European Journal of Cardiovascular Medicine

The Role of Quality in Laboratory Medicine

Quality assurance in laboratory medicine is the cornerstone of patient-centered healthcare. Laboratory test results influence nearly 60–70% of clinical decisions regarding diagnosis, treatment, and patient outcomes (1,2). As the demand for accurate and timely diagnostics increases, laboratories must ensure excellence across all stages of the Total Testing Process (TTP), which includes pre-analytical, analytical, and post-analytical phases (3,4). Although the analytical phase has seen considerable improvement through automation, quality cannot be achieved without addressing vulnerabilities in the extra-analytical phases, especially given that pre-analytical errors alone account for up to 60%–70% of total laboratory errors (5,6).

Accreditation and the Indian Laboratory Scenario

In India, the National Accreditation Board for Testing and Calibration Laboratories (NABL) has played a significant role in promoting quality through ISO 15189-based guidelines. Accreditation is voluntary but highly encouraged due to its impact on standardization, credibility, and improved patient trust (28). As per NABL records, 1,880 medical laboratories had been accredited by 2021, with many more under evaluation. Essential documents like NABL 112 and NABL 117, aligned with ISO 15189:2012, guide labs through quality management systems (QMS), audits, non-conformance management, and performance indicators. (7,8)

The Shift in Focus: From Analytical to Extra-analytical Errors

Technological advancements in automated analyzers, middleware, and Laboratory Information Systems (LIS) have dramatically decreased the frequency of analytical errors (9,10). However, this shift in capability has redirected quality control efforts toward pre-analytical and post-analytical phases, where human error, manual data entry, and system integration gaps still exist. Studies have shown that typographical errors and issues in sample handling or test requisition forms (TRF) contribute significantly to reporting errors (11,12).

Non-conformance Management Systems (NMS)

The development of Non-conformance Management Systems (NMS) within laboratory QMS frameworks has been recognized as a powerful tool for continuous quality improvement (13,14). These systems employ methods like root cause analysis (RCA), the “5 Whys”, and fishbone diagrams to identify, categorize, and prevent repeat occurrences of laboratory errors. This proactive strategy shifts focus from individual blame to system-level correction. The implementation of robust NMS has proven effective in accredited labs and serves as a model for laboratories aspiring for NABL certification.

Clinical Impact and Stakeholder Involvement

While not all laboratory errors result in patient harm, studies suggest that 3%–12% of laboratory errors have actual or potential clinical consequences (15). Errors in the pre-analytical and post-analytical phases are often outside the laboratory’s direct control, involving stakeholders like physicians, nurses, and phlebotomists. Therefore, interdisciplinary coordination, stakeholder education, and clear standard operating procedures (SOPs) are vital for ensuring error-free diagnostics. Grading errors by severity further helps prioritize interventions based on patient safety risk (16,17).

Aim of the Study

Given the persistent burden of pre- and post-analytical errors, especially typographical mistakes and software entry errors, it becomes imperative to audit these errors systematically. Clinical audits not only highlight current gaps in laboratory quality but also drive improvement in compliance with ISO 15189 and NABL 112 (28). This study was conducted to audit and analyze reporting errors over a multi-year period in a NABL-accredited clinical biochemistry laboratory. It explores root causes, categorizes error types, evaluates error trends, and offers strategies for corrective and preventive actions (CAPA)—contributing to the evolution of quality laboratory practices in the Indian context.

This retrospective, hospital-based observational study was carried out in the Clinical Biochemistry section of the Central Clinical Laboratory (CCL) at Acclin Path labs, India. The study aimed to analyze and categorize reporting errors associated with routine clinical biochemistry and immunoassay tests. It included all blood and body fluid samples such as cerebrospinal fluid, pleural fluid, peritoneal fluid, and urine that were received for biochemical testing. The hospital has a centralized sample collection unit, and samples were collected by trained phlebotomists, nursing staff, paramedical personnel, or resident doctors. These samples were then transported to the clinical biochemistry laboratory using an automated pneumatic tube system designed to preserve sample integrity during transit.

The Clinical Biochemistry section is equipped with advanced instrumentation including fully automated biochemistry analyzers, immunoassay systems, electrolyte analyzers, and specialized platforms for cardiac markers and HbA1c testing. All samples received were processed and analyzed within the biochemistry section, and reports were either sent electronically to inpatient wards or physically collected by patients or their representatives from the outpatient department.

As part of the laboratory’s ongoing quality management system, which aligns with standards set by national accreditation authorities, monitoring and documentation of reporting errors is a routine quality indicator. These errors were manually recorded and categorized into pre-analytical, analytical, and post-analytical phases, based on where the error occurred—whether during sample collection and transport, during the actual analysis, or during the reporting and result communication phase. Root cause analysis was performed for each error type, and corrective and preventive actions (CAPA) were implemented accordingly. This process was a part of the lab’s continuous quality improvement initiatives.

The data collected over the study period from 1st Jan 2024to 1st February 2025, included the total number of samples processed daily, and all observed errors were documented for analysis. The quantitative data were expressed as frequencies and percentages, and statistical comparison of error frequencies was done using the chi-square test.

Table 6. Year-wise Summary of Quality Indicators – Auto vs Manual Report Verification

This table highlights the progressive increase in auto-verification of lab reports over the last decade. In 2015, only 35% of reports were auto-verified, indicating significant manual dependency. By 2024, this increased to 62%, reflecting successful implementation of LIS upgrades, rule-based auto-verification, and middleware systems. While manual review still plays a role for critical values and outliers, this shift towards automation has likely contributed to improved efficiency and reduced post-analytical errors. These indicators are essential for ongoing QMS monitoring and NABL compliance.

The retrospective audit of reporting errors in the Clinical Biochemistry Section over a 10-year period (November 2015 to December 2024) revealed a total of 200 documented reporting errors from approximately 4.72 million processed tests.

1. Distribution by Type of Error

Out of the total 200 errors:Post-analytical errors were the most frequent: 130 cases (65%), all of which were typographical or transcriptional in nature.Pre-analytical errors accounted for 38 cases (19%), with the most common causes being wrong software entry (47.37%), sample collection issues (18.42%), and errors on TRF (13.16%).Analytical errors made up 32 cases (16%), primarily involving wrong test entry on analyzer (28.13%), sample aspiration errors (18.75%), and reagent/sample positioning issues.

2. Year-wise Error Trends

The highest number of errors was observed in 2018 (28 errors) and 2023 (27 errors).2020 showed a spike in error rate per 1000 tests (0.102), likely due to pandemic-related disruptions. Over the years, the overall error rate averaged ~0.042 per 1000 tests. There was a gradual improvement in auto-verification rates, increasing from 35% in 2015 to 62% in 2024, reducing manual dependency and helping to mitigate post-analytical errors.

3. Clinical Impact of Errors

Among the 200 errors:47 (23.5%) were identified as having a potential or actual clinical impact.Pre-analytical errors had the highest clinical impact rate (31.6%), followed by analytical (28.1%), and post-analytical (20%).These errors were associated with incorrect patient preparation, sample integrity, or misreporting that could influence clinical decisions or delay treatment.

4. CAPA and Quality Improvements: Root cause analysis categorized the common contributors to errors and corrective actions were implemented, including: Staff re-training, SOP revision, LIS double-verification, and barcoded e-TRF systems. Preventive strategies such as automated data validation, dual-step verification, and rule-based LIS auto-verification were strengthened over time.
5. Quality Indicators: There was a marked increase in the percentage of auto-verified reports, demonstrating enhanced system integration and quality workflows. Manual verification was still essential for borderline or critical values but steadily decreased, minimizing human error risk.

The Evolving Focus of Laboratory Error Audits

The concept of the Total Testing Process (TTP) provides a structured framework for error analysis by categorizing the entire laboratory workflow into three critical phases—pre-analytical, analytical, and post-analytical. Historically, analytical errors were the primary concern for quality control teams, as they were directly associated with the precision and accuracy of laboratory instruments and assays. However, over the past decade, increased automation, standardization of procedures, and integration of internal quality control (IQC) systems have drastically reduced the frequency of errors within the analytical phase.

Our 10-year retrospective audit strongly reflects this global trend, with only 16% of the total 200 errors originating from the analytical phase. This finding is consistent with studies by Plebani et al. and Bonini et al., which reported that the analytical phase now constitutes a minor proportion of total laboratory errors in well-regulated environments (16, 3). The enhanced performance of modern analyzers, real-time QC monitoring, and rigorous calibration protocols have significantly diminished analytical variability.

Contrastingly, post-analytical errors emerged as the dominant category, accounting for 65% of all documented errors. Most of these were typographical or transcription-related mistakes occurring during result entry or report dispatch. As observed by Gawade et al. (1), such errors persist in setups with partial LIS implementation or manual report compilation, often going unnoticed in audits unless structured error reporting systems are used. These findings underscore the importance of extending quality improvement strategies beyond instrumentation to encompass data handling and communication protocols.

The Persistent Vulnerability of the Pre-analytical Phase

Despite improvements in analytical precision, the pre-analytical phase continues to present notable vulnerabilities, primarily because it involves multiple human-dependent steps such as sample collection, patient identification, labeling, test requisition form (TRF) completion, and sample transport.

In our study, 19% of errors occurred in the pre-analytical phase, and common causes included incorrect software entries, mismatches on TRFs, improper sample collection, and use of wrong vacutainers. These findings are consistent with prior Indian studies by Kotasthane et al. and Toshniwal et al., who reported pre-analytical error rates ranging between 46% and 68%, especially in high-volume tertiary care hospitals (3, 18). Notably, 31.6% of pre-analytical errors in our study had a potential or actual clinical impact, underscoring the high-risk nature of this phase.

These errors often originate from time pressure, poor interdepartmental communication, lack of awareness among clinical and nursing staff, and inconsistent adherence to SOPs. Addressing them requires institution-wide interventions, including regular training of phlebotomists, visual SOPs at collection points, ward-level audits, and cross-verification protocols for test requisition data.

3. Post-analytical Errors: Typing Mistakes with Clinical Consequences

The post-analytical phase, often regarded as a “low-risk” zone, was found to be the most error-prone in our setting. 65% of the total errors were attributed to this phase, primarily due to manual transcription and typographical mistakes during the reporting process. These included wrong result entry, misplaced decimal points, or selecting incorrect test values while uploading results.

Despite being clerical, these errors are far from benign—20% of post-analytical errors had clinical implications, resulting in delayed treatments, patient recalls, and in some instances, revision of reports. These findings mirror observations by Blumenthal and Leape et al., who highlighted the vital role of accurate communication and reporting systems in patient safety (15, 19).

The gradual implementation of Laboratory Information Systems (LIS), coupled with auto-verification features and rule-based middleware, significantly mitigated these issues over time. Notably, our auto-verification rate improved from 35% in 2015 to 62% in 2024, leading to reduced manual entry and a downward trend in such errors. This is supported by Tieman’s analysis of lab automation, which found a direct correlation between LIS integration and improvements in turnaround time (TAT), report accuracy, and staff efficiency (21).

4. Pandemic-induced Challenges and Learning Opportunities

A notable outlier was observed in 2020, where the error rate per 1000 tests reached 0.102, the highest during the audit period. This spike coincided with the peak of the COVID-19 pandemic, which disrupted normal workflows through staff shortages, increased emergency testing, reallocation of resources, and a heavy dependence on manual verifications.

This scenario mirrors the findings of Sodavadiya et al. (2), who documented process breakdowns and equipment-related non-conformances during pandemic-induced stress periods. While challenging, this phase acted as a turning point in our laboratory’s QMS evolution. It led to procurement of new analyzers with in-built safety features, implementation of barcode-based e-TRFs, and upgrades to our LIS to support remote verification and digital TAT monitoring.

Pandemic-era feedback also prompted retraining programs, RCA documentation protocols using fishbone and 5 Why’s techniques, and revision of SOPs across all sections. These interventions not only restored our error rates to baseline but also enhanced long-term resilience in quality assurance practices.

This decade-long audit highlights the changing landscape of laboratory errors, where the focus has shifted from analytical to extra-analytical domains. While analytical errors have declined, thanks to technology and QC protocols, pre- and post-analytical errors remain significant challenges, particularly in high-throughput environments.

The high frequency of post-analytical transcription errors (65%), along with the high clinical risk associated with pre-analytical lapses (31.6%), point toward the urgent need for process re-engineering, staff training, and advanced informatics integration.

Adopting rule-based LIS auto-verification, investing in barcoded e-TRF systems, and institutionalizing regular RCA audits were pivotal to quality improvement. Additionally, ward-level coordination, closed-loop order-to-report mechanisms, and dashboard-based QI tracking are future-ready strategies that align with both NABL 112/117 standards and ISO 15189:2012 guidelines.

To ensure sustainable improvements, laboratories must foster a culture of transparency, proactive error reporting, and system-driven CAPA practices. These approaches not only safeguard patient safety but also enhance laboratory credibility and accreditation readiness in the evolving landscape of healthcare diagnostics.

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