Role of intervention on laboratory performance: evaluation of quality indicators in a tertiary care hospital. Indian J Clin Biochem. 2012;27:61-68. [PMID: 23277714 DOI: 10.1007/s12291-011-0182-7]

Magnitude of extra-analytical errors and associated factors in medical laboratories in thirteen Public Hospitals in Addis Ababa, Ethiopia

Background

Currently, more than two-thirds of medical decisions are made based on laboratory test results. However, due to the compromised pre-analytical and post-analytical phases (or extra-analytical phase), the reporting of incorrect or inappropriate test results was still prevalent. Studies focusing on the prevalence of and factors associated with laboratory error remain very limited in Ethiopia.

Objective

This study aimed to assess the magnitude of extra-analytical errors and associated factors in medical laboratory services in 13 public hospitals in Addis Ababa, Ethiopia, from January to April 2020.

Materials and Methods

A hospital-based cross-sectional study was conducted in the medical laboratories of public hospitals in Addis Ababa from January to April 2020 to assess extra-analytical errors and associated factors. Data were collected from 2401 laboratory request forms ordered within the study period and 169 laboratory professionals working in public hospitals. The collected data were entered and analyzed using SPSS version 23 software. Data were analyzed using simple descriptive statistics, percentages, and frequencies and summarized using tables and text. Bivariate logistic regression analysis was conducted, and variables (with a p value below 0.25) were included in the multivariate logistic regression model. A p value <0.05 was considered a cutoff point in the final model.

Results

In this study, of the errors detected, 60.3% occurred in the pre-analytical phase and 39.7% in the post-analytical phase. Extra-analytical errors in laboratory services were significantly associated with lack of written procedures for laboratory activities (adjusted odds ratio = 2.79, 95% confidence interval = 1.34-5.86), 1-2 years of work experience (adjusted odds ratio = 5.5, 95% confidence interval = 2.54-9.31), work experience (3-5 years) (adjusted odds ratio = 2.67, 95% confidence interval = 0.94-4.56), and education (diploma) (adjusted odds ratio = 6.30, 95% confidence interval = 2.17-12.26).

Conclusion

This study discovered errors in the pre-analytical (60.3%) and post-analytical (39.7%) phases, and none of the requisition papers had complete data. This frequency may be affected by workload, a lack of written procedures, the number of years of work experience, and the level of education of the staff.

Blood specimen rejection rate in clinical laboratory: A systematic review and meta-analysis

Background

Clinical laboratory errors have a great impact on patient safety and treatment. Although specimen rejections result in longer turnaround times and increased health-care costs, different studies present inconsistent findings. Therefore, the study aimed to determine the pooled prevalence of blood specimen rejection in clinical laboratory.

Methods

Electronic databases including MEDLINE, PubMed, EMBASE, HINARI, Cochrane Library, Google Scholar, and Science Direct were comprehensively searched. Articles were screened and the data extracted independently by authors. Publication bias was checked by funnel-plots and Egger's statistical test. Pooled prevalence was estimated using a random-effects model. The I² statistical test were performed to assess heterogeneity. The possible sources of heterogeneity were analyzed through subgroup and sensitivity analysis.

Results

Total of 26 articles with 16,118,499 blood sample requests were included in the meta-analysis. The pooled prevalence of blood specimen rejection in the clinical laboratory was 1.99% (95% CI: 1.73, 2.25). Subgroup analysis showed that, the highest prevalence of specimen rejection was observed in Asia [2.82% (95%CI: 2.21, 3.43)] and lowest in America [0.55% (95%CI: 0.27, 0.82)]. The leading cause of blood specimen rejection in clinical laboratories were clotted specimen (32.23% (95%CI: 21.02, 43.43)), hemolysis (22.87% (95%CI: 16.72, 29.02)), insufficient volume (22.81% (95%CI: 16.75, 28.87)), and labelling errors (7.31% (95%CI: 6.12, 8.58)).

Conclusion

The pooled prevalence of blood specimen rejection rate is relatively high especially in developing regions. Therefore, proper training for specimen collectors, compliance with good laboratory practices specific to specimen collection, transportation, and preparation is required to reduce the rejection rate.

Detecting Preanalytical Errors Using Quality Indicators in a Hematology Laboratory

BACKGROUND AND OBJECTIVES

Monitoring laboratory performance continuously is crucial for recognizing errors and fostering further improvements in laboratory medicine. This study aimed to review the quality indicators (QIs) and describe the laboratory errors in the preanalytical phase of hematology testing in a clinical laboratory.

METHODS

All samples received in the Hematology Laboratory of the Maternity and Pediatric Hospital in Hail for 3 years were retrospectively reviewed and evaluated for preanalytical issues using a set of QIs. The rate of each QI was compared to the quality specifications cited in the literature.

RESULTS

A total of 95002 blood samples were collected for analysis in the hematology laboratory from January 2017 through December 2019. Overall, 8852 (9.3%) were considered to show preanalytical errors. The most common were "clotted specimen" (3.6%) and "samples not received" (3.5%). Based on the quality specifications, the preanalytical QIs were classified generally as low and medium level of performance. In contrast, the sigma-based performance level indicates acceptable performance on all the key processes. Further analysis of the study showed a decreasing rate of preanalytical errors from 11.6% to 6.5%.

CONCLUSIONS

Preanalytical errors remain a challenge to hematology laboratories. The errors in this case were predominantly related to specimen collection procedures that compromised the specimen quality. Quality indicators are a valuable instrument in the preanalytical phase that allows an opportunity to improve and explore clinical laboratory process performance and progress. Continual monitoring and management of QI data are critical to ensure ongoing satisfactory performance and to enhance the quality in the preanalytical phase.

Critical analysis of laboratory requisition forms received in a cytopathology laboratory of a tertiary care centre: An audit and review of literature

Background:

A laboratory requisition form (LRF) is the main communication link between the laboratories and the clinicians. In a cytopathology laboratory, incomplete forms with inadequate information significantly impact the quality of the results and waste precious time of the lab.

Aims:

The aim of this study was to audit the LRFs for adequacy of demographic and clinical data and to analyze the reasons for the same.

Settings and Design:

A retrospective study was conducted in the cytopathology laboratory of a tertiary care center.

Materials and Methods:

All the original LRFs received for Pap smears and FNACs of 1-month duration were retrieved. The forms were scrutinized for the presence of specific parameters which were classified as patient information, clinician information and clinical information. In addition to the completeness of the form, clarity of the data was also noted.

Statistical Analysis:

The data were entered on excel worksheets and percentage of Pap smear and FNAC forms lacking information of various parameters was calculated.

Results:

A total of 431 LRFs were received in the month of January 2020. These included 274 Pap smear LRFs and 157 FNAC LRFs. Patient information was mentioned in predominantly all the forms, however, clinician and clinical information, which is indispensible for reporting, was missing in a significant proportion of the Pap smear and fine needle aspiration cytology (FNAC) forms.

Conclusions:

Receiving inadequately filled LRFs has been an age-old problem in all medical laboratories. Audit of inadequacy of LRFs helped in assessing the prevailing practices in the hospital and gave an insight into the quality of information available to the cytologists for reporting. Many clinicians withhold information out of ignorance about its importance or due to lack of time to fill up the details on the LRF. Also, filling out a LRF is a task usually delegated to the junior doctor in the OPD and the significance of filling the LRF correctly and comprehensively is often not emphasized upon adequately by the senior clinicians. This audit helped us taking preventive action by giving feedback to the clinicians and emphasizing to them the importance of clinical data on the LRF and in improvising the LRF using a more objective and user-friendly format.

Applicability of commercial clinical chemistry test kits for horse serum

Objective

Validation of a test method is critical for confirming that the test can generate accurate and precise data. Although commercial biochemical test kits exist there are no specific and validated commercial clinical chemistry test kits designed for horses. The aim of this study was to validate commercial clinical chemistry test kits designed for a human serum for use in horses.

Results

Blood samples were collected from 29 apparently healthy adult male horses and pooled serum was prepared. Validation comprises replication and recovery experiments. Total observable error (TE_o), sigma (σ) metrics, and quality goal index (QGI) were used to support the validation studies. Intra- and inter-assay variability was 2.05% and 2.08%, 2.26% and 1.89%, 2.4% and 1.63%, for total cholesterol, urea and total protein, respectively; recovery was 99.46%, 97.32%, and 100.1% for total cholesterol, urea and total protein, respectively. TE_o% for the specified analytes was within the total allowable error (TE_a). All three analytes satisfied the recommended requirement (> 3σ). The QGI for urea, as it had below 6σ was 0.95 indicating imprecision and inaccuracy. The results endorse the suitability of the studied commercial test kits and illustrated the acceptance criteria for horse’s serum.

Chronometric vs. Structural Hypercoagulability

Prolonged tourniquet stasis induced by venepuncture can lead to the release of the plasma of cell lysis products, as well as tissue factor (TF), impairing the quality of coagulation test results. The accidental presence of TF in vitro can trigger the coagulation mechanism, generating a false decrease in prothrombin time (PT). Background and Objectives: Identification of short PT tests below the normal reference value that could suggest a situation of hypercoagulability. The study aimed to compare the results of the shortened PT tests at their first determination with the eventual correction following duplication of the analysis from the same sample. Materials and methods: Identification of the shortened PT tests has been carried out for a period of 4 months, upon 544 coagulation samples referred to the Hematology department of Sf. Spiridon County Clinical Emergency Hospital from Iasi, Romania. Results: Out of the 544 samples of which the results indicated a state of hypercoagulability, by repeating the determination from the same sample, for 200 (36.76%) PT tests (p = 0.001) the value was corrected, falling within the normal reference range. For 344 (63.24%) tests, the results suggested a situation of hypercoagulability. Conclusions: In order to guarantee the highest quality of the laboratory services, a proper interpretation and report of the patients' results must be congruent and harmoniously associated to the actual clinical condition of the patient. Duplication of the PT determination from the same sample would exclude situations of false hypercoagulability and would provide significant improvement for the patient's safety.

Inadequate clinical data on Pap test request form: Where are we headed in the era of precision medicine?

The request form accompanying any sample to a clinical laboratory constitutes an important communication tool between the clinician and the laboratory personnel. Much has been written about the inadequacy of pertinent clinical data on the request slips for hematology and biochemistry tests and its impact on the subsequent test interpretation and error liability. Although the cytology laboratories, including those performing cervical cytology, have to deal with a similar problem of lack of clinical information critical to the proper interpretation of cytomorphologic features, the issue has not been attended to or reported adequately in the literature. This article attempts to explore this topic of inadequate clinical data on Pap test request form from multiple perspectives and suggest possible ways to circumvent this age-old problem. These recommendations may be tailor-made and adopted as per the individual laboratory’s logistics.

A critical review of laboratory performance indicators

Healthcare budgets worldwide are under constant pressure to reduce costs while improving efficiency and quality. This phenomenon is also visible in clinical laboratories. Efficiency gains can be achieved by reducing the error rate and by improving the laboratory's layout and logistics. Performance indicators (PIs) play a crucial role in this process as they allow for performance assessment. This review aids in the process for selecting laboratory PIs-which is not trivial-by providing an overview of frequently used PIs in the literature that can also be used in clinical laboratories. We conducted a systematic review of the laboratory medicine literature on PIs. As the testing process in clinical laboratories can be viewed as a production process, we also reviewed the production processes literature on PIs. The reviewed literature relates to the design, optimization or performance assessment of such processes. The most frequently cited PIs relate to pre-analytical errors, timeliness, resource utilization, cost, and the amount of congestion. Their citation frequency in the literature is used as a proxy for their importance. PIs are discussed in terms of their definition, measurability and impact. The use of suitable PIs is crucial in production processes, including clinical laboratories. By also reviewing the production processes literature, additional relevant PIs for clinical laboratories were found. The PIs in the laboratory medicine literature mostly relate to laboratory errors, while the PIs in the production processes literature relate to the amount of congestion in the process.

Common causes of EID sample rejection in Zimbabwe and how to mitigate them

Early infant diagnosis (EID) of HIV provides an opportunity for early HIV detection and access to appropriate Antiretroviral treatment (ART). Dried Blood Spot (DBS) samples are used for EID of exposed infants, born to HIV-positive mothers. However, DBS rejection rates in Zimbabwe have been exceeding the target of less than 2% per month set by the National Microbiology Reference Laboratory (NMRL), in Harare. The aim of this study was to determine the DBS sample rejection rate, the reasons for rejection and the possible associations between rejection and level of health facility where the samples were collected. This is an analytical cross-sectional study using routine DBS sample data from the NMRL in Harare, Zimbabwe, between January and December 2017.A total of 34 950 DBS samples were received at the NMRL. Of these, 1291(4%) were rejected. Reasons for rejection were insufficient specimen volume (72%), missing request form (11%), missing sample (6%), cross-contamination (6%), mismatch of information (4%) and clotted sample (1%). Samples collected from clinics/rural health facilities were five times more likely to be rejected compared to those from a central hospital. Rejection rates were above the set target of <2%. The reasons for rejection were ‘pre-analytical’ errors including labelling errors, missing or inconsistent data, and insufficient blood collected. Samples collected at primary healthcare facilities had higher rejection rates.

Quality of patient information in emergency department

PURPOSE

Data comprise one of the key resources currently used in organizations. High-quality data are those that are appropriate for use by the customer. The quality of data is a key factor in determining the level of healthcare in hospitals, and its improvement leads to an improved quality of health and treatment and ultimately increases patient satisfaction. The purpose of this paper is to assess the quality of emergency patients' information in a hospital information system.

DESIGN/METHODOLOGY/APPROACH

This cross-sectional study was conducted on 385 randomly selected records of patients admitted to the emergency department of Shahid Beheshti Hospital in Kashan, Iran, in 2016. Data on five dimensions of quality, including accuracy, accessibility, timeliness, completeness and definition, were collected using a researcher-made checklist and were then analyzed in SPSS. The results are presented using descriptive statistics, such as frequency distribution and percentage.

FINDINGS

The overall quality of emergency patients' information in the hospital information system was 86 percent, and the dimensions of quality scored 87.7 percent for accuracy, 86.8 percent for completeness, 83.9 percent for timeliness, 79 percent for definition and 62.1 percent for accessibility.

ORIGINALITY/VALUE

Increasing the quality of patient information at emergency departments can lead to improvements in the timely diagnosis and management of diseases and patient and personnel satisfaction, and reduce hospital costs.

Errors in the Total Testing Process in the Clinical Chemistry Laboratory at the University of Gondar Hospital, Northwest Ethiopia

Background

Laboratory services have been described as the major processes contributing to safe patient care in the modern healthcare sector. However, occurrences of errors in the overall testing processes impair the clinical decision-making process. Such errors are supposed to be high in resource-poor countries, like Ethiopia. The objective of this study was to assess errors in the total testing process in the Clinical Chemistry laboratory of the University of Gondar Hospital, Northwest Ethiopia.

Methods

A cross-sectional study was conducted at the University of Gondar Hospital from February to March 2016. All the required data were collected using established quality indicators. Data were analyzed using SPSS version 20. Frequencies and cross-tabulations were used to summarize descriptive statistics.

Results

A total of 3259 samples and corresponding laboratory request forms were received for analysis. The analysis of the overall distribution of errors revealed that 89.6% were pre-analytical errors, 2.6% were analytical, and 7.7% were post-analytical errors. Of the pre-analytical errors, incomplete request form filling was the most frequent error observed, followed by sample rejection rate (3.8%). Analytical errors related to internal and external quality control exceeding the target range, (14.4%) and (51.4%) respectively, were reported. Excessive turnaround time and unreported critical value cases were the major defects in the post-analytical phase of quality assurance.

Conclusion

The present finding showed relatively high frequency of errors, which alarms the importance of quality indicators to assess errors in the total testing process. The University of Gondar Hospital laboratory should improve the quality of healthcare services based on these findings using laboratory standards.

Effectiveness of Laboratory Practices to Reducing Patient Misidentification Due to Specimen Labeling Errors at the Time of Specimen Collection in Healthcare Settings: LMBP™ Systematic Review

Background

Specimen labeling errors have long plagued the laboratory industry putting patients at risk of transfusion-related death, medication errors, misdiagnosis, and patient mismanagement. Many interventions have been implemented and deemed to be effective in reducing sample error rates. The objective of this review was to identify and evaluate the effectiveness of laboratory practices/ interventions to develop evidence based recommendations for the best laboratory practices to reduce labeling errors.

Content

The standardized LMBP™ A-6 methods were used to conduct this systematic review. Total evidence included 12 studies published during the time periods of 1980 to September 2015. Combined data from seven studies found that the interventions developed as a result of improved communication and collaboration between the laboratory and clinical staff resulted in substantial decrease in specimen labeling errors (Median relative percent change in labeling errors: -75.86; IQI: -84.77, -58.00). Further data from subset of four studies showed a significant decrease in specimen labeling errors after the institution of the standardized specimen labeling protocols (Median relative percent decrease in specimen labeling errors: -72.45; IQI: -83.25, -46.50).

Summary

Based on the evidence included in this review, the interventions that enhance the communication and collaboration between laboratory and healthcare professionals can decrease the specimen identification errors in healthcare settings. However, more research is needed to make the conclusion on the effectiveness of other evaluated practices in this review including training and education of the specimen collection staff, audit and feedback of labeling errors, and implementation of new technology (other than barcoding).

Diagnostic Laboratories in India: Investigating Quality Characteristics, Productivity and Time of Reporting

This is the result of a Survey of diagnostics laboratories in the Asia Pacific (APAC) region with perspectives on India, investigating the three key aspects that are central to the success of a laboratory: quality, cost and speed. This Survey provides a comparison in selected performance indicators in a large number of diagnostic laboratories in a broad range of countries in the APAC region. The Survey provides data on some key performance characteristics such as quality improvement activities, staff productivity and Turnaround Time (TAT). This Survey also demonstrates in India the common issues facing all the laboratories surveyed but also common solutions using a Quality Systems approach which involves Accreditation, customer responsiveness, greater use of IT, automation and Lean principles. Indian laboratories reported less automation and fewer laboratories have Laboratory Information Systems. The productivity by various measures in Indian laboratories was less than in other APAC laboratories. TAT was more commonly monitored in the Indian specimens though there were fewer laboratories compared with the APAC specimens where there were separate TATs for Short Turnaround Time and Routine specimens.

Active tracking of rejected dried blood samples in a large program in Nigeria

AIM: To study the impact of rejection at different levels of health care by retrospectively reviewing records of dried blood spot samples received at the molecular laboratory for human immunodeficiency virus (HIV) early infant diagnosis (EID) between January 2008 and December 2012.

METHODS: The specimen rejection rate, reasons for rejection and the impact of rejection at different levels of health care was examined. The extracted data were cleaned and checked for consistency and then de-duplicated using the unique patient and clinic identifiers. The cleaned data were ciphered and exported to SPSS version 19 (SPSS 2010 IBM Corp, New York, United States) for statistical analyses.

RESULTS: Sample rejection rate of 2.4% (n = 786/32552) and repeat rate of 8.8% (n = 69/786) were established. The mean age of infants presenting for first HIV molecular test among accepted valid samples was 17.83 wk (95%CI: 17.65-18.01) vs 20.30 wk (95%CI: 16.53-24.06) for repeated samples. HIV infection rate was 9.8% vs 15.9% for accepted and repeated samples. Compared to tertiary healthcare clinics, secondary and primary clinics had two-fold and three-fold higher likelihood of sample rejection, respectively (P < 0.05). We observed a significant increase in sample rejection rate with increasing number of EID clinics (r = 0.893, P = 0.041). The major reasons for rejection were improper sample collection (26.3%), improper labeling (16.4%) and insufficient blood (14.8%).

CONCLUSION: Programs should monitor pre-analytical variables and incorporate continuous quality improvement interventions to reduce errors associated with sample rejection and improve patient retention.