Abstract
This study assessed the types and frequency of pre-analytical errors in the hematology laboratory at Debre Tabor Comprehensive Specialized Hospital, recognizing that such errors are most common in the pre-analytical phase, significantly affecting laboratory results, which influence 60–70% of clinical decisions. A cross-sectional study was conducted at the hematology laboratory of Debre Tabor Comprehensive Specialized Hospital from March 1 to April 30, 2025. All hematology test requests and blood samples received during routine hours were included. Pre-analytical errors were assessed using a checklist, and data were verified before entry into Epi-Data version 4.0. Analysis was carried out using STATA version 14, focusing on descriptive statistics, such as frequency and percentage. A total of 2,221 blood samples and their request forms were reviewed, with 50.11% from the inpatient department and 49.89% from the outpatient department. Only 19.45% of the forms contained complete data. While patients’ names and laboratory tests were included, critical information such as diagnosis (20.08%), date of request (8.46%), and patient age (7.16%) was missed. Additionally, 5.4% of samples were of poor quality, mainly due to insufficient volume (1.35%) and unlabeled samples (1.22%). Pre-analytical errors remain a major challenge in laboratory medicine, largely due to human factors in test requests, specimen collection, transport, and processing. In the hematology lab, 80.55% of requisition forms contained errors, while 5.4% of specimens were inadequate for analysis. Reducing these errors requires better ward–laboratory communication, the adoption of laboratory information systems, and continuous staff training with competency assessments.
Keywords: Frequency, Pre-analytical errors, Hematology, Laboratory, Ethiopia
Subject terms: Diseases, Health care, Medical research
Introduction
Background
The clinical laboratory is a rapidly changing field that influences and greatly impacts patient treatment outcomes1. Modern diagnostic practices rely heavily on the accuracy of laboratory test results, which must be both reliable and precise2. However, achieving a zero-error rate in quality result reporting remains a challenge1. Medical laboratories play an important role in providing timely and accurate results, which are required for effective patient management2. Additionally, laboratory results account for approximately 60% to 70% of clinical decisions related to patient admission, medication prescriptions, and discharge3–5. The clinical hematology department is among the most crucial and most indispensable divisions within the medical laboratory sciences, where hematological assays are performed routinely, even in resource-limited laboratories offering only basic diagnostic services6.
The primary objective of quality assurance in the laboratory is to ensure consistent and reliable results. High-quality medical diagnostics are required for safe and effective healthcare delivery. Thus, quality assurance in the hematology laboratory is critical to ensuring the accuracy and reliability of test results2. Total quality management for any laboratory is essential to ensure consistent quality, enhance reliability, and minimize errors1,7. Total quality management is defined as ensuring that every step of the total testing process is completed correctly, allowing for more informed medical decisions and better patient care1. Laboratories strive to implement strong quality control measures, which include operational techniques for ensuring that quality requirements are met. In contrast, quality assurance ensures that these processes consistently adhere to established quality standards7.
Errors may occur at any phase of clinical hematology workflows, encompassing the entire continuum from blood specimen collection through processing to the final dissemination of laboratory results1,4. These stages are categorized into three phases: pre-analytical, analytical, and post-analytical2,4,8,9. The pre-analytical phase includes several critical steps, starting from formulating the medical question to patient preparation or ordering the appropriate test for the patient, specimen collection, handling, transportation, and preparing it for analysis2,3,6. Errors in the pre analytical phase may occur during blood specimen collection and can include misidentification of the patient or sample, use of inappropriate devices or needles, incorrect order of draw, improper use of additive tubes or anticoagulants, and collection of unsuitable samples either in terms of quality (e.g., hemolyzed or contaminated) or quantity (e.g., insufficient volume or incorrect blood-to-anticoagulant ratio). Other potential errors include inadequate mixing of the sample with anticoagulant, ordering tests for the wrong patient, requesting the wrong test, missing samples or test requests, incorrect or missing identification, clotting due to improper handling, use of inappropriate containers, and incorrect labeling4,6,10.
While all three phases are equally important for improving overall total quality management, each phase should be addressed separately to effectively improve laboratory standards. According to studies, the majority of laboratory errors (60% to 70%) occur during the pre-analytical phase7–9,11. These errors can have serious consequences for patient care, such as delays in diagnosis, misdiagnosis, or treatment. Such errors, if not identified before the results are reported, can cause anxiety or inconvenience to the patient. When a specimen cannot be recollected, valuable diagnostic or screening information may be lost. Furthermore, undetected errors can lead to inaccurate or missed diagnoses and unnecessary retesting or treatment, and can ultimately jeopardize patient safety3,7.
The high rate of errors during the pre-analytical phase is primarily due to activities occurring outside the laboratory and the manual handling of specimens3,11. These errors can have a significant impact on the accuracy and reliability of test results, ultimately compromising patient care, harming the institution’s reputation, eroding trust in healthcare services, and raising costs for both the laboratory and the hospital. They also increase the financial burden on the healthcare system11,12. Improving laboratory processes can thus play an important role in improving disease prevention, diagnosis, treatment, clinical monitoring, and cost savings12. Studies from hematology laboratories at different hospitals indicate that the prevalence of pre-analytical errors ranged from 75.5% to 89.6%13,14.
Within the continuum of laboratory workflows, spanning pre-analytical, analytical, and post-analytical phases, the pre-analytical stage constitutes a substantial proportion of the overall process and, consequently, carries a heightened susceptibility to errors. These errors can occur either before or after the sample reaches the laboratory, but always before the analytical phase of testing2. Subsequently-analytical errors are not solely the responsibility of the laboratory and its staff; therefore, it is important for management and all personnel involved in blood collection to be aware of these issues to minimize such errors and support the production of high-quality laboratory results. Despite the fact that, the pre-analytical phase is the most common source of laboratory errors, little is known about the prevalence and types of these errors in Ethiopia, particularly in our study area. Therefore, this study aimed to assess the types and frequency of pre-analytical errors in the hematology laboratory at Debre Tabor Comprehensive Specialized Hospital (DTCSH).
Materials and methods
Study area, design, and period
A cross-sectional study was conducted at DTCSH hematology laboratory from March 1 to April 30, 2025. The hospital is located in Debre Tabor town, about 667 km from Addis Ababa, the capital of Ethiopia, and 103 km from Bahir Dar, the capital of the Amhara Regional State. The hospital provides a variety of medical services for 2.7 million residents in South Gondar Zone, as well as for nearby zones.
The hospital has seven different laboratory sections, one of which is the hematology laboratory. A variety of tests are performed in a hematology laboratory, including complete blood count (CBC), hematocrit (HCT), peripheral blood smear examination (PM), and erythrocyte sedimentation rate (ESR). The labeling of the test tubes was performed manually by writing on the blood collection tubes. For admitted patients, specimens were collected by both trained laboratory personnel and non-laboratory healthcare personnel, such as nurses, midwives, and physicians. In contrast, for the ambulatory patients, all samples are collected solely by qualified laboratory personnel.
Sample size and sampling technique
This study included all hematology test requests and samples submitted to the DTCSH hematology laboratory during the study period at routine working hours. The total sample size included every hematology test request and sample submitted during this timeframe. Using a consecutive sampling approach, the study included all test orders that required blood samples for hematological tests during the study period and the pre-analytical errors were evaluated using different variables (Table 1).
Table 1.
List of variables used for data collection.
| S.No. | List of variables | |
|---|---|---|
| Variables related to the test request | Variables related to sample quality | |
| 1 | Full name | Mis-labeled sample |
| 2 | MRN* | Unlabeled sample |
| 3 | Date of request | Hemolysis sample |
| 4 | Clinical diagnosis | Lipemic sample |
| 5 | Age | Incorrect sample container |
| 6 | Sex | Insufficient sample volume |
| 7 | OPD/Ward name | Clotted sample |
| 8 | Clinician name | Diluted sample |
| 9 | Clinician signature | |
| 10 | Date of specimen collection | |
| 11 | Time of Specimen Collection | |
MRN*- Medical record number.
Data collection and quality assessment
A structured data collection checklist was used to assess pre-analytical errors in DTCSH’s hematology laboratory. The tool was developed using hematology laboratory quality indicators and a review of existing literature from previous studies that addressed similar topics. Three trained data collectors (laboratory personnel) participated in the data collection after receiving extensive training on the study’s objectives and data collection tool. Throughout the study, data were collected in the hematology laboratory during regular working hours. The principal investigator closely monitored the process to ensure that the data were complete and consistent.
Inclusion and exclusion criteria
All test requests and blood samples collected and submitted for routine hematological testing were included in the study. However, requests and specimens collected for non-routine hematological analyses, such as pleural, synovial, cerebrospinal, and peritoneal fluids, were excluded.
Data management and quality control
Before data collection, a pretest was performed to assess the feasibility, clarity, acceptability, and consistency of the structured data collection checklist. Before beginning the actual data collection process, all necessary modifications were made. A senior laboratory technologist and the principal investigator both monitored data collection procedures throughout the study to ensure compliance with established quality indicators. The principal investigator closely monitored all study activities. Completed checklists were only collected after rigorous manual verification for consistency and completeness.
Data analysis and interpretation
Following manual quality checks for data completeness, all verified data were entered into Epi-Data version 4.0 before being transferred to STATA version 14 for analysis. Descriptive statistical methods, such as frequency and percentage, were used to determine the frequency and types of preanalytical errors in the hematology laboratory.
Ethics and consent
Ethical approval for this study was obtained from the Research Committee of the Medical Laboratory Science Education and Service Directorate, College of Health Sciences, Debre Tabor University, with reference number: CHS/lab/350/2025. Formal authorization was granted by DTCSH before study commencement. To ensure confidentiality, all study data were anonymized using unique identification codes, and no personal identifiers were recorded. While maintaining patient privacy, any detected errors were properly documented and communicated to relevant personnel solely for quality improvement initiatives and improved patient care management. Data was collected by observing the request forms and quality of samples; as a result, the Research Committee of the Medical Laboratory Science Education and Service Directorate, College of Health Sciences, Debre Tabor University has waived informed consent for the study. Therefore, informed consent was not obtained for this study. All methods were performed in accordance with the Declaration of Helsinki.
Result
During the study period, a total of 2221 blood samples and their corresponding request papers were inspected. Of these samples, 50.11% (1113/2,221) were sent from the inpatient department (IPD), while 49.89% (1108/2221) came from the outpatient department (OPD).
In reviewing the parameters on the test request form, it was found that out of 2221 requisition papers, only 19.45% (432/2221) contained complete data, which means they included all necessary information. Out of all the required information, the patient’s name and the laboratory tests ordered were present in all 2221 (100%) of the laboratory test request forms. The majority of the laboratory test request forms missed critical patient information: the diagnosis was not provided on 20.08% (446/2221) of the request papers, the date of request was missing in 8.46% (188/2221), and the patients’ age was not provided in 7.16% (159/2221) of the data. Additionally, the request paper lacked significant information from the requesting clinicians. The requesting clinician’s name was missing in 9.86% (219/2221) of requesting papers, the signature was absent in 11.71% (260/2221), and the exact OPD/ward name was incomplete in 14.81% (329/2221) of the requested papers. Furthermore, important sample quality indicators were not filled out, like the time of collection was missed in 43.90% (975/2221) of forms, and the sample collection date was missed in 54.84% (1218/2221) of request papers (Table 2).
Table 2.
Frequency of the various missed data observed on submitted test request papers to the hematology laboratory at DTCSH, North central Ethiopia, 2025 (n = 2221).
| Type of missed variables | Address/Location | Total (n = 2221) | ||||
|---|---|---|---|---|---|---|
| IPD* Missed (n = 1113) | OPD* missed (n = 1108) | |||||
| Yes = n (%) | No = n (%) | Yes = n (%) | No = n (%) | Yes = n (%) | No = n (%) | |
| Full name | 0/1113 (0%) | 1113/2221 (50.11%) | 0/1108 (0%) | 1108/2221 (49.89%) | 0/2221 (0%) | 2221/2221 (100%) |
| MRN* | 35/1113 (3.14%) | 1078/1113 (96.86%) | 23/1108 (2.08%) | 1085/1108 (97.92%) | 58/2221 (2.61%) | 2163/2221 (97.39%) |
| Date of request | 64/1113 (5.75%) | 1049/1113 (94.25%) | 124/1108 (11.19%) | 984/1108 (88.81%) | 188/2221 (8.46%) | 2033/2221 (91.54%) |
| Clinical diagnosis | 253/1113 (22.73%) | 860/1113 (77.27%) | 193/1108 (17.42%) | 915/1108 (82.58%) | 446/2221 (20.08%) | 1775/2221 (79.92%) |
| Age | 113/1113 (10.15%) | 1000/1113 (89.85%) | 46/1108 (4.15%) | 1062/1108 (95.85%) | 159/2221 (7.16%) | 2062/2221 (92.84%) |
| Sex | 66/1113 (5.93%) | 1047/1113 (94.07%) | 64/1108 (5.78%) | 1044/1108 (94.22%) | 130/2221 (5.85%) | 2091/2221 (94.15%) |
| OPD/ward name | 5/1113 (0.45%) | 1108/1113 (99.55%) | 324/1108 (29.24%) | 784/1108 (70.76%) | 329/2221 (14.81%) | 1892/2221 (85.19%) |
| Clinician name | 103/1113 (9.25%) | 1010/1113 (90.75%) | 116/1108 (10.47%) | 992/1108 (89.53%) | 219/2221 (9.86%) | 2002/2221 (90.14%) |
| Clinician signature | 98/1113 (8.81%) | 1015/1113 (91.19%) | 162/1108 (14.62%) | 946/1108 (85.38%) | 260/2221 (11.71%) | 1961/2221 (88.29%) |
| Date of specimen collection | 630/1113 (56.60) | 483/1113 (43.40%) | 588/1108 (53.07%) | 520/1108 (46.93%) | 1218/2221 (54.84%) | 1003/2221 (45.16%) |
| Time of specimen collection | 510/1113 (45.82%) | 603/1113 (54.18%) | 465/1108 (41.97%) | 643/1108 (58.03%) | 975/2221 (43.90%) | 1246/2221 (56.10%) |
MRN*- Medical record number, OPD- Outpatient department, IPD- Inpatient department.
Sample quality indicators
The other observation made during the study period was an assessment of sample quality. Of the total number of 2221 samples submitted to the hematology laboratory for hematological tests, 5.40% (120/2221) were poor samples. The most common reason for poor samples was insufficient sample volume,1.35% (30/2221), followed by an unlabeled sample, 1.22% (27/2221) (Table 3).
Table 3.
Type and frequency of reasons for poor sample quality in the hematology laboratory at DTCSH, North central Ethiopia, 2025 (n = 2221).
| Causes for poor sample quality | Address/Location | Total(n = 2221) | ||||
|---|---|---|---|---|---|---|
| IPD(n = 1113) | OPD (n = 1108) | |||||
| Yes = n (%) | No = n (%) | Yes = n (%) | No = n (%) | Yes = n (%) | No = n (%) | |
| Mis-labeled sample | 16/1113 (1.44%) | 1097/1113 (98.56%) | 4/1108 (0.36%) | 1104/1108 (99.64%) | 20/2221 (0.90%) | 2201/2221 (99.10%) |
| Unlabeled sample | 22/1113 (1.98%) | 1091/1113 (98.02%) | 5/1108 (0.45%) | 1103/1108 (99.55%) | 27/2221 (1.22%) | 2194/2221 (98.78%) |
| Hemolysis sample | 0/1113 (0%) | 1113/1113 (100%) | 5/1108 (0.45%) | 1103/1108 (99.55%) | 5/2221 (0.23%) | 2216/2221 (99.77%) |
| Lipemic sample | 0/1113 (0%) | 0/1113 (0%) | 0/1108 (0%) | 0/1108 (0%) | 0/2221 (0%) | 2221/2221 (100%) |
| Incorrect sample container | 6/1113 (0.54%) | 1107/1113 (99.46%) | 0/1108 (0%) | 0/1108 (0%) | 6/2221 (0.27%) | 2215/2221 (99.73%) |
| Insufficient sample volume | 18/1113 (1.62%) | 1095/1113 (98.38%) | 12/1108 (1.08%) | 1096/1108 (98.92%) | 30/2221 (1.35%) | 2191/2221 (98.65%) |
| Clotted sample | 11/1113 (0.99%) | 1102/1113 (99.01%) | 9/1108 (0.81%) | 1099/1108 (99.19%) | 20/2221 (0.90%) | 2201/2221 (99.10%) |
| Diluted sample | 12/1113 (1.08%) | 1101/1113 (98.92%) | 0/1108 (0%) | 1108/1108 (100%) | 12/2221 (0.54%) | 2209/2221 (99.46%) |
Discussion
A laboratory error is any mistake that occurs during the testing process, from the initial order to the final report, that compromises the quality of laboratory services. Our study revealed that the only information consistently documented on all request forms was the patient’s name and the type of test requested. The laboratory test request forms were often incomplete, lacking one or more required details. This study showed that 80.55% (1,789/2,221) of hematological test request forms contained pre-analytical errors due to missing critical information. This finding is consistent with studies conducted at St. Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia,75.5%13, and University of Gondar Comprehensive Specialized Hospital, Ethiopia,74.8%15. However, the rate observed in this study is higher than that reported in another study from Addis Ababa, Ethiopia,60.3%16 and lower than the rate from the University of Gondar Hospital, Northwest Ethiopia, 89.6%14, and a study done in Central Oromia, Ethiopia,32.9%17. These differences might be attributed to variations in sample size. Our results indicate that pre-analytical errors are common in the hematology laboratory and can be reduced through regular analysis of contributing variables. Addressing these errors may be achieved by providing ongoing education to those health professionals who are involved in requesting laboratory tests and phlebotomy procedures, such as laboratory staff, physicians, nurses, and midwives1.
In the current study, 9.86% and 17.71% laboratory request forms were not filled with the requesting clinician’s name and signature, respectively. This finding is lower than the study done at the hematology laboratory at St. Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia, where 85% laboratory request forms were not filled with the requesting physician’s name, and 24.5% of request forms were not filled with the requesting physician’s signature13, and a study done at Central Oromia, Ethiopia showed, 22.5% and 30% of the request forms were not filled with the requesting physician’s name and signature, respectively16, study done at Hawassa University Comprehensive Specialized Hospital in Sidama Zone, Southern Ethiopia, showed that 76.08% and 72.8% request forms were not filled with the requesting physician’s name and signature, respectively18, study done at a Ghanaian tertiary hospital showed 44.6% of laboratory test request forms were not filled with the requesting clinician’s name, and 24.3% laboratory request forms were also not filled with the requesting clinician’s signature19. This difference might be due to workload, the number of staff assigned to the work, and the application of the hospital to the standards.
In our study, 5.40% (120/2221) of samples were poor in quality due to various reasons. This finding was higher than a study conducted in Ethiopia, 3.8%14, India, 0.7%20, Pakistan,1.48%21, India, 0.51%7, Saudi Arabia, 0.39%3 but lower than other studies conducted in Ethiopia, 11.4%17, 74.8%15, and 75.5%13. This discrepancy might be due to the periodic influx of students, increased patient flow, higher workload, lack of periodic refreshment training for staff who collect the specimens, and lack of cooperation and communication among staff members.
In the current study, the most common cause of poor sample quality was insufficient sample volume,1.35% (27/2221), which is comparable with a study done in Nepal, 2.28%1, and India,1.11%9. However, the finding of this study was lower than the findings in a study conducted at St. Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia,48.9%13, University of Gondar Hospital, Ethiopia, 33.3%14, Central Oromia, Ethiopia,4.5%17, India,52.3%2, University of Gondar Comprehensive Specialized Hospital, 23.46%22 and 10.1%23, but the finding is lower than the study done in India, 0.06%24 and in Addis Ababa, Ethiopia, 0.3%16. The variation of the result might be due to variation in the number of samples assessed. In addition, the reasons for insufficient sample quantity could be related to challenging and uncooperative veins in children, obesity, and chronic disease.
The second most common cause for poor sample quality was an unlabeled sample,1.22% (27/2221). This finding is comparable to a study done in India, 0.96%9. The result of the current study was lower than the study done in India,10.01%25, at Hawassa University Comprehensive Specialized Hospital, Southern Ethiopia,2.3%18. However, the result of our study was higher than a study done at the University of Gondar Comprehensive Specialized Hospital, Ethiopia, 0.66%22, and Central Oromia, Ethiopia,0.5%17. This variation is attributed to load, non-adherence to the standard operating procedure, and negligence of the staff who collect the specimen.
The third most common cause for poor sample quality identified in this study was a mislabeled sample,0.90% (20/2221). This result is comparable with the findings of a study done at the University of Gondar Hospital, Ethiopia,1.1%14, at Central Oromia, Ethiopia, 1.3%17, and Indian, 1.2%2. On the other hand, the finding of the current study result was lower than the study done in India, 2.42%9, at St. Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia,5.7%13, and in Saudi Arabia, 2.4%23, 3.27%3, but the finding of our study was higher than the result of the study done in India, 0.005%24. This could be due to excessive patient load (disproportionate number of patients to phlebotomists), the absence of a functional laboratory information system, and the pneumatic tube complicated proper labeling and delivery of samples with corresponding request forms. Loss of attention and poor communication between staff might further aggravate the problem.
The other cause for poor sample quality identified in our study was a clotted sample, which accounted for 0.90% (20/2221). This finding is comparable with the studies done in India, 0.28%24, and 0.12%4.
However, this finding is smaller than the study done at St. Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia, 2.3%13, in Saudi Arabia, 33.6%23, 20.09%3, in Pakistan, 14.9%21, in Nepal,12%1,33.6%23. The causes of sample clotting after collection may include improper mixing with anticoagulant, delays in transferring the sample from a syringe to a tube, or severe trauma during the draw that activates coagulation. To prevent sample clotting, it is essential to transfer the blood to a test tube immediately and mix it thoroughly. This can be achieved by performing 3 to 6 complete end-over-end inversions of the tube to ensure even distribution of the anticoagulant6.
Diluted samples have been identified as another cause of poor sample quality in our study, accounting for 0.54% (12/2221). Additionally, hemolysis and the use of incorrect sample containers also contribute to poor sample quality, representing 0.23% (5/2221) and 0.27% (6/2221), respectively. The findings of our study are consistent with a study conducted in South India, which reported rates of 0.06%, 0.04%, and 0.03% for diluted, incorrect container, and hemolyzed samples, respectively4. Another study in India recorded a similar rate of 0.02% for hemolysis24. In contrast, a different study in India revealed higher rates: 1.7% for diluted samples, 6.4% for hemolyzed samples, and 14.1% for samples in incorrect containers2. Additionally, a study in Saudi Arabia found that 33.6% of clotted specimens were collected23, while a study in Nepal reported a 20% rate of hemolysis1.
These findings suggest that the laboratory in our current study lacked a well-integrated system with clinicians. The likely reasons for the collection of diluted samples, the use of incorrect containers, and instances of hemolysis may include high workloads, the use of incorrect needles for specimen collection, and a general lack of awareness regarding proper specimen collection procedures among sample collectors. To enhance the quality of specimens in this study setting, it would be beneficial to consider phlebotomy as a separate profession, as is the practice in many developed countries.
Conclusion
Preanalytical errors represent a persistent and critical challenge in laboratory medicine, primarily attributable to the high degree of human involvement across multiple stages of the testing process, including requisition form completion, specimen collection, transport, receipt, and preparation. In the hematology laboratory, the prevalence of preanalytical errors related to laboratory test requisition form completion was high, and few of the submitted specimens were of suboptimal quality, rendering them unsuitable for accurate processing and reliable result reporting. Effective reduction of such errors requires a comprehensive and systematic approach encompassing enhanced communication and coordination between clinical wards and the laboratory, integration of advanced laboratory information systems, and the routine implementation of staff competency assessments. In addition, structured training programs and application of quality assurance techniques, such as Failure Mode and Effects Analysis (FMEA), ongoing professional development, and continuing medical education for both laboratory and other health professionals who are involved in laboratory test requestion and phlebotomy procedures are critical. Finally, fostering greater awareness of preanalytical factors that influence laboratory outcomes remains essential for ensuring the accuracy, reliability, and overall quality of hematology services.
Limitations of the study
Due to budgetary constraints, the present study was restricted to the preanalytical phase of the total testing process and did not encompass all hospital laboratories. In addition, the short study duration and the checklist-based observational design may affect the depth of the study.
Acknowledgements
The authors would like to acknowledge the Debre Tabor Comprehensive Specialized Hospital laboratory staff.
Abbreviations
- DTCSH
Debre Tabor Comprehensive Specialized Hospital
- IPD
Inpatient Department
- OPD
Outpatient Department
Author contributions
AB: the conception and design of the work, the acquisition, analysis, and interpretation of data, the creation of new software used in the work, having drafted the work, and substantively revised the manuscript. BS: analysis, interpretation of data, the creation of new software used in the work, and drafting the work. AA: analysis, interpretation of data, the creation of new software used in the work, and drafting the work, review and editing. GA: analysis, interpretation of data, the creation of new software used in the work, and drafting the work. NT: Conception, analysis, interpretation of data, the creation of new software used in the work, and drafting the work SD: interpretation of data, the creation of new software used in the work, drafting the work, and substantially revising the manuscript BL: analysis, interpretation of data, the creation of new software used in the work, and drafting the work. TS: analysis, interpretation of data, the creation of new software used in the work, and drafting the work. DT: interpretation of data, having drafted the work, and substantively revised the manuscript. SW: The creation of new software used in the work, having drafted the work, and substantively revised the manuscript. ME: analysis, interpretation of data, the creation of new software used in the work, and drafting the work. BG: analysis, interpretation of data, the creation of new software used in the work, and drafting the work. AyAs: The conception and design of the work, the creation of new software used in the work, drafting the work, and substantively revising the manuscript. AF: The conception and design of the work, the creation of new software used in the work, drafting the work, and substantively revising the manuscript. MSF: The conception and design of the work, the creation of new software used in the work, drafting the work, and substantively revising the manuscript. TK: The conception and design of the work, the creation of new software used in the work, drafting the work, and substantively revising the manuscript. TT: having drafted the work and substantively revised the manuscript. BM: The creation of new software used in the work, having drafted the work, and substantively revised the manuscript. BMalk: The conception and design of the work, the creation of new software used in the work, drafting the work, and substantively revising the manuscript.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The data associated with our study are not deposited into a publicly available repository. The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Ethical approval
Ethical approval for this study was obtained from the Research Committee of the Medical Laboratory Science Education and Service Directorate, College of Health Sciences, Debre Tabor University. Formal authorization was granted by DTCSH before study commencement. To ensure confidentiality, all study data were anonymized using unique identification codes, and no personal identifiers were recorded. While maintaining patient privacy, any detected errors were properly documented and communicated to relevant personnel solely for quality improvement initiatives and improved patient care management. Data was collected by observing the request forms and quality of samples; as a result, the Research Committee of the Medical Laboratory Science Education and Service Directorate, College of Health Sciences, Debre Tabor University has waived informed consent for the study. Therefore, informed consent was not obtained for this study.
Consent for publication
Not applicable.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data associated with our study are not deposited into a publicly available repository. The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.