Evaluation of Pre-Analytical, Analytical, and Post-Analytical Quality Indicators Before and After Laboratory Audit in a Tertiary Care Hospital

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Periodic audits of laboratory operations can also identify any gaps in the operations and improve the laboratory’s quality management system. Methods: A prospective study was completed to examine laboratory QIs in a busy clinical laboratory from January through December 2025. QIs assessed through this study include sample rejection rate, reasons for sample rejection, re-testing rate, compliance with turnaround times (TAT), biologic alerts, and performance on external quality assessment (EQA) surveys. A laboratory audit was conducted in October 2025, and comparatives were made between laboratory quality indicators both prior to the laboratory audit and after the laboratory audit was completed. Statistical analysis was performed using the Chi-square test. Results: Overall, the study evaluated a total of 1,885,043 samples during the study timeframe. The rate of samples being rejected was consistently 0.14% (p=0.91) for both the pre-audit and post-audit periods of time. The rate of re-test testing was significantly elevated in the post-audit period (0.04% vs 0.06% p<0.001). The number of reports that surpassed the TAT measure decreased from 5.61% in the pre-audit period to 5.01% in the post-audit period (p<0.001). Clotted samples were the primary reason for a sample being rejected, accounting for 47.5% of the total sample rejections, while the second most common reason was due to the incorrect vacuum vial or barcode mismatch. The failed external quality assessment (EQA) tests percentage were also not statistically significantly different between pre-audit 4.91% and post-audit 7.55% (p=0.10). Conclusion: Continual assessment of laboratory quality indicators is essential to identify operational weaknesses and increase the overall quality of laboratory delivery and service. Periodic audits can also assist with optimizing the post-analytical period of laboratory delivery and compliance with TAT. 1. Introduction Laboratories are a key part of the health care system as they provide laboratory test results that influence a large percentage of decisions made in the diagnosis, monitoring, and treatment of a patient [ 1 ]. The accuracy, reliability, and timeliness of laboratory tests have a direct impact on both safety and clinical outcome for patients [ 2 ]. There can be many types of errors in laboratory medicine which can occur at any point in the testing process; this testing process is broken down into three phases of testing; pre-analytical, analytical, and post-analytical [ 3 ]. Although error rates in the analytical phase of the testing process have been reduced significantly due to advances in automation and quality control, the pre-analytical and post-analytical phases account for the majority of laboratory errors which occur during specimen collection, labeling, transporting, and reporting of laboratory results [ 4 ]. Errors that remain undetected and uncorrected can result in rejections of specimens, delayed reporting of laboratory results, and have the potential to cause treatment problems for patients [ 5 ]. Laboratories have used quality indicators (QIs), since their introduction, as tools to monitor the performance of their laboratories and improve their quality management systems [ 6 ]. QIs can be continuously evaluated over time to allow laboratories to identify operational weaknesses, evaluate performance trends over time, and implement corrective actions to improve an organization's laboratory processes [ 7 ]. The globally accepted international standard ISO 15189 specifies that laboratories implement QIs as an integral part of their laboratory QA programs [ 8 ]. Through regular laboratory audits, quality management can be improved by evaluating compliance to existing laboratory procedures and identifying opportunities for improvement [ 9 ]. The current study provides a prospective evaluation of laboratory QIs and evaluates the effect a laboratory audit has had on laboratory pre-analytical, analytical, and post-analytical performance at a large-scale clinical laboratory. 2. Methodology 2.1. Study Design The prospective observational study was performed at a tertiary care hospital from January 1st, 2025 to December 31st, 2025 in a clinical laboratory. The laboratory is currently accredited by The National Accreditation Board for Testing And Calibration Laboratories (NABL). Furthermore, it runs its operations according to the requirements of ISO 15189:2022 for all medical laboratories Quality Management System. Additionally, the clinical laboratory has an exceptionally high volume of samples coming from both inpatient and outpatient services, all procedures for specimen collection, processing, analysis and reporting follow standardized processes as per NABL & ISO procedures; therefore compliance has been met at each step of operations.Ethics approval was granted by the Institutes Ethics Committee before starting this study. 2.2. Quality Indicator Monitoring Key laboratory quality indicators were prospectively monitored across the total testing process including pre-analytical, analytical and post-analytical phases. The pre-analytical indicators included the rejection rate of samples and causes for sample rejection such as clotted samples, hemolysed specimens, insufficient quantity, wrong vacutainer or barcode errors, empty vacutainers, and duplicate barcodes. The analytical indicators included retest rate and EQA (External Quality Assessment) performance, while the post-analytical indicators included compliance with turnaround time (TAT) and the number of critical alerts reported. 2.3. Definitions of Quality Indicators Turnaround Time (TAT) is defined as the time from receiving a sample in the laboratory until the validated laboratory report is issued. If the time taken to issue a report exceeds the pre-defined TAT, this may be referred to as delayed reporting [ 10 ]. Re-testing of samples for the purpose of confirming that they have abnormal/unexpected results prior to when they are validated as final results refers to repeat testing of those samples [ 2 ]. EQA Failure is defined as a failure of EQA by a score of ± 2.9 through the criteria set forth by the EQA provider that does outside of those set forth by the EQA provider [ 11 ]. 2.4. Audit Intervention During the month of October 2025, a laboratory audit was carried out as one of the routine quality assurance activities. Essentially, the purpose of the audit was to assess compliance with standard operating procedures regarding specimen collection, sample handling, instrument service and maintenance, quality control procedures, and reporting of results. The corrective actions identified during the audit supported training of personnel to improve processes in the laboratory. For purposes of analytical comparisons between the two time periods (i.e., before and after the audit), the study time period was subdivided into the pre-audit (January - September 2025) and post-audit (October - December 2025) phases. 2.5. Data Collection and statistical analysis Monthly data related to laboratory quality indicators were collected from quality records and the quality monitoring logs maintained as part of the quality management system. This data was subsequently compiled using Microsoft Excel to prepare for statistical analysis. Descriptive statistics were applied to the data to summarize the frequency and proportion of each of the quality indicators. Comparisons were made between the pre-audit and post-audit periods using the Chi-square test, in order to determine if there was a difference in the proportions between the two time periods. The statistical analyses were performed using GraphPad Prism with an alpha level of 0.05 as the criterion for making inferences about statistically significant differences. 3. Results 3.1. Laboratory Workload During the study period from January to December 2025, a total of 1,885,043 samples were processed in the clinical laboratory. Of these, 1,434,410 samples (76.1%) were processed during the pre-audit period (January–September), while 450,633 samples (23.9%) were processed in the post-audit period (October–December). Monthly workload and sample rejection rates are summarized in Table 1 . Table 1 Monthly Laboratory Workload and Sample Rejection Rate Month Total Samples Received Sample Rejections Rejection Rate (%) January 154,835 231 0.15 February 138,278 457 0.33 March 154,416 207 0.13 April 154,850 212 0.14 May 160,939 174 0.11 June 154,379 163 0.11 July 169,552 241 0.14 August 169,191 204 0.12 September 177,970 184 0.10 October * 157,708 219 0.14 November 119,251 224 0.19 December 173,674 205 0.12 *Audit performed in October. 3.2 Pre-analytical quality indicators During the study, the rate of rejected samples as a percentage of total processed samples were consistently low. For instance, during the pre-audit period 2,073 of the 1,434,410 processed samples (0.14%) were rejected, but the same rejection rate (0.14%) occurred in the post-audit period (648 rejected of 450,633 processed). The difference in overall rejection rate between pre-audit vs. post-audit periods was not statistically significant (p value = 0.91). The lab quality indicators that were evaluated before and after the completion of the audit are listed in Table 2 . Some of the main reasons that caused sample rejections are given in Table 3. Clotted samples accounted for a majority (47.5%) of the total rejected samples, followed by either a wrong vacutainer or errors associated with barcodes (31.9%). Other reasons for rejected samples contained quantity insufficient samples (7.6%), empty vacutainers (2.6%), duplicate barcodes (1.8%) and hemolysed samples (1.5%). There was relatively little difference in terms of number of clotted samples rejected between the pre-audit and post-audit periods (i.e., they were similar). Table 2 Comparison of Laboratory Quality Indicators Before and After Audit Quality Indicator Pre-Audit (Jan–Sep) n (%) Post-Audit (Oct–Dec) n (%) p-value Total Samples Processed 1,434,410 450,633 — Sample Rejections 2,073 (0.14%) 648 (0.14%) 0.91 Re-tests 583 (0.04%) 286 (0.06%) < 0.0001 Out-of-TAT Reports 80,511 (5.61%) 22,597 (5.01%) < 0.0001 Critical Alerts 341 147 — EQA Failures 39 (4.91%) 20 (7.55%) 0.10 3.4 Analytical Quality Indicator During the study, there were 583 and 286 re-tests before and after the audit (0.04% and 0.06% of processed samples, respectively). This represents an increase of re-tests after the audit (p-value < 0.001), suggesting a higher level of confirmation of test results. The performance of external quality assessments (EQA) over the course of the study summarized in Table 5 . During the pre-audit period, there were 39 failures of 795 parameters (4.91%) compared to 20 failures of 265 parameters (7.55%) during the post-audit period, although this difference is not statistically significant (p-value = 0.10). 3.5 Post- Analytical Quality Indicator Table 4 shows the performance indicators for the post-analytical phase of testing. A total of 80,511 out of 1,424,115 tests (5.61%) had a delayed turnaround time (TAT) in the pre-audit period, while 22,597 (5.01%) were delayed in the post-audit period. There was a statistically significant decrease (p < 0.001) in the number of tests with delayed TATs following the audit. A monthly analysis revealed a significant amount of variation in TAT performance, with the highest proportion of reports delayed recorded in August (15.6%) and September (19.4%) due to analyzer malfunctions. After the audit in October, the performance of TATs stabilized over the following months. Critical alerts were reported 341 times during the pre-audit period and 147 times during the post-audit period. Table 4 Monthly Analytical and Post-Analytical Quality Indicators Month Re-tests (n) Out-of-TAT Reports (n) Out-of-TAT (%) Critical Alerts (n) January 52 1,415 0.91 55 February 61 357 0.26 50 March 59 6,826 4.42 36 April 75 6,620 4.28 21 May 45 389 0.24 80 June 62 615 0.40 14 July 80 3,954 2.33 19 August 79 26,387 15.60 12 September 70 34,568 19.42 54 October * 76 8,950 5.68 45 November 73 5,707 4.79 42 December 137 7,940 4.57 60 *Laboratory audit performed in October . Table 5 Monthly External Quality Assessment (EQA) Performance Month EQA Failures (z-score > 2.9) Total Parameters Assessed Failure Rate (%) January 4 93 4.30 February 4 86 4.65 March 3 86 3.49 April 3 93 3.23 May 4 86 4.65 June 10 86 11.63 July 3 93 3.23 August 5 86 5.81 September 3 86 3.49 October * 6 86 6.98 November 5 93 5.38 December 9 86 10.47 *Laboratory audit performed in October. 4. Discussion Clinical laboratories play an integral role in today's health care system, providing the means by which patients are evaluated, diagnosed, and treated for medical conditions. As a key component of the overall healthcare system, the quality of laboratory services is imperative to the effective management of patients and their care. Through monitoring specific laboratory quality indicators throughout all three stages of the total testing process (pre-analytical, analytical, and post-analytical), health care professionals can identify areas of possible error or improvement regarding their laboratories and overall performance. Furthermore, recent literature supports the systematic evaluation of quality indicators as a means of determining objective benchmarks for evaluating laboratory processes and improving patient safety [ 12 , 13 ]. During this research, the total number of samples being discarded continued to be low (0.14%) throughout the whole-time frame when the samples has been accessed, and both IFCC studies show a total rejection of less than 1% for all participating facilities. The study of pre-analytical errors in clinical laboratories found, through the use of quality indicator tracking, low rejection rates could be achieved through early detection of process failures [ 12 ]. Additionally, other groups such as the International Federation of Clinical Chemists (IFCC) have developed quality indicator monitoring systems for evaluating laboratory error rates to ensure that pre-analytical error rates remain within acceptable limits by continuously monitoring their detection rate. Our consistently low rejection rates may be due to utilizing uniform specimen collection practices and, thus, complying with the quality management program that is applicable to all NABL accredited laboratories [ 13 ]. In this study's analysis of reasons for failed specimens, clotted specimens accounted for the most failures, followed by the vacuum tube/barcode issues. Many other studies concerning preanalytical errors in laboratory testing have observed similar results. For example, a number of studies evaluating hematology laboratory errors, and other studies, have also indicated that specimen collection errors are among the most significant reasons for specimen rejection, such as clotted specimens, hemolyzed specimens, and insufficient specimens for analysis [ 14 ]. Another recent study that assessed preanalytical errors concluded that the vast majority of laboratory errors occur as a result of specimen collection or handling errors since these processes often take place outside the direct supervision of laboratory personnel [ 1 ]. These findings indicate that continuous training of phlebotomists and strict adherence to standardized specimen collection protocols should help reduce the prevalence of preanalytical errors. An increase in retesting among patients after an audit intervention was noted in the current study. It could be interpreted that increased retesting is detrimental; however, it may also reflect improved verification processes and increased diligence towards validating results. Many studies have indicated that repeat testing sometimes occurs due to prior abnormal testing results or inconsistent analytical results prior to issuing a final report [ 15 ]. These verification processes must be conducted to preserve reliability of analytical testing results and to prevent the issuance of erroneous testing results. Therefore, it is likely the increased retesting that was observed in our study indicates that the quality assurance practices of that laboratory improved after the completion of the audit. Other important results showed a significant decrease in turnaround time (TAT) failures after the audit, which showed improved post-analytical quality of the service provided by the lab. TAT is generally accepted to be one of the most important measures for performance in laboratory medicine because delaying the reporting of an outcome has an adverse effect on clinical decision-making, as well as management of patients [ 16 ]. During the study in August and September, a large number of TAT failures occurred due to reported breakdown of analyzers at that time. Instrument downtime has been identified as a contributor to the efficiency of laboratories and TAT's ability to process tests in laboratories with large volumes of tests [ 17 ]. After the audit intervention in October, TAT performance returned to stable performance levels for months following the audit, indicating that the corrective action taken during the audit led to improved efficiency and workflow in reporting results. The current study found that the EQA performance show moderate variation from the beginning to the end of study period and there was no statistically significant difference between the pre-audit EQA performance levels and the post-audit EQA performance levels. EQA programs are a critical part of any laboratory quality assurance program, permitting laboratories to independently assess their analytical accuracy and ability to compare analytical results with other laboratories. Through participation in EQA programs, laboratories are able to identify pre- and post-audit EQA performance levels, so that, when necessary, corrective actions can be taken where analytical deviations occurred [ 18 ]. The relatively stable EQA performance of our laboratory suggests that the analytical phase was under appropriate control during the study period as demonstrated elsewhere in the literature supporting that the implementation of automation and standardized quality assurance practices in clinical laboratories has reduced the occurrence of analytical errors. The current study has several strengths, including monitoring laboratory quality measure prospectively in a high-volume NABL-accredited lab and assessing audit-driven quality improvement strategies. However, some limitations are present. The research was limited to a single institution, limiting the generalizability of the results to other laboratory settings. Also, while the analysis focused on laboratory process measures, clinical outcomes of delayed or rejected samples were not examined. Despite these limitations, the results of the current study support the need for continuous laboratory quality measure monitoring and established quality assurance measures through auditing in identifying business inefficiencies and improving laboratory operations. Continuous evaluation of quality measures allows laboratories to identify operational trends, determine the effectiveness of corrective action taken, and create stronger quality assurance systems consistent with global accreditation criteria [ 19 ]. Thus, the development of consistent quality monitoring programs combined with employee education and periodic audits will increase the dependability, efficiency, and safety of laboratory services leading ultimately to greater patient care. 5. Conclusion The emphasis of this study is on the use of prospective monitoring of laboratory quality indicators to evaluate and enhance laboratory performance within the context of increased volume. Despite an overall lower than expected rejection rate for all samples during the time period covered by the study, frequent monitoring has facilitated the identification of the most commonly occurring pre-analytical errors, including clotted or poorly labelled blood specimens. The audit completed during this time period also resulted in improved post-analytical performance as demonstrated by improved consistency within established turnaround times. Prospective monitoring of laboratory quality indicators enables laboratory staff to continually evaluate operational trends, apply corrective actions, and strengthen their quality management system according to internationally recognized standards for accreditation. Thus, consistent monitoring of laboratory quality indicators along with routine audits and employee training can significantly improve the reliability, effectiveness, and safety of laboratory services leading to improved patient outcomes. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9157899","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614031356,"identity":"452cf9c7-2fc7-47a0-8ec7-eec6b548a9ca","order_by":0,"name":"Anshul Jangra","email":"","orcid":"","institution":"All India Institute of India","correspondingAuthor":false,"prefix":"","firstName":"Anshul","middleName":"","lastName":"Jangra","suffix":""},{"id":614031357,"identity":"74f65dde-12a1-45e3-b0d1-f1509e7cdfac","order_by":1,"name":"Sudhahar Tamizhan","email":"","orcid":"","institution":"All India Institute of India","correspondingAuthor":false,"prefix":"","firstName":"Sudhahar","middleName":"","lastName":"Tamizhan","suffix":""},{"id":614031358,"identity":"38eb12b3-9d77-40ad-a62f-0aa67a103450","order_by":2,"name":"Rupali Bains","email":"","orcid":"","institution":"All India Institute of India","correspondingAuthor":false,"prefix":"","firstName":"Rupali","middleName":"","lastName":"Bains","suffix":""},{"id":614031359,"identity":"4e5f4c55-abd2-46eb-8995-9ca781580674","order_by":3,"name":"Sudip Kumar Datta","email":"","orcid":"","institution":"All India Institute of India","correspondingAuthor":false,"prefix":"","firstName":"Sudip","middleName":"Kumar","lastName":"Datta","suffix":""},{"id":614031360,"identity":"75ca0985-582e-46b4-9002-4878e29b559f","order_by":4,"name":"Ashok Kumar Ahirwar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDCCAwwMzAwMCQxsQJLhQ4UNkGRsPEC0FsYZZ9JAWhqI0wICzLxth2GCuAHf7cOPPxdUpMnzsXMnf+BhO2+3tv0w0JYam2hcWiTPpZlJzziTY9jGzLtNQoLndvK2M4lALcfSchtwaDE4w2AGdE8FI0gLg4HE7WSzA0AtjA2H8Whh//wZqMUeqGXzhwSDc8lm5x8S0sJjIM3blpMI1LJB4kDCATuzGwRskTzDUybNcyYtGeQwyYYDyQlmN4C2JODxC98Z9s2feSqSbef3n938+e8/O3uz8+kPH3yoscGpBQMkglUmEKscBOxJUTwKRsEoGAUjAwAAgxZimmR3d2AAAAAASUVORK5CYII=","orcid":"","institution":"All India Institute of India","correspondingAuthor":true,"prefix":"","firstName":"Ashok","middleName":"Kumar","lastName":"Ahirwar","suffix":""}],"badges":[],"createdAt":"2026-03-18 10:08:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9157899/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9157899/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106402064,"identity":"e01e1f1a-8d02-4ada-855f-c4ba01ec524f","added_by":"auto","created_at":"2026-04-08 09:10:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":744078,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9157899/v1/7622942c-297e-402c-8bf0-3bdf971b6580.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of Pre-Analytical, Analytical, and Post-Analytical Quality Indicators Before and After Laboratory Audit in a Tertiary Care Hospital","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLaboratories are a key part of the health care system as they provide laboratory test results that influence a large percentage of decisions made in the diagnosis, monitoring, and treatment of a patient [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The accuracy, reliability, and timeliness of laboratory tests have a direct impact on both safety and clinical outcome for patients [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. There can be many types of errors in laboratory medicine which can occur at any point in the testing process; this testing process is broken down into three phases of testing; pre-analytical, analytical, and post-analytical [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although error rates in the analytical phase of the testing process have been reduced significantly due to advances in automation and quality control, the pre-analytical and post-analytical phases account for the majority of laboratory errors which occur during specimen collection, labeling, transporting, and reporting of laboratory results [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Errors that remain undetected and uncorrected can result in rejections of specimens, delayed reporting of laboratory results, and have the potential to cause treatment problems for patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLaboratories have used quality indicators (QIs), since their introduction, as tools to monitor the performance of their laboratories and improve their quality management systems [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. QIs can be continuously evaluated over time to allow laboratories to identify operational weaknesses, evaluate performance trends over time, and implement corrective actions to improve an organization's laboratory processes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The globally accepted international standard ISO 15189 specifies that laboratories implement QIs as an integral part of their laboratory QA programs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Through regular laboratory audits, quality management can be improved by evaluating compliance to existing laboratory procedures and identifying opportunities for improvement [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The current study provides a prospective evaluation of laboratory QIs and evaluates the effect a laboratory audit has had on laboratory pre-analytical, analytical, and post-analytical performance at a large-scale clinical laboratory.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design\u003c/h2\u003e \u003cp\u003eThe prospective observational study was performed at a tertiary care hospital from January 1st, 2025 to December 31st, 2025 in a clinical laboratory. The laboratory is currently accredited by The National Accreditation Board for Testing And Calibration Laboratories (NABL). Furthermore, it runs its operations according to the requirements of ISO 15189:2022 for all medical laboratories Quality Management System. Additionally, the clinical laboratory has an exceptionally high volume of samples coming from both inpatient and outpatient services, all procedures for specimen collection, processing, analysis and reporting follow standardized processes as per NABL \u0026amp; ISO procedures; therefore compliance has been met at each step of operations.Ethics approval was granted by the Institutes Ethics Committee before starting this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Quality Indicator Monitoring\u003c/h2\u003e \u003cp\u003eKey laboratory quality indicators were prospectively monitored across the total testing process including pre-analytical, analytical and post-analytical phases. The pre-analytical indicators included the rejection rate of samples and causes for sample rejection such as clotted samples, hemolysed specimens, insufficient quantity, wrong vacutainer or barcode errors, empty vacutainers, and duplicate barcodes. The analytical indicators included retest rate and EQA (External Quality Assessment) performance, while the post-analytical indicators included compliance with turnaround time (TAT) and the number of critical alerts reported.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Definitions of Quality Indicators\u003c/h2\u003e \u003cp\u003eTurnaround Time (TAT) is defined as the time from receiving a sample in the laboratory until the validated laboratory report is issued. If the time taken to issue a report exceeds the pre-defined TAT, this may be referred to as delayed reporting [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Re-testing of samples for the purpose of confirming that they have abnormal/unexpected results prior to when they are validated as final results refers to repeat testing of those samples [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. EQA Failure is defined as a failure of EQA by a score of \u0026plusmn;\u0026thinsp;2.9 through the criteria set forth by the EQA provider that does outside of those set forth by the EQA provider [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Audit Intervention\u003c/h2\u003e \u003cp\u003eDuring the month of October 2025, a laboratory audit was carried out as one of the routine quality assurance activities. Essentially, the purpose of the audit was to assess compliance with standard operating procedures regarding specimen collection, sample handling, instrument service and maintenance, quality control procedures, and reporting of results. The corrective actions identified during the audit supported training of personnel to improve processes in the laboratory. For purposes of analytical comparisons between the two time periods (i.e., before and after the audit), the study time period was subdivided into the pre-audit (January - September 2025) and post-audit (October - December 2025) phases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data Collection and statistical analysis\u003c/h2\u003e \u003cp\u003eMonthly data related to laboratory quality indicators were collected from quality records and the quality monitoring logs maintained as part of the quality management system. This data was subsequently compiled using Microsoft Excel to prepare for statistical analysis. Descriptive statistics were applied to the data to summarize the frequency and proportion of each of the quality indicators. Comparisons were made between the pre-audit and post-audit periods using the Chi-square test, in order to determine if there was a difference in the proportions between the two time periods. The statistical analyses were performed using GraphPad Prism with an alpha level of 0.05 as the criterion for making inferences about statistically significant differences.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Laboratory Workload\u003c/h2\u003e \u003cp\u003eDuring the study period from January to December 2025, a total of 1,885,043 samples were processed in the clinical laboratory. Of these, 1,434,410 samples (76.1%) were processed during the pre-audit period (January\u0026ndash;September), while 450,633 samples (23.9%) were processed in the post-audit period (October\u0026ndash;December). Monthly workload and sample rejection rates are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMonthly Laboratory Workload and Sample Rejection Rate\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Samples Received\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample Rejections\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRejection Rate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154,835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154,416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154,850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160,939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154,379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169,552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169,191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e177,970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOctober\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157,708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119,251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173,674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Audit performed in October.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Pre-analytical quality indicators\u003c/h2\u003e \u003cp\u003eDuring the study, the rate of rejected samples as a percentage of total processed samples were consistently low. For instance, during the pre-audit period 2,073 of the 1,434,410 processed samples (0.14%) were rejected, but the same rejection rate (0.14%) occurred in the post-audit period (648 rejected of 450,633 processed). The difference in overall rejection rate between pre-audit vs. post-audit periods was not statistically significant (p value\u0026thinsp;=\u0026thinsp;0.91). The lab quality indicators that were evaluated before and after the completion of the audit are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSome of the main reasons that caused sample rejections are given in Table\u0026nbsp;3. Clotted samples accounted for a majority (47.5%) of the total rejected samples, followed by either a wrong vacutainer or errors associated with barcodes (31.9%). Other reasons for rejected samples contained quantity insufficient samples (7.6%), empty vacutainers (2.6%), duplicate barcodes (1.8%) and hemolysed samples (1.5%). There was relatively little difference in terms of number of clotted samples rejected between the pre-audit and post-audit periods (i.e., they were similar).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Laboratory Quality Indicators Before and After Audit\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality Indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Audit (Jan\u0026ndash;Sep) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-Audit (Oct\u0026ndash;Dec) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Samples Processed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,434,410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e450,633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSample Rejections\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,073 (0.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e648 (0.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRe-tests\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e583 (0.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286 (0.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOut-of-TAT Reports\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80,511 (5.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,597 (5.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCritical Alerts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEQA Failures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (4.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (7.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analytical Quality Indicator\u003c/h2\u003e \u003cp\u003eDuring the study, there were 583 and 286 re-tests before and after the audit (0.04% and 0.06% of processed samples, respectively). This represents an increase of re-tests after the audit (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting a higher level of confirmation of test results.\u003c/p\u003e \u003cp\u003eThe performance of external quality assessments (EQA) over the course of the study summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. During the pre-audit period, there were 39 failures of 795 parameters (4.91%) compared to 20 failures of 265 parameters (7.55%) during the post-audit period, although this difference is not statistically significant (p-value\u0026thinsp;=\u0026thinsp;0.10).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Post- Analytical Quality Indicator\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the performance indicators for the post-analytical phase of testing. A total of 80,511 out of 1,424,115 tests (5.61%) had a delayed turnaround time (TAT) in the pre-audit period, while 22,597 (5.01%) were delayed in the post-audit period. There was a statistically significant decrease (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the number of tests with delayed TATs following the audit.\u003c/p\u003e \u003cp\u003eA monthly analysis revealed a significant amount of variation in TAT performance, with the highest proportion of reports delayed recorded in August (15.6%) and September (19.4%) due to analyzer malfunctions. After the audit in October, the performance of TATs stabilized over the following months.\u003c/p\u003e \u003cp\u003eCritical alerts were reported 341 times during the pre-audit period and 147 times during the post-audit period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMonthly Analytical and Post-Analytical Quality Indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRe-tests (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOut-of-TAT Reports (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOut-of-TAT (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCritical Alerts (n)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26,387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34,568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOctober\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Laboratory audit performed in \u003cb\u003eOctober\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMonthly External Quality Assessment (EQA) Performance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEQA Failures (z-score\u0026thinsp;\u0026gt;\u0026thinsp;2.9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Parameters Assessed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFailure Rate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOctober\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Laboratory audit performed in October.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eClinical laboratories play an integral role in today's health care system, providing the means by which patients are evaluated, diagnosed, and treated for medical conditions. As a key component of the overall healthcare system, the quality of laboratory services is imperative to the effective management of patients and their care. Through monitoring specific laboratory quality indicators throughout all three stages of the total testing process (pre-analytical, analytical, and post-analytical), health care professionals can identify areas of possible error or improvement regarding their laboratories and overall performance. Furthermore, recent literature supports the systematic evaluation of quality indicators as a means of determining objective benchmarks for evaluating laboratory processes and improving patient safety [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring this research, the total number of samples being discarded continued to be low (0.14%) throughout the whole-time frame when the samples has been accessed, and both IFCC studies show a total rejection of less than 1% for all participating facilities. The study of pre-analytical errors in clinical laboratories found, through the use of quality indicator tracking, low rejection rates could be achieved through early detection of process failures [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, other groups such as the International Federation of Clinical Chemists (IFCC) have developed quality indicator monitoring systems for evaluating laboratory error rates to ensure that pre-analytical error rates remain within acceptable limits by continuously monitoring their detection rate. Our consistently low rejection rates may be due to utilizing uniform specimen collection practices and, thus, complying with the quality management program that is applicable to all NABL accredited laboratories [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study's analysis of reasons for failed specimens, clotted specimens accounted for the most failures, followed by the vacuum tube/barcode issues. Many other studies concerning preanalytical errors in laboratory testing have observed similar results. For example, a number of studies evaluating hematology laboratory errors, and other studies, have also indicated that specimen collection errors are among the most significant reasons for specimen rejection, such as clotted specimens, hemolyzed specimens, and insufficient specimens for analysis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Another recent study that assessed preanalytical errors concluded that the vast majority of laboratory errors occur as a result of specimen collection or handling errors since these processes often take place outside the direct supervision of laboratory personnel [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These findings indicate that continuous training of phlebotomists and strict adherence to standardized specimen collection protocols should help reduce the prevalence of preanalytical errors.\u003c/p\u003e \u003cp\u003eAn increase in retesting among patients after an audit intervention was noted in the current study. It could be interpreted that increased retesting is detrimental; however, it may also reflect improved verification processes and increased diligence towards validating results. Many studies have indicated that repeat testing sometimes occurs due to prior abnormal testing results or inconsistent analytical results prior to issuing a final report [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These verification processes must be conducted to preserve reliability of analytical testing results and to prevent the issuance of erroneous testing results. Therefore, it is likely the increased retesting that was observed in our study indicates that the quality assurance practices of that laboratory improved after the completion of the audit.\u003c/p\u003e \u003cp\u003eOther important results showed a significant decrease in turnaround time (TAT) failures after the audit, which showed improved post-analytical quality of the service provided by the lab. TAT is generally accepted to be one of the most important measures for performance in laboratory medicine because delaying the reporting of an outcome has an adverse effect on clinical decision-making, as well as management of patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. During the study in August and September, a large number of TAT failures occurred due to reported breakdown of analyzers at that time. Instrument downtime has been identified as a contributor to the efficiency of laboratories and TAT's ability to process tests in laboratories with large volumes of tests [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. After the audit intervention in October, TAT performance returned to stable performance levels for months following the audit, indicating that the corrective action taken during the audit led to improved efficiency and workflow in reporting results.\u003c/p\u003e \u003cp\u003eThe current study found that the EQA performance show moderate variation from the beginning to the end of study period and there was no statistically significant difference between the pre-audit EQA performance levels and the post-audit EQA performance levels. EQA programs are a critical part of any laboratory quality assurance program, permitting laboratories to independently assess their analytical accuracy and ability to compare analytical results with other laboratories. Through participation in EQA programs, laboratories are able to identify pre- and post-audit EQA performance levels, so that, when necessary, corrective actions can be taken where analytical deviations occurred [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The relatively stable EQA performance of our laboratory suggests that the analytical phase was under appropriate control during the study period as demonstrated elsewhere in the literature supporting that the implementation of automation and standardized quality assurance practices in clinical laboratories has reduced the occurrence of analytical errors.\u003c/p\u003e \u003cp\u003eThe current study has several strengths, including monitoring laboratory quality measure prospectively in a high-volume NABL-accredited lab and assessing audit-driven quality improvement strategies. However, some limitations are present. The research was limited to a single institution, limiting the generalizability of the results to other laboratory settings. Also, while the analysis focused on laboratory process measures, clinical outcomes of delayed or rejected samples were not examined.\u003c/p\u003e \u003cp\u003eDespite these limitations, the results of the current study support the need for continuous laboratory quality measure monitoring and established quality assurance measures through auditing in identifying business inefficiencies and improving laboratory operations. Continuous evaluation of quality measures allows laboratories to identify operational trends, determine the effectiveness of corrective action taken, and create stronger quality assurance systems consistent with global accreditation criteria [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Thus, the development of consistent quality monitoring programs combined with employee education and periodic audits will increase the dependability, efficiency, and safety of laboratory services leading ultimately to greater patient care.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe emphasis of this study is on the use of prospective monitoring of laboratory quality indicators to evaluate and enhance laboratory performance within the context of increased volume. Despite an overall lower than expected rejection rate for all samples during the time period covered by the study, frequent monitoring has facilitated the identification of the most commonly occurring pre-analytical errors, including clotted or poorly labelled blood specimens. The audit completed during this time period also resulted in improved post-analytical performance as demonstrated by improved consistency within established turnaround times. Prospective monitoring of laboratory quality indicators enables laboratory staff to continually evaluate operational trends, apply corrective actions, and strengthen their quality management system according to internationally recognized standards for accreditation. Thus, consistent monitoring of laboratory quality indicators along with routine audits and employee training can significantly improve the reliability, effectiveness, and safety of laboratory services leading to improved patient outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.J., S.T., , S.K.D and A.K.A. contributed to the study conception and design. Data collection and analysis were performed by A.J., ST, and R.B.. The first draft of the manuscript was written by A.J. and A.K.A. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eNordin N, Rahim SNA, Omar WFAW, Zulkarnain S, Sinha S, Kumar S, et al. Preanalytical errors in clinical laboratory testing at a glance: source and control measures. Cureus [Internet]. 2024 Mar 30;16(3):e57243. Available from: https://doi.org/10.7759/cureus.57243\u003c/li\u003e\n \u003cli\u003ePlebani M. Diagnostic Errors and Laboratory Medicine - Causes and strategies. PubMed [Internet]. 2015 Jan 1;26(1):7\u0026ndash;14. Available from: https://pubmed.ncbi.nlm.nih.gov/27683477\u003c/li\u003e\n \u003cli\u003eMehndiratta M, Pasha EH, Chandra N, Almeida EA. Quality indicators for evaluating errors in the preanalytical phase. Journal of Laboratory Physicians [Internet]. 2021 May 26;13(02):169\u0026ndash;74. Available from: https://doi.org/10.1055/s-0041-1729473\u003c/li\u003e\n \u003cli\u003eSciacovelli L, Lippi G, Sumarac Z, West J, Del Pino Castro IG, Vieira KF, et al. Quality Indicators in Laboratory Medicine: the status of the progress of IFCC Working Group \u0026ldquo;Laboratory Errors and Patient Safety\u0026rdquo; project. Clinical Chemistry and Laboratory Medicine (CCLM) [Internet]. 2016 Dec 18;55(3):348\u0026ndash;57. Available from: https://doi.org/10.1515/cclm-2016-0929\u003c/li\u003e\n \u003cli\u003ePlebani M, Scott S, Simundic AM, Cornes M, Padoan A, Cadamuro J, et al. New insights in preanalytical quality. Clinical Chemistry and Laboratory Medicine (CCLM) [Internet]. 2025 Apr 23;63(9):1682\u0026ndash;92. Available from: https://doi.org/10.1515/cclm-2025-0478\u003c/li\u003e\n \u003cli\u003eSciacovelli L, Padoan A, Aita A, Basso D, Plebani M. Quality indicators in laboratory medicine: state-of-the-art, quality specifications and future strategies. Clinical Chemistry and Laboratory Medicine (CCLM) [Internet]. 2023 Jan 20;61(4):688\u0026ndash;95. Available from: https://doi.org/10.1515/cclm-2022-1143\u003c/li\u003e\n \u003cli\u003eAlavi N, Khan SH, Saadia A, Naeem T. Challenges in preanalytical phase of laboratory medicine: rate of blood sample nonconformity in a tertiary care hospital. PubMed [Internet]. 2020 Mar 1;31(1):21\u0026ndash;7. Available from: https://pubmed.ncbi.nlm.nih.gov/32256286\u003c/li\u003e\n \u003cli\u003eGiannoli JM, Vassault A, Carobene A, Liaudet AP, Blasutig IM, Dabla PK, et al. Ensuring internal quality control practices in medical Laboratories: IFCC recommendations for practical applications based on ISO 15189:2022. Clinica Chimica Acta [Internet]. 2025 Mar 8;571:120240. Available from: https://doi.org/10.1016/j.cca.2025.120240\u003c/li\u003e\n \u003cli\u003eZhang L, Jiang K, Chen J, Zhang Z, Zhang L, Xu M, et al. Quality indicators in laboratory medicine: a 2020\u0026ndash;2023 experience in a Chinese province. Clinical Chemistry and Laboratory Medicine (CCLM) [Internet]. 2025 Mar 7;63(8):1573\u0026ndash;81. Available from: https://doi.org/10.1515/cclm-2024-1457\u003c/li\u003e\n \u003cli\u003eDawande PP, Wankhade RS, Akhtar FI, Noman O. Turnaround time: an efficacy measure for medical laboratories. Cureus [Internet]. 2022 Sep 6;14(9):e28824. Available from: https://doi.org/10.7759/cureus.28824\u003c/li\u003e\n \u003cli\u003eKristensen GBB, Meijer P. Interpretation of EQA results and EQA-based trouble shooting. Biochemia Medica [Internet]. 2017 Jan 1;27(1):49\u0026ndash;62. Available from: https://doi.org/10.11613/bm.2017.007\u003c/li\u003e\n \u003cli\u003eAlshaghdali K, Alcantara TY, Rezgui R, Cruz CP, Alshammary MH, Almotairi YA, et al. Detecting preanalytical errors using quality indicators in a hematology laboratory. 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Preanalytical errors in hematology: Insights from a tertiary care hospital. Cureus [Internet]. 2024 Sep 18;16(9):e69641. Available from: https://doi.org/10.7759/cureus.69641\u003c/li\u003e\n \u003cli\u003eAlcantara JC, Alharbi B, Almotairi Y, Alam MJ, Muddathir ARM, Alshaghdali K. Analysis of preanalytical errors in a clinical chemistry laboratory: A 2-year study. Medicine [Internet]. 2022 Jul 8;101(27):e29853. Available from: https://doi.org/10.1097/md.0000000000029853\u003c/li\u003e\n \u003cli\u003eTarekegn N, Mamo AG, Abdulsemed KA, Abdlshikure SA, Mekonnen Z. Evaluating the total laboratory testing process and performance via quality indicators in clinical chemistry and hematology laboratories at Pawi General Hospital, Benishangul Gumz, Northwest Ethiopia: a prospective cross-sectional study. Discover Health Systems [Internet]. 2025 Jul 19;4(1). Available from: https://doi.org/10.1007/s44250-025-00260-4\u003c/li\u003e\n \u003cli\u003eKristensen GBB, Meijer P. Interpretation of EQA results and EQA-based trouble shooting. Biochemia Medica [Internet]. 2017 Jan 1;27(1):49\u0026ndash;62. Available from: https://doi.org/10.11613/bm.2017.007\u003c/li\u003e\n \u003cli\u003eSingh N, Rawat A, Priya R, Mittal A, Chandel V, Mishra J. Strengthening Quality in The Pre-Analytical Phase of Laboratory Medicine: The Role of Quality Indicators. J Neonatal Surg [Internet]. 2025 May 20 [cited 2026 Mar. 7];14(25S):667-71. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/6191\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 3","content":"\u003cp\u003eTable 3 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"accreditation-and-quality-assurance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acqa","sideBox":"Learn more about [Accreditation and Quality Assurance](http://link.springer.com/journal/769)","snPcode":"769","submissionUrl":"https://submission.nature.com/new-submission/769/3","title":"Accreditation and Quality Assurance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9157899/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9157899/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of the most effective ways of ensuring laboratory performance and patient safety is to use quality indicators (QIs) to monitor and analyze laboratory processes across the pre-analytical, analytical, and post-analytical stages of the total testing process. Periodic audits of laboratory operations can also identify any gaps in the operations and improve the laboratory’s quality management system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA prospective study was completed to examine laboratory QIs in a busy clinical laboratory from January through December 2025. QIs assessed through this study include sample rejection rate, reasons for sample rejection, re-testing rate, compliance with turnaround times (TAT), biologic alerts, and performance on external quality assessment (EQA) surveys. A laboratory audit was conducted in October 2025, and comparatives were made between laboratory quality indicators both prior to the laboratory audit and after the laboratory audit was completed. Statistical analysis was performed using the Chi-square test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, the study evaluated a total of 1,885,043 samples during the study timeframe. The rate of samples being rejected was consistently 0.14% (p=0.91) for both the pre-audit and post-audit periods of time. The rate of re-test testing was significantly elevated in the post-audit period (0.04% vs 0.06% p\u0026lt;0.001). The number of reports that surpassed the TAT measure decreased from 5.61% in the pre-audit period to 5.01% in the post-audit period (p\u0026lt;0.001). Clotted samples were the primary reason for a sample being rejected, accounting for 47.5% of the total sample rejections, while the second most common reason was due to the incorrect vacuum vial or barcode mismatch. The failed external quality assessment (EQA) tests percentage were also not statistically significantly different between pre-audit 4.91% and post-audit 7.55% (p=0.10).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinual assessment of laboratory quality indicators is essential to identify operational weaknesses and increase the overall quality of laboratory delivery and service. Periodic audits can also assist with optimizing the post-analytical period of laboratory delivery and compliance with TAT.\u003c/p\u003e","manuscriptTitle":"Evaluation of Pre-Analytical, Analytical, and Post-Analytical Quality Indicators Before and After Laboratory Audit in a Tertiary Care Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 15:30:55","doi":"10.21203/rs.3.rs-9157899/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-20T13:40:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-18T22:26:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T15:39:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220788845108138948999771744967157944874","date":"2026-04-05T20:53:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18910976172157485498799969004735424695","date":"2026-04-01T14:42:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T09:52:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T23:47:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307493471412075240260052957217536433051","date":"2026-03-26T22:01:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74613393304595860694020865892461485100","date":"2026-03-26T16:14:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-26T12:56:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-21T18:32:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-19T13:52:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Accreditation and Quality Assurance","date":"2026-03-18T09:52:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"accreditation-and-quality-assurance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acqa","sideBox":"Learn more about [Accreditation and Quality Assurance](http://link.springer.com/journal/769)","snPcode":"769","submissionUrl":"https://submission.nature.com/new-submission/769/3","title":"Accreditation and Quality Assurance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"484ab401-6a73-4434-b270-f083328c9652","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T13:39:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 15:30:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9157899","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9157899","identity":"rs-9157899","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}