Designing a TB lab workload assessment tool for staff allocation

Australia's tuberculosis laboratory network stretches across vast distances, with reference capacity concentrated in metropolitan centres such as Sydney, Melbourne, Brisbane, Perth and Adelaide. Sputum smear microscopy and GeneXpert services extend into regional hospitals and remote clinics, and specimen transport times from the Kimberley or Pilbara to Perth can exceed 48 hours. The Queensland Mycobacterium Reference Laboratory in Brisbane handles culture, drug susceptibility testing and whole-genome sequencing for much of northern Australia, including services for the Torres Strait and Cape York communities where TB incidence among Aboriginal and Torres Strait Islander peoples remains a recognised public health priority. Designing a workload assessment tool for staff allocation in this context means accounting for distance, sample logistics, and uneven demand.

A workload assessment tool gives laboratory managers a structured way to quantify how much staff time each test category consumes, where bottlenecks appear, and how changes in sample volume or test mix should translate into full-time equivalent (FTE) adjustments. The tool sits squarely within the Personnel and Assessment Quality System Essentials, and its outputs feed directly into continual improvement plans. For laboratories already operating under the GLI Quality Tool roadmap, a workload instrument strengthens the evidence base for staffing decisions that were previously made on instinct or historical precedent.

The starting point for any such tool is clarity about which activities count as workload. Microscopy slides, mycobacterial culture bottles, molecular assays, drug susceptibility tests, and follow-up reporting each carry different time burdens. A robust instrument also captures non-analytical tasks: specimen reception, data entry, cleaning, quality control, equipment maintenance, internal audits, and training. Without this wider lens, the tool will overestimate bench capacity and underestimate the human effort required to keep the laboratory compliant and safe.

This article walks through five practical phases for building a TB laboratory workload assessment tool, from mapping process steps and capturing time-motion data, through FTE conversion, field validation, and finally integration with broader quality system documentation. It is written for laboratory managers, quality officers and senior scientists across Australian public pathology services and reference laboratories.

Mapping test activities and process steps

The first task is to break every workflow into its component steps and assign a realistic time value to each one. A useful starting template lists each test method offered by the laboratory — ZN smear, fluorescence microscopy, MGIT or Löwenstein-Jensen culture, phenotypic DST, line-probe assays, Xpert MTB/RIF or Ultra, and whole-genome sequencing where available — then traces the journey of a specimen from reception to final report. Process mapping often reveals hidden steps that consume more time than expected: repeat testing for contaminated cultures, batch preparation of media, autoclave cycles, and supervisory review of unexpected results.

Where the laboratory uses external referral pathways, such as sending a sample from Darwin to the Queensland Mycobacterium Reference Laboratory in Brisbane for second-line drug susceptibility, the workload tool should still record the administrative time spent packaging, dispatching and following up the referral. In Western Australia, PathWest coordinators spend meaningful hours arranging category-B courier pickups from regional hospitals, and that time belongs in the assessment just as much as bench work does.

It helps to engage bench scientists, scientific officers, technical officers and laboratory assistants directly when building the steps list. Staff who perform a task daily usually provide more accurate time estimates than managers who observe occasionally. Many laboratories also consult the Royal College of Pathologists of Australasia's quality assurance frameworks for benchmarking average test durations, then adjust these to local reality.

Building the data capture framework

Once the process map is agreed, the next step is to design the data capture template. A practical spreadsheet or database needs columns for date, specimen identifier, test ordered, time of receipt, time of report, technologist identifier, and any rework or repeat events. Some Australian laboratories use middleware or laboratory information system extracts to auto-populate much of this data, while smaller sites still rely on manual bench logs. Either approach is workable, provided the data is consistent and reviewable.

The data capture sheet should also flag quality-related events: equipment downtime, failed runs, contamination rates, and instances where a result falls outside expected specifications. These events ripple into staffing because every failed run requires investigation, repeat testing and documentation. Linking the workload sheet to the laboratory's out-of-specification procedures ensures the time spent managing these events is counted, not absorbed into general miscellaneous categories.

For laboratories operating across multiple sites, including hub-and-spoke arrangements in Queensland, Western Australia and the Northern Territory, the capture template should record site of origin, courier dispatch time, and receipt time at the reference centre. These data points support later analysis of where surge capacity should sit. A capture window of four to eight weeks is usually enough to reveal patterns, although seasonal variation in TB screening may warrant additional time.

Converting workload into staffing requirements

Raw time data becomes useful when it is converted into FTE equivalents. A common method sums the total minutes spent on each activity over the data collection period, divides by the average productive minutes per FTE per day (typically 360 minutes after deducting breaks, meetings, training and leave coverage), and produces an FTE figure per activity. These figures are then summed to give the total FTE requirement for the laboratory's current test menu and volume.

Seasonal multipliers should reflect Australian realities. TB screening volumes typically rise in late summer and early autumn as newly arrived migrants, refugees and international students settle into the country, with peaks around February and March in capital cities. Conversely, remote-area screening may dip during the wet season in the tropical north, when access to some communities is limited. A workload model that uses an annual average will underestimate peak demand in Sydney and Melbourne, and underestimate trough capacity in Darwin.

The conversion should also include a contingency factor of 10 to 20 percent to account for training, leave, equipment downtime and unexpected surges. Laboratories that support outbreak investigations, such as the response frameworks coordinated by state health departments, need an additional reserve capacity built into the model. The result is a defensible staffing plan that can be presented to hospital executives, health department planners and union representatives when workforce changes are proposed.

Validating the tool in real laboratory conditions

A workload assessment tool is only credible once it has been tested against reality. Validation involves running the model for three to six months and comparing its FTE recommendations with the actual staffing rosters used during that period. Discrepancies between predicted and observed capacity usually point to gaps in the process map, missing data fields, or tasks that were underestimated.

In practice, validation often uncovers surprising findings. A regional New South Wales hub may discover that transport logistics consume more staff time than microscopy itself, while a metropolitan reference centre may find that supervisory oversight — including mentoring junior scientists through RCPAQAP proficiency testing schemes — is more time-intensive than initial estimates suggested. Validation is also the right moment to check that the tool accounts for cross-training, where a single scientist covers multiple test methods during a shift.

Once the model is refined, it should be piloted as part of the laboratory's annual planning cycle. The piloting phase should include consultation with laboratory managers, quality managers and frontline staff, with documented feedback incorporated into the next version. Version control, review frequency and approval pathways should mirror those used for other quality documents, including the laboratory's tailored safety manual, which similarly requires regular review and approval.

Linking workload planning with the quality management system

The workload assessment tool reaches its full value when it is integrated into the laboratory's broader quality management system rather than treated as a stand-alone spreadsheet. The Personnel QSE expects evidence that staffing levels are matched to workload, while the Assessment QSE requires laboratories to monitor and evaluate their performance against defined indicators. Workload data should therefore be reviewed at management review meetings, alongside non-conformities, customer complaints, internal audit findings, and proficiency testing results.

Integration also means connecting workload outputs to continual improvement projects. If the model shows that contamination rates are driving additional culture work, the root cause analysis and corrective action should run in parallel, not separately. If equipment downtime is creating hidden FTE pressure, the Equipment QSE's preventive maintenance schedule becomes a direct input into the workload forecast. The tool thus becomes a living document that informs operational decisions throughout the year.

For Australian laboratories working toward accreditation under the National Pathology Accreditation Advisory Council standards, this integration is particularly important. Assessors increasingly look for evidence that staffing decisions are data-driven rather than inherited. A well-designed workload assessment tool, regularly reviewed and clearly linked to the quality system, demonstrates that the laboratory understands its capacity and plans proactively for demand fluctuations.

Ready to put these ideas into practice? The GLI Quality Tool provides phase-specific checklists and downloadable templates that can be adapted for workload assessment, including guidance on aligning staffing analyses with Personnel and Assessment QSE requirements. Start by mapping one test workflow end-to-end, capture time data for four weeks, and use the resulting FTE estimate as the foundation for a staffing conversation with your laboratory director. The tool will only become useful once it is used — and the first version does not need to be perfect, it needs to exist.