochem 2021;93:90
Design, validation and implementation
  • Design of AV systems
    • Select AV software (middleware / LIS)
    • Assemble a multidisciplinary team that includes medical directors, supervisors, MLTs and information technology (IT) staff
    • Develop AV algorithms
      • Review current laboratory practices and avoid automating procedures that add no value
      • Laboratories with limited experience in AV should begin with simple algorithms
      • Higher AV pass rates can be achieved by implementing more complex algorithms after acquiring experience
      • Rule types comprising AV algorithms can range from numerical verification intervals to cascading boolean rule sets
      • Machine learning based approaches have been described; however, these approaches are unlikely to replace current practices in the near future (Clin Lab Med 2023;43:47)
    • Complete AV programming and convert rules to computer code
  • Validation of AV algorithms is required before implementation and occurs in 2 phases
    • Wet testing
      • Performed by analyzing samples meeting the desired criteria on the appropriate instruments and passing them through the AV system
      • Samples meeting the criteria to test the AV rules may be difficult to obtain and can be generated by manipulating native samples (spiking, diluting, adding interferents)
    • Dry testing
      • Performed using simulated results or selecting results that meet the desired criteria from previously run samples
      • Simulated results should be indistinguishable from those released from the instrument
  • Implementation
    • AV systems should be intensively audited for several days following initial implementation and periodically after that
    • AV algorithms require revalidation any time changes are made (CAP GEN.43875)
    • Laboratories should maintain records of initial AV system validation and validation of any changes following implementation including thorough audit trails
    • AV rules adopted by the lab should be readily available and understood by laboratory staff
    • Results held for manual review should be identifiable for retrospective review
    • Process to suspend the AV system is necessary and should be documented
    • Monitor AV performance and continuously improve algorithms
      • Test and report based AV pass rates should be monitored
Advantages
  • AV of laboratory test results occurs in the postanalytical phase and represents the last quality assurance measure performed before results are released into a patient data repository
  • AV is a postanalytical workflow improvement process that applies a consistent set of criteria across all samples and limits the number of test results that need to be manually reviewed by a MLT (Clin Biochem 2019;73:11)
  • AV pass rates of 50 - 60% are readily achievable upon initial implementation
  • Pass rates > 95% can be achieved through continuous improvement
  • AV can improve turnaround time, staff utilization, operational efficiency and error detection rates
Limitations
  • Once programmed, AV rules require strict version control processes to prevent inadvertent changes
  • Different patient populations require unique AV rules, therefore, standardized AV rules do not exist
  • Acquiring data released from the LIS is straightforward; however, data for samples held by AV rules need to be mined from other locations (i.e., middleware) and are not readily available
  • It is not possible to test all combinations of every rule during validation
    • Continued auditing of the AV system to identify unintended occurrences is essential
  • Little guidance exists regarding AV system quality standards, rule development or benchmarks to measure effectiveness (Clin Biochem 2019;73:11)
Diagrams / tables

Images hosted on other servers:
Master algorithm template for biochemical tests

Master algorithm template for biochemical tests

Board review style question #1
What is autoverification (AV) in the clinical laboratory?

  1. A procedure used to verify new algorithms that are used for reporting test results
  2. A process related to reporting test results which is required for CLIA certification
  3. The process of using computer based rules to evaluate and validate laboratory results without human interaction
  4. The process of using patient results to verify the integrity of new reagents on high throughput chemistry analyzers
Board review style answer #1
C. The process of using computer based rules to evaluate and validate laboratory results without human interaction. AV accomplishes 2 things: 1) releases patient results that do now require intervention and 2) identifies samples that require manual review by a medical laboratory technologist. Answer A is incorrect because AV is the process of using computer based rules to evaluate and validate laboratory results without human interaction, not a procedure used to verify new algorithms that are used for reporting test results. Answer B is incorrect because AV is not required for CLIA certification. Answer D is incorrect because reagent verification is not part of AV.

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Reference: Autoverification in the core clinical laboratory
Board review style question #2
Which of the following are advantages of using autoverification (AV)?

  1. Can improve the accuracy of test results in the clinical laboratory
  2. Can improve turnaround time, staff utilization, operational efficiency and error detection rates in the clinical laboratory
  3. Can reduce the reagent consumption of chemistry analyzers
  4. Improves staff satisfaction, result accuracy and the efficiency of reagent use
Board review style answer #2
B. Can improve turnaround time, staff utilization, operational efficiency and error detection rates in the clinical laboratory. Answer A is incorrect because AV does not affect the analytical accuracy of test results. Answer C is incorrect because AV does not affect the amount of reagent consumed. Answer D is incorrect because AV does not affect result accuracy or efficiency of reagent usage.

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Reference: Autoverification in the core clinical laboratory
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