Laboratory Errors Research Articles

Evaluation of 18 quality indicators from the external quality assurance preanalytical programme of the Spanish Society of Laboratory Medicine (SEQCML).

Most errors in laboratory medicine occur in the pre- and post-analytical phases of the total testing process (TTP). In 2014, the Spanish Society of Laboratory Medicine (SEQCML) started the current Preanalytical Phase EQA Programme, with the objective of providing a tool for the improvement of the preanalytical phase. The aim of this study was to review the evolution of quality indicators (QI) and the comparability of established performance specifications (PS) with other EQA programmes. In the SEQCML programme, participants were asked to register rejections of the main specimens and the causes for rejections. Data collected from 2014 to 2017, and then reviewed biennially (2018-2019), was used to calculate the percentiles; p25, p50, p75, and p90 for every round, and their means were set as PS. These PS were compared with the results of other programmes. The evolution of QI results for 2018-2019 period showed general maintenance or improvement, e.g., a significant decrease in the number of serum samples with a haemolytic index≥0.5g/L, except for EDTA and citrate samples handle, maybe for an improvement in detection. The comparison with PS for the QI of the IFCC Working Group "Laboratory Errors and Patient Safety" and the Key Incident Management and Monitoring System (KIMMS) programme of the RCPA showed comparable results, supporting the validity of the established specifications. The PS obtained are a helpful tool for benchmarking and to identify processes of the preanalytical phase whose improvement should be set as a priority.

M102 Laboratory errors in clinical biochemistry: The quality of laboratory testing in B.P. Koirala institute of health sciences, Nepal

M102 Laboratory errors in clinical biochemistry: The quality of laboratory testing in B.P. Koirala institute of health sciences, Nepal

Perbedaan Hasil Pemeriksaan Hematologi yang Segera Dilakukan Pemeriksaan dan yang Ditunda pada Suhu Ruangan dan Refrigerator menggunakan Sysmex XP-300

An automatic hematology analyzer (hematology analyzer, ha) is a routine hematology examination tool used extensively in clinical laboratory services. Sysmex XP-300 is the HA 3-part diff that is the most straightforward ha impedance examination of the number of cells based solely on cell sizes. The storage temperature and incubation times affected the complete blood count examination. This study determined the difference between hematology and EDTA's immediate examination of blood samples and was delayed at room temperature and temperature using a Sysmex XP-300. Hopefully, this research could be developed with a method of validation of checks to minimize errors in clinical laboratories. Experimental research methods with a pre-experimental design through the one group afflicted posttest design with a sample of as many as 15 laboratories, and we analyzed with ANOVA. The results of this study have WBC, RBC, HGB, HCT, MCV, MCH, and MCHC different results at room temperatures 2, 24, 48, and 72 hours compared to temperature fluctuations; the PLT parameters alone show no significant difference when it comes to prompt examinations with delays in the application. Morphology of erythrocyte is influenced by temperature, and storage should be hypotensive.

The diagnostic pitfalls and challenges associated with basic hematological tests

Several generations of automated hematology analyzers are currently being used to determine a wide range of hematological parameters. As their results form the basis of many medical interventions, it is required that they undergo analytical validation. Samples flagged as being pathological or non-diagnostic require re-testing in a different mode, revision, or additional diagnostic workup (e.g. microscopic smear). In order to avoid mistakes, close cooperation and continuous communication are needed between laboratory and medical staff. To address this, in this review we discuss the most frequent errors and pitfalls associated with the preanalytical and analytical phases of basic hematological tests. While not all diagnostic pitfalls are avoidable, this guidance regarding potentially problematic diagnostic situations will allow for their quick verification at the laboratory stage. An awareness of the causes of errors and of the existence of pitfalls can lower the costs of analytical procedures by minimizing the need to repeat analyses of potentially pathological samples, and have a positive impact on patient safety. In addition, reducing the potential for laboratory errors can improve the accuracy of medical diagnoses and avoid unnecessary treatment.

Innovation Research on Fingerprint and DNA Identifications

In this study, two models for fingerprint and DNA identifications are constructed based on modern technologies, while offering significant advances over prior models. Our models have high credibility, obtaining relatively accurate results under different circumstances. Under different assumptions, this model tests the probability of the validity in the statement that human fingerprints are unique to be 93.94%. In other words, the percentage of misidentification is 6.06%. This model is a robust fingerprint identification method that can tolerate highly nonlinear deformations. The model is tested on the basis of a self-built database, proving that the model has high credibility, and convincing results are obtained from sensitivity analysis. In order to estimate the odds of misidentification by DNA evidence, we emphasized on two factors that might contribute to misidentification: random match probability and the probability arising from laboratory errors. Then, a model is developed using Bayes’ theorem to reveal the inherent relationship between them, which carries some reference value. The probability of matching by DNA evidence is estimated based on the changes in the significant level. Finally, the probabilities of misidentification by both fingerprint evidence and DNA evidence are compared using numerous data. We found that the probability of the former is 6.06% and that of the latter is smaller than 4.0 x 10?10. Therefore, it can be concluded that DNA identification is far better than that of fingerprint identification.

Patient Safety, Clinical Microbiology, and Collaborative Healthcare

“Right to health” is a universal right inclusive of a culture of safety. This review aims to highlight how clinical microbiology laboratories can contribute to patient safety. They can bring down medical errors through clinical collaboration and quality control. Timely and accurate inputs from microbiology laboratory help in clinical correlation and aid in safe patient care. Through internet search, using keywords such as “medical errors” and “quality assurance,” global burden of medical errors has been compiled. References have been taken from guidelines and documents of standard national and international agencies, systematic reviews, observational studies, retrospective analyses, meta-analyses, health bulletins and reports, and personal views. Safety in healthcare should lay emphasis on prevention, reporting, analysis, and correction of medical errors. If not recorded, medical errors are regarded as occasional or chance events. Global data show adverse events are as high as 10% among hospitalized patients, and approximately two-thirds of these are reported from low- to middle-income countries (LMICs). This includes errors in laboratories as well. Clinical microbiology can impact patient safety when practiced properly with an aim to detect, control, and prevent infections at the earliest. It is a science that integrates a tripartite relationship between the patient, clinician, and a microbiology specialist. Through collaborative healthcare, all stakeholders benefit by understanding common errors and mitigate them through quality management. However, errors tend to happen despite standardization and streamlining all processes. The aim should be to minimize them, have fair documentation, and learn from mistakes to avoid repetition. Local targets should be set and then extended to meet national and global benchmarks.

Safe handovers: Safe patients-why good quality structured handovers in the transfusion laboratory are important.

Effective transfer of information relating to patient care is vital in healthcare. In the UK formal handover is an established and well reported process in the clinical setting but less so in transfusion laboratories. Blood transfusions occur within many hospital specialities and across clinical and laboratory staff shifts, making robust handover critical for safe practice. Failure to adequately transfer information relating to pending or ongoing provision of blood components during shift handover in the laboratory can have an adverse impact on patient care. To identify transfusion errors where laboratory handover was considered a contributory factor. Serious adverse events and reactions reported to the UK haemovigilance scheme, Serious Hazards of Transfusion (SHOT), involving handover in the transfusion laboratory were reviewed for a 6-year period. Laboratory incidents involving handover were mainly associated with incorrect blood component transfused-specific requirements not met (IBCT-SRNM) and delays in provision of blood components transfusion, with 16.6% of these cases involving major haemorrhage situations. Handover was found to be insufficient in most cases, no handover was completed in 29.5% of cases, inadequate written handover accounted for 14.8% cases, and inadequate verbal for 5.7% of cases. Poor handover can lead to laboratory errors, particularly delays in provision of blood components. Embedding effective handover processes in the laboratory and including handover time within the shift working pattern, may help reduce errors and ensure continuity of care. Handover should be considered a task that is built into laboratory routine practices, ensuring effective transfer of information and appropriate follow up actions are taken. SHOT have created a handover template which can be adopted in laboratories to formalise this process.

Rates and Reasons of Laboratory Sample Rejection due to Pre-analytical Errors in Clinical Settings.

Laboratory analysis errors in procedure or interpretation may be seen during the process of completing physician test orders. They may also result in rejection of the requests due to some applicability reasons. Hence, this study was carried out to determine the rate and reasons for such rejections in clinical settings. This cross-sectional comparative study was performed on 104008 laboratory tests in a one-year period in terms of the percentage and type of errors that occurred in Shahid Bahonar Hospital in Kerman, Iran, in 2018. The types of studied errors included hemolysis, sample clotting, insufficient sample size, and mistakes in labels or absence of labels on the sample. In this study, 104008 laboratory tests were performed, with 2299 (2.21%) sample rejections, 456 (32.31%) complete blood count (CBC) sample clotting; 417 (29.38 %) hemolysis; and 150 (17.47 %) inadequate sample volume as the majority of errors. There was no statistically significant relationship between pre-analysis errors and clinical aspects (P=0.124). According to the results, it may be concluded that considering the high prevalence of laboratory errors in comparison with the majority of other studies, continuous training courses and determination of the causes of these errors are crucial to attaining better function and basic knowledge.

Quality specimen project

Most laboratory errors occur in the pre-analytical phase of testing. Quality sample collection, handling and transport are paramount to quality results and patient care. Pre-analytical error can produce erroneous patient results, prompt a recollection and is a significant cost to the laboratory. The Quality Specimen Project has been of great value to the organisation. We have identified the most common pre-analytical errors, where they are occurring, which collectors they relate to and how much these errors are costing. One of the areas we identified for improvement was unspun serum tubes. Our usual practice is to ensure all serum tubes are spun at the site of collection. Since 2018 we have seen the number of unspun samples per year almost halved. This has important implications for our patients undergoing glucose tolerance tests for the diagnosis of diabetes mellitus. This project has now been automated such that area managers receive a monthly ‘Quality Specimens’ report. These reports can be filtered to include a specific area or collector and assists the implementation of further training for specific collection sites or collectors. The Quality Specimen Project is ongoing and has created a benchmark for us to use as a target for improvement.

Variation in Laboratory Reports: Causes other than Laboratory Error.

When a sample of an individual is measured at different times at the same or different clinical laboratory, the results are always different, even the state of health of an individual is the same. This disparity in the results from clinical laboratories might confuse diagnosing, treating, and monitoring disease. Patients and healthcare professionals usually interpret these differences as laboratory errors. However, this might not always be the case, because laboratory test results are highly variable and are neither consistent nor comparable due to several reasons other than laboratory error, namely preanalytical variation, biological variation, and analytical variation.

Reliability of environmental DNA surveys to detect pond occupancy by newts at a national scale

The distribution assessment and monitoring of species is key to reliable environmental impact assessments and conservation interventions. Considerable effort is directed towards survey and monitoring of great crested newts (Triturus cristatus) in England. Surveys are increasingly undertaken using indirect methodologies, such as environmental DNA (eDNA). We used a large data set to estimate national pond occupancy rate, as well as false negative and false positive error rates, for commercial eDNA protocols. Additionally, we explored a range of habitat, landscape and climatic variables as predictors of pond occupancy. In England, 20% of ponds were estimated to be occupied by great crested newts. Pond sample collection error rates were estimated as 5.2% false negative and 1.5% false positive. Laboratory error indicated a negligible false negative rate when 12 qPCR replicates were used. Laboratory false positive error was estimated at 2% per qPCR replicate and is therefore exaggerated by high levels of laboratory replication. Including simple habitat suitability variables into the model revealed the importance of fish, plants and shading as predictors of newt presence. However, variables traditionally considered as important for newt presence may need more precise and consistent measurement if they are to be employed as reliable predictors in modelling exercises.

A study of pre-analytical errors in the clinical biochemistry laboratory at Victoria hospital, BMCRI, Bangalore

To categorize and calculate the percentage error of pre-analytical variables in the clinical biochemistry laboratory. Prospective observational study conducted for two months with documenting the frequency and type of pre-analytical errors occurring in venous samples. The total errors recorded were 1.31%. Insufficient volume followed by haemolysis amounted to a major proportion of errors. Continuous pre-analytical phase evaluation and taking corrective measures to make this phase error-free, have to be done.

Study of Pre-Analytical Errors in a Clinical Biochemistry Laboratory

Background: A diagnostic error was a much wider issue in the Laborotory Medicine and further steps are required towards improving in Understanding the issue to reduce the errors in Lab. The current study about the nature of laboratory testing associated errors mainly concerns from ordering test to interpretation of results and importance in reducing errors. Objective: To study errors in 24 hrs lab of GGH Kurnool for 3 months over the whole testing cycle including pre-analytical, analytical, and post-analytical phases. Materials and methods: The number of different type of errors in Pre- analytical, analytical and post-analytical phases was recorded on designed proforma for 2 months from Oct 2021 to NOV 30 2021. All collected data was entered and analysed by using SPSS version 21. Results: Over 2 months, all venous and arterial blood samples were received in 24 hrs lab of GGH. Of the 1, 80, 011 samples received during the study period, 2340 samples were found to be unsuitable for testing, accounting for 1.30% of the rejection. All these samples were rejected due to different types of pre-analytical errors that are due to All these samples were rejected due to different types of pre-analytical errors that are due to misidentification (0.06%), incorrect tube (0.1%) missing samples (0.06%), draw from IV site (0.09%), inadequate samples (0.5%), wrong timing of sample collection (0.09%), hemolysed samples (0.3%) and lipemic samples (0.1%). Conclusions: The use of a consensually-defined list of evidence-based Quality indicators to be applied in the accreditation programs of clinical laboratories according to the current International Standard (ISO 15189:2012) is an effective tool for improving quality, decreasing the risk of errors and increasing patient safety.

Evaluation of completeness of hematology requisition forms leading preanalytical errors in laboratory of a tertiary care teaching hospital

Background: Laboratory services provide crucial information for the optimal management of patients in a hospital setting. A crucial step in the preanalytical phase of sample testing is the correct filling up of the laboratory requisition form (LRF). Objectives: this study as planned to identify the possible parameters for pre analytical errors. Methodology: We analyzed the LRF submitted to the Hematology Section of Central Laboratory of a tertiary care teaching hospital from Hyderabad to assess the degree of completeness of the submitted LRF as a cause of preanalytical error in the laboratory workflow. Randomly selected laboratory forms submitted along with specimens to the Central Hematology Laboratory of a tertiary hospital over a period of 1 month for their completeness, as laid down in standard operating procedures. Collected data were entered in an Microsoft Excel Worksheet and analyzed by SPSS version 23. Only the patient's name, identification number, gender, type of sample, and examination requested were correctly filled. Results: Patient contact details were available only in 0.82% of LRFs. The diagnosis and clinical history were correctly filled in only 39.62% and 0.27% LRFs, respectively. There was a significant lack of information regarding the doctor requesting the investigation (doctor's name written in 8.47% of forms and doctor's contact telephone number written in 0.27% of forms evaluated). It was also found that the present format of the LRF was inadequate for providing the necessary details of the samples received. Conclusion: The existing LRF needs to be re-designed to enable the clinicians to provide more clinical details and patient-related details. Clinicians need to be sensitized regarding the necessity of submitting correctly filled LRFs. The use of system generated online requisition forms can also be done, where forms cannot be submitted unless complete.

Assessment of Total Laboratory Errors in Clinical Chemistry Laboratory: Experience at a Tertiary Care Hospital

Background: Efficient Laboratory services are the backbone of the modern healthcare facility. 70% of all medical decisions are based on laboratory data. We need to assess errors in the testing process to reduce their negative effect on patient outcomes and the entire healthcare sector. Objective: To assess Total Laboratory Errors (Pre-analytical, analytical, post-analytical) in the clinical chemistry laboratory. Materials and Methods: This cross-sectional study was conducted in Clinical Chemistry Laboratory, Sheikh Zayed Hospital, Rahim Yar Khan from 1st January to 30th June 2021. All blood samples received in Chemical Pathology Section for routine chemistry analysis in 6 months were included in the study using a consecutive sampling technique. 61891 blood samples were assessed for the following pre-analytical errors: Hemolysis, icterus, lipemia, incorrect order of draw, insufficient quantity, wrong tube, mislabeled specimen, missed patient information. Analytical errors were a violation of westgard rules while post-analytical errors included uninformed critical results, prolonged turnaround time and data transcription errors. Urine & other body fluids such as cerebrospinal fluid (CSF), ascitic fluid, pleural fluid, pericardial fluid received for chemical analysis were excluded. Data analysis was done using SPSS 16. Results: Out of the total laboratory errors 5574, pre-analytical errors were the most frequently occurring errors 4512 (80.95%) followed by post-analytical errors 1017 (18.25%) & analytical errors 45 (0.80%). The most frequently occurring pre-analytical error was hemolysis (64.38% of 80.95%). The most frequently occurring post-analytical error was uninformed critical results (70.79% of 18.25%). Conclusion: Pre-analytical errors have the highest percentage in total lab errors followed by post-analytical errors. Errors in the analytical phase are less due to Internal Quality Control & laboratory automation.

Direct cost analysis for 32,783 samples with preanalytical phase errors.

OBJECTIVEErrors in the laboratory process often occur in the preanalytical phase (PA). The study aims to calculate the direct cost elements of PA errors, including material, logistics, transfer, personnel workforce, and medical waste.METHODSMedical laboratory PA phase errors were retrospectively reviewed using the Laboratory Information Management System. We evaluated the whole 2019 laboratory data of the 836-bed Health Sciences University Umraniye Training and Research Hospital (UTRH). We assessed the direct cost elements of PA errors, such as those related to material, logistics, transfer, human resources, and waste. We performed the procedure for both samples analyzed in the hospital and transferred to the central laboratory.RESULTSWe analyzed 1,939,650 patient samples and 46,534,532 parameters studied in 2019 for UTRH. The rates for rejected tests and rejected samples (tube) for UTRH were noted as 0.32% and 1.7%, respectively. The total direct cost for PA errors was TRY 438,284.51 (68,918.07 euros) for 32,783 patient samples and 147,893 tests. We calculated the total cost for PA test errors detected in the hospital as TRY 390,238.06, while the total cost for PA test errors detected in the central laboratory was TRY 48,046.45. 89% of the total cost was for PA errors detected in the hospital, and 11% was for the errors detected in the central laboratory. The 2019 direct PA error cost we calculated based on our hospital’s data was 0.153% of the 2019 hospital operating cost. We calculated the direct cost per rejected sample as TRY 13.37 (2.1 Euro).CONCLUSIONProviding reliable laboratory service with the least possible financial loss is one of the main goals in terms of laboratory medicine. In achieving this goal, the prevention of error costs is a priority. The direct cost elements for the PA phase, where laboratory errors are concentrated, can be easily identified. The amount of PA phase error direct cost will attract the attention of health policy decision-makers and field professionals and inspire further research. Therefore, we tried to determine a threshold cost regarding interventions and practices required to prevent PA phase errors.

S126: AN EU -- ARISE INITIATIVE GAP ANALYSIS APPROACH TO IMPROVING THE QUALITY OF LABORATORY SYSTEMS IN SUB-SAHARAN AFRICA.

Background: Accurate and reliable laboratory results are critical to diagnostics in sickle cell disease (SCD) newborn screening. In sub-Saharan Africa, due to neglect of some national health laboratories, there has been persistently high levels of laboratory error, lack of functioning quality management systems and laboratory accreditation. ARISE (African Research and Innovative Initiative for Sickle Cell Education) is a research project funded by the EU. It involves a secondment plan across 8 work packages between institutions in EU (Italy, France, UK and Cyprus) and non-EU (Nigeria, Lebanon, Kenya and USA) countries, creating an interagency and multidisciplinary staff exchange programme to share best practices in newborn screening, diagnosis and treatment of SCD, leading to improved disease outcomes. Secondments include medical, nursing, laboratory, administrative, academic and research professionals. Led by the RCPath and INSERM, WP3 is tasked with improving laboratory diagnostics, capacity, and quality assurance systems for population screening, diagnosis, treatment and monitoring of SCD through assessments, training and mentoring of laboratory health professionals. This is facilitated by baseline, interim and final gap assessments Aims: To compare a baseline gap analysis at the commencement of the project in 2019 with an interim gap assessment 2 years into the project, following secondments to UK laboratories from Nigeria To identify areas of increased focus and support during planned secondments, workshops, and virtual lectures Methods: 6 partner organisation laboratories participated in the baseline gap assessment in 2019, while 10 laboratories participated in the interim gap assessment in 2021, due to new institutions joining the ARISE initiative. An electronic online questionnaire (40 questions) was sent to each laboratory lead. Results: Survey results of the 6 laboratories that undertook both the baseline and interim gap assessment were analysed. A sample of comparative results between 2019 and 2021 include the following: Working towards accreditation - 2/6 versus 3/6: There is the need for each laboratory to progress towards an application for accreditation. SOP’s available for tests and processes - 38 versus 32: This might suggest standardisation and rebranding of documents previously labelled as SOP’s. No schedule of SOP revisions in 0/6 laboratories versus 4/6 laboratories: This clearly demonstrates an understanding that SOP’s need to be updated via a document management system. Scientist certified competences - 22 versus 22. Self -assessed improvement targets - 18 versus 15. Laboratory lead for QA - 3/6 versus 6/6: The importance of QA leadership for each laboratory identified prior to interim assessment. Summary and Recommendations: Early evidence suggests that ARISE secondments have facilitated improved approaches to laboratory governance procedures and QA. Mentoring by EU partner laboratories will further enhance this. ARISE related workshops and train - the- trainer programmes will support capacity building and skills transfer. A final assessment will be undertaken at the end of the ARISE initiative. Conclusion: The ARISE initiative will build on the early success of improvement in laboratory systems, to further support service development and capacity building. The target for each partner laboratory will be a timeline of application to full accreditation

Influence of Different Fabrication Techniques on the Accuracy of Radiographic Scan Templates in Cases of Full-Arch Computer-Guided Implant Placement: An In Vitro Study.

To evaluate the influence of using different fabrication techniques, including intraoral scans, CBCT scans of patients' existing dentures, or denture duplicates, on the accuracy of radiographic scan templates. The influence of selecting different segmentation threshold values during reconstruction of CBCT data was also evaluated. A reference model was obtained by scanning five pairs of maxillary and mandibular acrylic complete dentures using a desktop laboratory laser scanner (DWOS 7Series, Dental Wings). Test scans were obtained from intraoral scans of dentures, CBCT scans of dentures and denture duplicates reconstructed at different grayscale segmentation thresholds, and a laboratory scan of denture duplicates. The resultant STL scan files were imported to an open source and cloud storage software (Medit Link) for the accuracy measurements by calculating root mean square estimate (RMSE) between reference and test scans. Collected data were then analyzed. Qualitative analysis was also performed using 3D color maps. The lowest RMSE (352.7) was found with intraoral denture scans. The highest RMSE (1,336.3) was found with the CBCT scans of the denture duplicates at segmentation threshold of -700 grayscale level. Qualitative analysis revealed that the intraoral denture scans exhibited the most homogenous deviation pattern relative to reference lab scans. Within the limitations of this study, the intraoral scans of the patients' existing dentures resulted in the fabrication of the most accurate radiographic scan templates. The improved accuracy of scan templates fabricated using the intraoral scanners can eliminate the possible laboratory errors associated with the conventional technical procedures as well as reduce the inaccuracies resulting from the image processing and segmentation of CBCT data.

Effects of air exposure, time, temperature, and humidity on different serum electrolytes

Background: Pre-analytical, analytical, or post analytical variations can induce, change, or alter the tests results. Laboratory errors lead to unnecessary delays in test report and also increased costs by repeat samples which have become a pain to the patients. Aims and Objectives: The aims of this study were to determine alterations in the concentration of serum sodium (Na+), potassium (K+), and ionized calcium (Ca++) concentration with reference to air exposure, time, temperature, and humidity. Materials and Methods: Fifty samples as case and 50 samples as control were included from a normal healthy population in this study. After getting the samples, first readings were taken for case samples and were uncapped and the remaining samples were set aside capped at 24°C, 20% humidity for half an hour and followed by second reading which was taken. Results: Variation in the mean serum sodium between groups is 0.06 mEq/L (0.04%) and 0.08 mEq/L (0.07%) which is very negligible and insignificant (P > 0.05). The mean level of serum K+ in cases is 4.35 mEq/L and in controls is 4.27 mEq/L. After half an hour, the mean level of serum K+ in cases is 4.51 mEq/L and, in controls, is 4.29 mEq/L. Hence, the variation in results in cases is 0.16 mEq/L (3.68%) and in controls is 0.02 mEq/L (0.47%) which is highly significant (P < 0.05). The mean level of serum Ca++ in cases is 1.15 mmol/L and in controls is 1.17 mmol/L. After half an hour, the mean level of serum Ca++ in cases is 1.09 mmol/L and in controls is 1.16 mmol/L. Hence, the variation in results in cases is 0.06 mmol/L (5.22%) and in controls is 0.01 mmol/L (0.85%) which is highly significant (P < 0.05). Conclusion: Air exposure significantly alters the serum K+ and Ca++ level, but the alteration in serum Na+ level is not significant.

Interventions to Increasing Test Cancellation Rates in Blood Bank Based on a Multi-Year Review.

Apart from the wasted resource, canceled tests cause delay of test results thus affecting patient care. The study aimed to identify the reasons for the test cancellation of samples received in a blood bank laboratory and determine the improvement target. The study retrospectively reviewed the canceled tests in a blood bank laboratory of a hospital in Hail, Saudi Arabia, from January 2017 to December 2020. Records of the canceled test taken from the quality assurance database were utilized to identify the various reasons. A total of 2,017 (2.7%) test cancellations were recorded in the 4-year study period with increasing rates noted. The two primary reasons were specimen quality issues (61.9%) and problems related to test orders (33.9%). The main reason for test cancellation was clotted specimen (48.5%) followed by incorrect test order (15.6%) and duplicate test order (13.9%). Statistically, a significant difference exists between the annual rates and reasons of cancellation, X2 (6, n = 2,017) = 83.24, p < 0.001. Test cancellations due to various reasons remain a significant challenge for clinical laboratories. Detailed analyses on these major reasons can aid in displaying an effective approach to decrease the cancellation rates. Harmonization among inter-professional health workers concerning specimen collection and handling, and involving clinical laboratory personnel could minimize laboratory errors and avoid test cancellations.