[version 1; peer review: 1 approved with reservations, 1 not approved]
No competing interests were disclosed.
Effective clinical laboratories are the need of the hour, they play a significant role in healthcare, providing tests for diagnosis, treatment monitoring. Having skilled and adequate staffing is crucial for clinical laboratories to operate efficiently, manage their workload effectively, and deliver test results in a timely manner. Despite being critical for healthcare delivery, ensuring adequate staffing levels in clinical laboratories remains a complex challenge for healthcare providers worldwide due to resource constraints and workforce shortages.
This study evaluates the effectiveness of clinical laboratory staff utilization by considering workload, work pressure, and staffing requirements. We utilized the Workload Indicators of Staffing Need (WISN) method to assess the staffing in the clinical laboratory of a tertiary care hospital. Annual hospital statistics were collected for two years from June 2021 to May 2023 to calculate the average number of days (234) worked annually, percentage of workload, distribution of activity time measurement units, and the ratio of needed, surplus, and existing staff. The findings aim to provide valuable insights for optimizing staffing levels and ensuring an efficient laboratory environment.
The analysis found a significant workforce gap in the laboratory between the current staff numbers and the calculated staffing needs. The WISN ratios ranged from 0.2 to 0.7, indicating the existing 33 staff members face a high workload burden. The calculated ideal staffing level was 46.33 personnel. These results demonstrate staff shortage and excess workload pressure on the current laboratory employees.
The results highlight the importance of optimizing staffing levels in clinical laboratories to ensure quality service delivery. The WISN methodology can be a useful tool in healthcare facilities for making evidence -based decisions for staff allocation, maximizing the utilization of employee skill sets, and establishing standard staffing benchmarks tailored to the needs of clinical laboratories.
Efficient healthcare relies heavily on the valuable contributions of clinical laboratory personnel. These skilled professionals help diagnose illnesses and effectively monitor treatments. Their expertise is genuinely indispensable for making informed decisions and delivering top-notch care. The effective use of clinical laboratory services improves doctors’ capacity to use the least resources possible while reducing total healthcare costs by helping them make evidence-based diagnostic and treatment decisions for their patients. Clinical laboratory services are the least intrusive and economical source of objective health information for preventing and diagnosing diseases, enhancing patient outcomes, guaranteeing patient safety, and carrying out crucial public health monitoring tasks (
The World Health Organization (WHO) developed Workload Indicators of Staffing Need (WISN), a human resource planning and management tool that gives health managers a mechanism to assess and determine the ideal staffing levels in healthcare facilities (
Health planners and policymakers must ensure that workers with the necessary skills are available appropriately. Policymakers should contribute critical insights to developing guidelines for healthcare facilities’ staffing standards in the context of resource constraints (
Diagnosis and treatment these days are primarily driven by evidence-based; the hematology and Clinical Pathology Laboratory is one of the most supporting medical units, and this functional unit plays a significant role in supporting hospital operations by testing the cause of the disease. Scarce research is available in the staffing and utilization of clinical laboratory staff. This exploration aids in assessing the effective use of technical staff in the context of workload, work pressure, and staff requirements in the laboratory.
The study was initiated after obtaining approval from the Institutional Ethics Committee, Kasturba Medical College (KMC) and Kasturba Hospital (KH) (IEC2-345/2023) (approved on 22 July 2023), Institutional Research Committee (IRC- MCHP-Mpl/IRC/PG/2023/150, 18 April 2023). The Workload Indicator Staffing Need (WISN) software is accessible upon registering with the WHO, making it an efficient and valuable tool for our research endeavors.
As human participants not included, no consent required for the performed study. However, data was collected with permission from laboratory in charge and head of the hospital while obtaining IRC and IEC approval.
The study was conducted in the Clinical Laboratory of a tertiary care hospital., operating 24/7 with a staff of 33 technicians. The data on workload, distribution of activity time, staff Available Work Time (AWT), time spent by staff for the supportive activities (such as training, meetings, sample pre examination procedures and equipment maintenance) were collected over a period of two years, from June 2021 to May 2023.
This study may help guide decisions during the planning stage of enhancing the capacity and calibre of laboratories. Both small and large workloads can negatively impact employees’ skills and workplace attitudes. Hence, laboratories must have competent staff to handle the workload. In emergency conditions, the number of laboratory tests that must be performed daily grows exponentially, and the time needed to complete core operations doubles. As a result, more workers are needed. Additionally, it issues a warning that workforce management and planning must be predicated on several circumstances of the demand for clinical laboratory services and capabilities, necessitating the professional engagement of HRM planners.
Data was obtained from the laboratory registers which included details regarding work hours, daily tasks performed by staff, and the typical workload encountered and other activities to achieve the task Other Quantitative data were procured from the information technology department which encompassed service of the staff, leave taken, number of holidays per year, and the total number of hematology and clinical pathology tests conducted.
Annual hospital statistics were collected, and the WISN methods were applied to the clinical laboratory, which has several analytical sections, autoanalyzers and partial manual tests. All data collected from the lab records and annual service statistics were analyzed using WISN software (
The study followed the WHO/WISN manual instructions to estimate the technical staff requirement, including the following steps.
The staff 33 technical staff, were categorized as a lab technologist and in charge, lab technologist, senior technician, lab technician grade I, and lab technician grade II.
This step calculates the staff’s actual working days for one year. Then, the days the staff may not work in a year are determined for various reasons.
AWT is the total available working time A is the number of possible working days in a year B is the number of days off for public holidays in a year C is the number of days off for annual leave in a year D is the number of days off due to casual leave in a year E is the number of days off due to other leave in a year.
The working days are then converted to hours by multiplying AWT in working days by daily working hours.
The workload components are the activities of the technical staff’s daily schedule. Then, we set a standard workload based on the information obtained by the available working time and established a standard workload for each workload component in the laboratory.
Standard workload = “AWT in a year divided by unit time” or “AWT in a year multiplied by a rate of working”. Activities per hour are a measure of the rate of working (
| Activity name | No Per Year | Service Standard
|
Standard Workload | Calculated staff Requirement |
|---|---|---|---|---|
| Complete Blood Count+ Retic +ESR | 1404293 | 1 | 112320 | 12.5 |
| QBC Malaria &Microfilaria, Malaria Smear | 3787 | 30 | 3744 | 1.01 |
| Special test in Blood and coagulation section
|
8361 | 15-180 | 624-7488 | 2.07 |
| Coagulation tests | 82041 | 8 | 14040 | 5.8 |
| Urine Analysis Complete | 51091 | 5 | 22464 | 2.18 |
| Fluid Analysis + CSF | 3738 | 30 | 3,744.00 | 0.99 |
| Staining of smears (bone marrow) | 4800 | 30 | 3744 | 1.28 |
| Phlebotomy | 230319 | 10 | 11232 | 20.5 |
The unit of reporting service standard is minutes/test except for phlebotomy (minutes/patients).
Low number of tests with high service standards.
Types of allowance standards: category allowance standards (CAS) and individual allowance standards (IAS), and an allowance factor is calculated separately for support and additional activities.
In this study, lab technician grade -II spends 126 minutes (equal to 2.1 hours) on activities supporting service activities (e.g., reagent preparation, QC analysis, startup and shutdown of analyzers, smear preparation, and receiving and segregation of samples, etc.).
The CAS % can be calculated as 2.1 hours multiplied by 234 (annual working days)/1872 (AWT) multiplied by 100
The IAF calculation divides the annual total IAS by the AWT.
Here in this study lab. Technician grade II has an annual IAS of 286 hours, and AWT is 1872 hours, then
The technical staff has an hour or 60-minute lunch break, a 15-minute tea break, and a standard allowance per week of 1.15 × 6 = 6.9 hours and allowance per year = 6.9 × 52 weeks = 358.8 hours/year. Standard allowance = average per allowance factor/Available working time = 358.8/1872 = 0.19 hrs/year
The technical staff requirement is obtained by dividing the annual workload for each workload component by its standard workload and by adding all workload components together = the total staff required in the clinical laboratory (Workload Indicators of Staffing Need - User’s Manual, n.d. 2023).
For example: To perform the Complete Blood Count, the required staff = Total number of tests (annual workload)/standard workload = 1404293/112320 = 12.5.
The study included all 33 technical staff working in the clinical laboratory. The staff ages ranged from 24 to 56 years, with experience levels varying from 5 to 36 years. Each technician works a standard 48-hour week (8 hours per day with a 1-hour 15-minute break), resulting in an annual available working time (AWT) of 1872 hours after accounting for non-working days.
Laboratory technicians perform various tasks across the pre-analytical, analytical, and post-analytical phases. These include sample reception and registration, sample processing, running assays on analyzers, data entry, and report verification. Additionally, they assist with patient procedures, reagent preparation, quality control, and other support activities. The specific workload components for each technician are determined by their daily schedule and the types of tests performed.
In two years (June 2021 to May 2023), 4593430 patients were registered, and approximately 3692642 tests were performed, including phlebotomy. On average, per year, 1558111 tests and 230319 phlebotomies were done. The Workload Indicators of Staffing Need (WISN) method was used to analyze staffing requirements based on available working time and activity standards. The WISN data provided workload standards in minutes per activity and served as the basis for calculating the standard annual workload for each component. We then followed the steps outlined in the WISN manual to assess workload pressure and staff needs. (Workload Indicators of Staffing Need - User’s Manual, n.d.2023) The difference and ratio generated by the WISN software will be further analyzed in the discussion section to understand staffing adequacy (
Supporting activities in the laboratory consumed 15.63% to 31.73% of annual working time. The category allowance factor (CAF) indicated that 1.19% to 1.46% of technical staff time should be dedicated to both primary and supporting activities. Individual allowance factors (IAF) ranged from 0.03 to 0.34, accounting for additional tasks performed by specific staff members.
The WISN analysis assessed staffing adequacy through two key metrics:
Difference: The difference between the current and required number of staff indicates understaffing or overstaffing. WISN Ratio: This ratio reflects workload pressure, with a value of 1 indicating balanced staffing, >1 suggesting overstaffing, and <1 indicating understaffing.
The available working time (AWT) for lab technicians was determined by subtracting the total number of non-working days (78 days excluding Sundays) from the total possible working days (312 days). There are 52 weeks a year, with 39 working weeks and 13 non-working weeks. Each lab technician works 48 hours per week, which translates to 8 hours daily, including a 1-hour fifteen-minute break. Therefore, the total annual working hours amount to 1872.
In the laboratory, significant levels of work overload and workload pressure were experienced by the section where the WISN ratio of 0.2 to 0.7 and the WISN ratio from 1 to 1.6 revealed that sections had no workload pressure. The WISN tool states that the workload increases with a lower ratio (
Upon completion of a workload study, the technical staff ratio assesses the existing number of technical staff against the actual requirement for technical personnel. The data analysis revealed a disparity between the number of technical workers needed and those currently available in the clinical laboratory.
Along with WISN, the Shapiro-Wilk test was utilized to assess the normality of the data. A p-value below 0.01 indicated that the data originated from a non-normal distribution. Consequently, the Kruskal-Wallis test was conducted as a non-parametric alternative to ANOVA. A p-value less than 0.01 indicated a notable disparity in the demand for Technical Staff and workload across each section of the clinical laboratory.
The clinical laboratory’s technical staff’s efficiency is critical for ensuring high-quality patient care and efficient diagnostics (
A study highlights the critical role of human resource planning and management in the healthcare sector, particularly within clinical laboratory environments. Research involved a large group of health laboratory workers, medical laboratory technicians, and medical biochemists, illustrating the diverse personnel involved in such services (
In a District Hospital of Madhya Pradesh, the laboratory technicians were constantly under pressure to match the existing workload due to shortage of 10 manpower. Hence, it indicates an urgent need for improved human resource management in the healthcare facility (
The field of medical laboratory science is overwhelmingly female, with women comprising 88.99% of medical laboratory technicians and 81.8% of medical biochemists, with an average age of 50 (
A scarcity of technical staff, an ageing workforce, and ticking demographic time bombs of pension-related issues all threaten to compound the situation. The impact of these challenges is evident in delayed diagnoses and errors, directly impacting patient care quality (
The midwives at Asrade Zewude Memorial Hospital worked five working days with an actual working time of 1030 hours annually. 58.4% of hours were spent on health service activity, additional activities, 36.6% hours, and supporting activities, 5% hours (
In a study in the RSU Anutapura Palu laboratory, reveals that the productive time used for activities was 88.6%, and the working hours was 114,240 minutes/year. The workload standard was 5817.32 annually, and the allowance was 0.4% annually. Employee in laboratory unit distribution has been successful; tasks and activities divided into productive, unproductive, and personal pursuits. Each employee has a 0.4 annual leeway factor, almost 40%. The laboratory has a deficit of 8 technicians. Qualification and competence are important for manpower planning. Understanding the optimal staffing levels can help hospital management make informed decisions regarding recruitment, training, and retention of staff members, ensuring that the workforce is adequate and competent to handle the workload (
The WISN difference shows a 20% and 14% Full-Time Equivalent (FTE) medical biochemist and medical laboratory technician shortfall, respectively, as well as a highly inconsistent workload pressure/WISN ratio of 0.4 to 1.0 (
There was a significant difference between the number of employees currently employed and the number calculated by WISN. 25% of the employees were accessible to meet their needs, and 75% were shortfalls (
A moderate to high workload is shown on the WISN ratio, which ranges from 0.86 to 0.50 for laboratory workers (
Despite encountering a heavier workload, the present study indicates that the reporting turnaround time was commendable. This was achieved through the redistribution of tasks among sections and the delegation of activities to graduates and students under supervision. This approach not only benefited patient care but also provided valuable training opportunities for future laboratory professionals. Considering the significant workload, this strategy played a pivotal role in reducing the turnaround time. It provided students with a unique opportunity to enhance their skills and become proficient laboratory technicians equipped with the education and experience necessary for success in their field of work.
The WISN approach serves as a valuable tool for HRM planners, assisting in the determination of whether there is an excess or deficit of human resources due to work-related stress. The establishment of suitable HRM policies requires consideration of current labour market analysis based on reliable data (
Using non-parametric Kruskal–Wallis one-way ANOVA (analysis of variance), facility-specific raw (unrounded) WISN values for all cadres were evaluated for a cross-state differences to investigate data heterogeneity. Since the count data produced by the WISN rounding technique caused problems with saturation raw numbers, a better fit was used in the ANOVA model. A non-parametric test was selected because of the data’s apparent skewness (
The dedicated technological tool provided by the WHO for the WISN Software process facilitated the measurement of workload and the evaluation of technical staff utilization and needs. Based on the WISN, this study’s computation reveals a significant discrepancy between the actual workforce 33 and the WISN-calculated demand 46.33. The WISN results proved that staffing minimum and maximum standards and technical staff are optimally utilized in clinical laboratories. The WISN ratio is < 1, and the required number of laboratory staff is 13.33, indicating the workload pressure. This study focuses on staffing needs in modern clinical laboratories, which are increasingly automated compared to traditional facilities. This unique approach can inform policies for developing workforce requirements in future laboratories and healthcare settings.
The calculations for staffing requirements are based on data from previous financial years and focused on a single clinical laboratory, limiting the generalizability of the findings. However, adjustments may be required to account for the current year’s workload and staffing needs. The WISN tool is helpful, but it has limitations. It may not fully consider facilities that operate all day and night or cases where different staff groups do similar tasks. Also, incorrect workload data could make the system suggest too few staff members. Despite its limitations, the WISN tool remains a valuable resource for healthcare staffing decisions. It works best in combination with other approaches.
The study was initiated after obtaining approval from the Institutional Ethics Committee, Kasturba Medical College (KMC) and Kasturba Hospital (KH) (IEC2-345/2023) (approved on 22 July 2023). The Workload Indicator Staffing Need (WISN) software is accessible upon registering with the WHO, making it an efficient and valuable tool for our research endeavors. As human participants were not included, no consent was required for the performed study. However, data was collected with permission from laboratory in charge and head of the hospital while obtaining IEC approval.
Figshare: Unveiling efficiency: A research inquiry into technical staff utilization in South Indian clinical laboratory using workload indicators staffing need (WSN) analysis,
WISN_Data file.
Data are available under the terms of the
The authors thank the Hematology and Clinical Pathology laboratory, Kasturba Hospital, Manipal, for their cooperation in this study. The authors acknowledge WHO for the WISN user manual.
As a Q1 journal committed to maintaining high scientific and methodological standards, I recommend not approving this manuscript. The study contains substantial flaws in both the Methods and Results sections, which undermine the validity and reliability of the findings. These issues must be thoroughly addressed before the manuscript can be reconsidered for indexing.
The topic of optimizing clinical laboratory staffing using the WISN method is relevant and potentially valuable for healthcare workforce planning. The authors have attempted to address a practical issue with implications for laboratory efficiency and quality of care. The title is overly long and includes abbreviations, which should be avoided. The abstract requires complete rewriting to better reflect the structure of a scientific summary. The current methods section of the abstract incorrectly states the study aim rather than describing the methodology applied. The manuscript inconsistently uses the term "lab/s." The full term “clinical laboratory/laboratories” should be used throughout for clarity and professionalism. A clear sample size calculation is missing. This is essential to assess whether the study was adequately powered to draw meaningful conclusions. Table 1 title should be revised to be more specific, and it would be helpful to include the number of tests performed per year. Results are currently presented in narrative form; they would benefit from tabular and/or graphical representation for clarity and better interpretation. The methodology lacks detail and structure. The statistical analysis is incorrectly placed in the Results section. It should be described under the Methods section The data were collected during the COVID-19 pandemic, which could have significantly influenced staffing patterns and workload. However, the manuscript does not mention or discuss the potential impact of the pandemic on the findings. The issue of financial allowances or compensations for overwork is raised but not adequately addressed or analyzed. This aspect needs more clarity and data support.
In summary, the manuscript requires substantial revisions to meet the scientific and methodological standards expected of a Q1 journal.
Is the work clearly and accurately presented and does it cite the current literature?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Is the study design appropriate and is the work technically sound?
No
Are the conclusions drawn adequately supported by the results?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
No
Reviewer Expertise:
Histopathology
I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.
We sincerely thank the reviewer for their constructive feedback and insightful suggestions.
We agree and have revised the title to: Workload Assessment of Technical Staff in a Clinical Laboratory in South India.
The abstract has been revised in a structured format (Background, Methods, Results, Conclusion). In the Methods section, we now provide a concise description of the WISN methodology, data sources (laboratory statistics over two years), and statistical tests applied, instead of restating the study aim.
We have carefully revised the manuscript to replace “lab”/“labs” with “clinical laboratory/laboratories” consistently.
Table 1 has been revised and retitled:
Workload components, standard workload, annual number of tests, and calculated staff requirements.
The Results section has been revised to improve clarity and interpretation by including tabular presentations. Specifically, one table summarizes the workload components, standard workloads, and calculated staff requirements (Table 1), and a second table presents actual versus WISN-based staffing with corresponding WISN ratios and workload pressure (Table 3). These tables provide a clear and comprehensive view of staff requirements and workload distribution across clinical laboratory sections.
We agree. The description of the Shapiro–Wilk test and Kruskal–Wallis test has been moved from the Results to the Methods section. Only the test outcomes are retained in the Results.
We thank the reviewer for this important point. We have now addressed this in the Limitations section:
The data were collected during the COVID-19 pandemic, which may have influenced staffing availability and workload patterns (e.g., absenteeism, emergency testing demands). This context may partly explain the higher workload pressure observed.
We acknowledge this concern. We have clarified in the Discussion that WISN is a workforce planning tool and does not account for financial compensation.
We have added the following statement under -
We respectfully clarify that our study was designed as a workload assessment using the WHO-developed WISN methodology. While it is not a hypothesis-driven clinical trial, it systematically applies an internationally recognized tool to evaluate workforce adequacy in a real-world setting. We have strengthened the Methods section to provide clearer structure and detail and revised the Discussion to focus specifically on workload assessment (rather than utilization), ensuring the findings are appropriately contextualized.
We have addressed the reviewer’s suggestions, including restructuring the abstract, refining the Methods and Results sections, revising terminology, updating tables and figures, and expanding the discussion of limitations. We believe these revisions have substantially improved the scientific and methodological clarity of the manuscript.
We appreciate the reviewer’s insightful comments, which have significantly improved the clarity, focus, and overall quality of the manuscript.
Dr. Asha Patil and co-authors have addressed a crucial and important concern regarding the health workforce, which presents significant challenges for Health Service Managers. The study focuses on the workload of technical staff in the flow cytometry section, which is a valuable contribution to understanding staff management in this domain.
However, there are a few issues that need attention. The title and methods section mention staff utilization, but this aspect is not sufficiently addressed in the study. Since the research focuses on workload rather than utilization, it would be more appropriate to revise the title to reflect the study's actual focus.
Additionally, the study is limited to one hospital in South India, and it does not include data from other hospitals. Expanding the scope or acknowledging this limitation would enhance the generalizability of the findings.
Minor corrections are also recommended regarding the use of the short form "WISN" (Workload Indicators of Staffing Needs) throughout the article to ensure consistency.
Further clarification on the calculation of service standards and allowance factors would be beneficial for better understanding. Specifically, the first WISN step, which determines staffing priorities, should emphasize the inclusion of staff based on the work's importance, rather than detailing which staff works in specific sections. This will help clarify the intent of the methodology.
In the discussion and conclusion sections, particularly on Page No. 7, there is a need for better clarity. Some paragraphs appear incomplete, and the discussion should focus more on workload assessment and its importance, rather than on utilization, as the current analysis does not provide insights into manpower utilization.
These revisions will contribute to the overall clarity and precision of the study.
Is the work clearly and accurately presented and does it cite the current literature?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Is the study design appropriate and is the work technically sound?
Partly
Are the conclusions drawn adequately supported by the results?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
Reviewer Expertise:
Hospital administration , Quality & patient Safety
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
We sincerely thank the reviewer for their constructive feedback and insightful suggestions.
The title and manuscript have been revised to focus exclusively on
Explanations of the WISN methodology, including service standards and allowance factor calculations, have been added for clarity.
The discussion and conclusion sections revised to emphasize workload findings, with reduced focus on utilization.
A statement acknowledging the limitation of data being drawn from a single hospital has also been included to address generalizability concerns.
Furthermore, WISN terminology and abbreviations have been standardized throughout the manuscript for consistency and accuracy.
We appreciate the reviewer’s insightful comments, which have significantly improved the clarity, focus, and overall quality of the manuscript.