Conducting a workload analysis to improve TB lab efficiency
Tuberculosis laboratories often face fluctuating specimen volumes, urgent diagnostic requests, staffing constraints, and equipment interruptions at the same time. A workload analysis makes these pressures visible. Instead of relying on impressions, laboratory leaders can examine how much work enters each process, how long tasks take, and where capacity is being lost.
The aim is not simply to make staff work faster. A sound analysis supports safer staffing, better scheduling, more balanced use of instruments, and timely reporting of results. It can also reveal whether delays arise from testing itself, pre-analytical steps, documentation, maintenance, stock management, or data entry.
For laboratories operating in varied resource settings, the process should be practical and proportionate. A small laboratory may begin with a one-week review of core activities, while a larger service may need several months of data to capture seasonal demand and referral patterns.
Why workload analysis matters in TB testing
Workload is broader than the number of specimens received. It includes registration, sorting, preparation, testing, result validation, reporting, cleaning, quality control, equipment maintenance, inventory tasks, and communication with clinicians or treatment programs. A laboratory that processes 100 specimens may carry very different demands depending on the testing methods and reporting requirements involved.
TB workflows also contain activities that are difficult to see in routine statistics. Staff may spend substantial time resolving incomplete request forms, locating samples, repeating invalid tests, troubleshooting instruments, or entering results into multiple systems. If these tasks are excluded, managers may underestimate the actual labor required.
A structured review can support decisions about shift patterns, cross-training, instrument placement, referral arrangements, and turnaround-time targets. It provides evidence for distinguishing a genuine staffing gap from a process problem that can be addressed through clearer procedures or better coordination.
Define the work before counting it
Begin by mapping the complete specimen pathway, from collection and transport to result dispatch and record retention. Separate the pathway into recognizable activities and identify who performs each one. For example, molecular testing may involve receipt, accessioning, sample preparation, loading, run monitoring, result review, reporting, and waste management.
Each activity should have a clear definition. “Testing” might mean hands-on preparation only, or it might include instrument setup and post-run verification. Without consistent definitions, two staff members can record the same task differently, making the final analysis unreliable.
Classify work by method, specimen type, urgency, and complexity. Routine molecular testing, smear microscopy, culture-related procedures, drug-resistance testing, referral packaging, and quality control should not automatically be treated as equivalent units. A weighted workload approach may be necessary when one test requires considerably more staff time or technical attention than another.
The laboratory information system guide can help teams consider how specimen, result, and workflow data should be organized. Even when a full information system is not yet available, agreed data fields and consistent registers improve the quality of a manual review.
Gather reliable data with limited disruption
Choose a defined observation period that reflects normal operations. Include enough days to capture routine variation, such as weekly referral schedules, staff rotations, supply deliveries, and periods of increased clinical demand. If possible, compare a typical month with a high-volume or low-volume month.
Useful measures include the number of specimens received, tests performed, repeats, invalid results, urgent requests, referrals, and reports issued. Record hands-on time separately from elapsed time. A test may take five minutes of active staff effort while requiring an hour before a result is available because of batching or instrument run times.
Data collection can combine registers, instrument logs, information-system records, staff activity sheets, and short observations. Staff should understand that the purpose is process improvement rather than individual surveillance. Recording should be simple enough to complete during normal work, with clear instructions and periodic checks for missing or implausible entries.
| Workload measure | What it reveals | Practical use |
|---|---|---|
| Specimens received by day | Demand and arrival patterns | Plan staffing and receiving coverage |
| Hands-on time per activity | Direct labor requirement | Estimate capacity and identify labor-intensive steps |
| Instrument runs and batch size | Equipment utilization | Adjust batching, schedules, and maintenance windows |
| Invalid or repeated tests | Rework burden | Investigate training, specimen quality, or equipment problems |
| Reports delayed beyond target | Service performance | Locate bottlenecks and prioritize corrective action |
| Non-testing duties | Hidden workload | Protect time for quality, records, stock, and safety tasks |
Compare demand with available capacity
Once the data are assembled, estimate capacity by activity rather than by headcount alone. A laboratory with four staff members may have only one person qualified for a specialized procedure, while another may be unavailable for testing because they are handling reception, reporting, or quality tasks.
A basic capacity estimate can use the available staff time multiplied by the proportion of time realistically available for the activity. Subtract scheduled leave, meetings, training, maintenance, cleaning, quality control, and other essential duties. The result will be more useful than assuming every paid hour is available for specimen processing.
Compare demand and capacity across days and workflow stages. A monthly total may appear manageable while Monday receiving creates a backlog that affects the rest of the week. Similarly, an instrument may have sufficient theoretical capacity but remain underused because samples arrive late, reagents are unavailable, or only one person is authorized to operate it.
Look for queue formation, idle periods, overtime, repeated interruptions, and work transferred between staff. These patterns often show that the issue is timing or coordination rather than absolute volume. A revised courier schedule, protected reporting period, cross-training, or a clearer escalation process may release capacity without adding equipment.
Turn findings into targeted changes
The analysis should lead to a short list of prioritized actions. Start with changes that improve flow, safety, and result timeliness while requiring limited resources. Examples include separating reception from testing tasks, preparing worksheets in advance, standardizing specimen acceptance checks, grouping compatible runs, or assigning a daily role for result review.
Avoid treating every variation as a reason to increase staffing. If repeat testing is high, investigate specimen quality, contamination, assay handling, environmental conditions, and equipment performance. If staff spend excessive time searching for information, improve documentation and records control. If urgent requests disrupt routine work, define criteria and a process for handling them.
Workload findings should be reviewed alongside the laboratory’s wider quality system. The quality management framework provides a useful foundation for connecting workload evidence with personnel competence, equipment, documents, assessment, safety, and continual improvement. This prevents efficiency efforts from undermining essential controls.
Any change should have an owner, a deadline, and a measurable indicator. For example, a laboratory might aim to reduce reports exceeding the turnaround-time target, decrease invalid runs, or cut the number of specimens waiting at the end of a shift. Indicators should be reviewed after implementation to confirm that the change produced the intended effect.
Protect quality while improving efficiency
Efficiency in TB testing must never mean bypassing biosafety, quality control, verification, or result authorization. A faster process that increases errors, exposure risk, or incorrect reporting is not an improvement. Workload decisions should therefore include risk assessment and consultation with the staff who perform each activity.
Competence is another essential safeguard. Cross-training can reduce dependency on one person, but staff must be trained, assessed, authorized, and periodically observed before taking on independent duties. The workload review may show that the laboratory needs a training plan as much as it needs a revised roster.
Use the findings to examine whether equipment, reagents, workspace, and information systems match the workload. An instrument with unused capacity may still be unsuitable if preventive maintenance is overdue or consumables are unreliable. Conversely, a modest system may perform well when specimen flow, stock control, and staff responsibilities are organized.
Priorities for practical action
- Map every major activity from specimen receipt through result reporting, including hidden administrative and quality tasks.
- Measure both the volume of work and the hands-on time required for different procedures.
- Compare demand by day and workflow stage instead of relying only on monthly totals.
- Investigate rework, delays, and interruptions before requesting additional resources.
- Assign owners and indicators to each improvement action, then repeat the review at an agreed interval.
Make workload review part of routine management
A single assessment provides a useful snapshot, but workload changes with disease patterns, testing policies, referral networks, staffing, and technology. Laboratories should repeat the review after a major change, such as introducing a new molecular platform, expanding testing hours, changing courier routes, or adopting a new reporting system.
A short monthly dashboard can keep the process manageable. It might include specimens received, tests completed, invalid results, staff time used, turnaround-time performance, equipment downtime, and outstanding reports. Trends are more informative than isolated figures, especially when interpreted with notes about stock-outs, training, outbreaks, or staff absence.
Discuss results during regular management and quality meetings. Staff involvement helps validate the data and brings forward practical explanations that numbers alone cannot show. It also creates shared ownership of solutions and makes it easier to identify improvements that can be tested quickly.
The GLI Quality Tool’s phased approach and Quality Systems Essentials can help laboratories place workload analysis within a broader improvement cycle. Data collection, action, review, and adjustment should become part of normal laboratory governance rather than an occasional response to crisis.
A well-designed workload analysis gives TB laboratories a defensible basis for organizing people, equipment, time, and information. Begin with one clearly defined workflow, collect dependable data, address the most consequential bottleneck, and track the result. Regular, evidence-based review can improve efficiency while preserving the accuracy, safety, and reliability that patients and TB programs depend on.