Of the three primary phases in the total laboratory testing process, the pre-analytical phase (generally defined from the time a laboratory request is made by a physician until the sample is ready for analysis) covers laboratory processes including test selection, patient biological variation, patient preparation, and specimen collection, transportation, processing, and storage. Any variation or unexpected condition change during these dynamic processes can adversely affect laboratory test results, thereby leading to patient mismanagement and compromised patient safety (see TABLE 1).
In recent decades, vast improvements and advancements in methodology, instrument technology, automation, standardization, and quality control have dramatically reduced analytical-phase errors leaving the pre-analytical phase as the area wherein most errors affecting test results occur (see TABLE 2)1-6. Further, pre-analytical phase variables affect a wide range of laboratory testing, which account for up to 70% of total laboratory errors.4,5,7,8 Accordingly, a focused effort should be made to mitigate and eliminate error causation in the pre-analytical phase.
Pre-analytical Error Detection and Prevention
Many clinical laboratories face a major challenge in improving pre-analytical processes and quality tests due to a lack of process and procedure (P&P) standardization. While control systems designed to ensure the quality of the analytical phase are highly developed, quality improvement intending to reduce pre-analytical errors as part of total quality management has not been fully formed. However, efforts should be made to detect and prevent pre-analytical phase errors through training, standardization of laboratory manuals and related P&Ps, establishment of effective error detection methods and prevention strategies, and implementation of a continuous quality improvement program.
Pre-analytical Error Detection
Begin by establishing a quality system to identify the most common pre-analytical errors, as sample quantity and integrity can easily be determined on front-end automation systems. An LIS-integrated quality system also can increase error detection/identification and enable tracking to ensure accurate and timely test results. Detection and prevention of the most common interferences, such as hemolysis (H), icterus (I), and lipemia (L), are critical. While manual detection of these interferences can be subjective and labor intensive, implementation and quantitation of the HIL indices and other rejection criteria by front-end automation, analytical instruments, middleware, or LIS programs can result in more efficient and accurate error detection and quality assurance.4,9
Once an error is detected, a system needs to be in place to reject related specimens. Laboratories need to establish specimen quality markers and enable the quality system to execute error detection and specimen rejection. Specimens should be rejected with specific conditions to help ensure accurate test results (see TABLE 3).
Pre-analytical Error Prevention
Recognizing risk points in the complex pre-analytical phase and the potential for error occurrence at each point is a challenge, yet delayed error detection and failure to prevent error replication can significantly impact patient care. Therefore, error prevention should become the ultimate priority. Preventive measures should be implemented throughout the pre-analytical phase and target areas such as those listed in TABLE 2 or those that are self-identified by the individual lab. Nuance aside, the following key preventive components should be considered:
Standardization: It is good laboratory practice to establish standard operating procedures (SOPs) for all pre-analytical P&Ps leaving nothing to chance. Development and implementation of SOPs is integral to error prevention.
Education and Training: Proper training and continuing education for all health care professionals involved in collecting, handling, preparing, and transporting patient samples is crucial to the mitigation of pre-analytical errors. Further, all laboratorians and technologists involved in the testing process should be trained on and knowledgeable of pre-analytical phase SOPs with good compliance.
Given that specimen collection is an area where errors have traditionally been common, all phlebotomists and nurses that perform blood draws or otherwise are put in charge of patient specimens should be trained on and follow the same SOPs to ensure the proper patient ID, collection device, container, labeling, order of draw, etc. Unacceptable specimens due to misidentification, insufficient volume, incorrect tubes or anticoagulant, or specimen quality issues should be rejected (see TABLE 3). Staff competencies on these SOPs should be assessed annually.
Targeting Errors: Laboratories should develop a plan to target the most common pre-analytical errors encountered in their specific labs to ensure preventive measures are relevant and focused. For example, implementation of a bar coding system and positive patient ID can help prevent mislabeling and patient ID issues. Specific education should be provided to phlebotomists and nurses regarding potential errors involving specimen quantity, as well as quality issues, such as hemolysis or incorrect patient ID or labeling. Devising and following corrective strategies can gradually free a lab from such errors.
Continuous Quality Improvement Program: Each laboratory should establish a continuous quality improvement program as part of a total quality management system. Laboratories can utilize quality tool principles (eg, Lean, Six Sigma, 5S) to minimize errors and improve quality service. Continuous quality improvement requires cultural buy in and is open ended.
Pre-analytical error detection and prevention requires excellent communication and collective effort from all stakeholders and health care teams involved in the laboratory testing process; from the phlebotomists and nurses who collect specimens, through transport, and on to personnel receiving specimens.
Ongoing Pre-analytical Error Analysis
There are numerous quality indicators for the pre-analytical phase and various approaches can be applied to quantitate and analyze errors and error reduction efforts. In this case, quality indicators are any measurable quantity that can be used as a metric in quality improvement and error detection in the pre-analytical phase (for examples, see TABLE 4). Analysis of HIL indices and delta checks are additional measures that can be taken:
Hemolysis, icterus, and lipemia are the most commonly seen interferences in laboratory testing with spectrophotometric methods. These altered results may lead to repeat testing, incorrect interpretation, wrong diagnosis, and potentially, inappropriate intervention and unfavorable patient outcomes. Thus, detection and prevention of these common interferences is critical. While some laboratories continue to manually check these interferences on a routine basis, many others are implementing the HIL indices into front-end automation, instrumentation, or middleware systems. By doing so, results can be either reported with comment cautioning clinicians to the potential interference or cancelled for recollection or repeat.
The difference between a patient’s laboratory test result and a prior result found to have exceeded a predefined limit (ie, the Delta check) is a metric that should be monitored. For some parameters, where the levels are strictly regulated to maintain bodily homeostasis (eg, MCV, aPTT, sodium, potassium, hemoglobin), Delta checks should be investigated by the lab internally to first rule out specimen mislabeling, clerical error, or possible analytical error. Once a potential error is identified, a discussion between the relevant clinician(s) and a laboratory technologist to investigate possible pre-analytical errors may be initiated. These could include specimens collected from IV lines, mislabeling, or actual changes in a patient’s condition.
The pre-analytic phase variables affect a wide range of laboratory testing and account for up to 70% of total laboratory errors. Thus, clinical laboratories face a major challenge in improving pre-analytical processes and ensuring quality tests. TABLE 5 indicates a sampling of general approaches to pre-analytical improvement that can be adopted by clinical laboratories.
To address these challenges, clinical laboratories should create a strategy for error detection and reduction, and this strategy should incorporate standardization of laboratory manuals, SOPs, and P&Ps, and complement a continuous quality improvement program.
Qing He Meng, PhD, MD, DABCC, FAACC, is professor and section chief of clinical chemistry laboratories, department of laboratory medicine, at The University of Texas MD Anderson Cancer Center. He earned his MD in 1985 from Xuzhou Medical College, Jiangsu, China and his PhD in Clinical Chemistry from the University of Helsinki, Finland in 1999. Qing has served as the President of the Canadian Academy of Clinical Biochemistry and as the Chair of Division Management and Membership Taskforce for AACC, in addition to numerous other committee appointments over the years. He currently sits for the Committee on Analytical Quality for IFCC and the AACC Science and Practice Core Committee, and is Chair of the AACC Texas section and Chair of AACC’s Tumor Markers and Cancer Diagnostics Division.
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