GLI GLI Quality Tool
GLI Quality Tool — Version 2.0

Building a monthly TB laboratory quality dashboard

Creating a TB laboratory monthly quality indicator dashboard turns scattered records about sputum specimens, AFB smears, cultures and molecular tests into a practical management tool. It shows how quickly results are authorised, how often specimens or reports contain errors, and where corrective action may improve care. Its value comes from consistent definitions and a short, repeatable review cycle, not a large display of numbers.

Each month, laboratory and collection teams can compare workload, turnaround, rejected specimens, invalid results and amended reports against agreed targets. The format should fit Australian conditions: concise enough for a staff-room screen or printed meeting pack, clear about urban, regional and remote services, and careful not to expose patient or community information. It supports, rather than replaces, the quality management system, accreditation activities and communicable disease notification processes.

Define turnaround time before collecting data

Start with one primary laboratory turnaround time: elapsed hours from receipt of an acceptable specimen in the testing laboratory to electronic validation and authorisation of the final report. Record collection, receipt, test commencement and report authorisation as separate events. This reveals where delay occurs and prevents transport time from being confused with laboratory processing time.

TB testing includes several stages with different clocks. A rapid nucleic acid amplification test may be completed in a day, while mycobacterial culture can require weeks; an average across all TB work would therefore be misleading. Calculate turnaround separately for each test or report pathway, and include the median, the 90th percentile and the percentage meeting the locally agreed service target. Report pending workload by age as well.

Document every pause, repeat, referral and rerun rather than removing difficult cases from the calculation. Raw elapsed time and adjusted laboratory time can both be valuable if the rules are explicit. The GLI Quality Tool offers a four-phase quality management roadmap organised around twelve Quality Systems Essentials, providing a useful basis for defining measurement rules before results are displayed.

Count errors with stable denominators

Use the word error carefully. A rejected or unsatisfactory specimen is a pre-analytical failure; an invalid test result may reflect a specimen or analytical issue; and a corrected or amended report is a post-analytical event. Combining them into one percentage hides the cause and the corrective action needed. Show each category separately on the dashboard.

Give every rate a transparent numerator and denominator. Rejections can be divided by all specimens received, invalid results by tests performed, and amended reports by reports authorised. State whether duplicate specimens, repeat tests and referrals are included. Counts must reconcile with the laboratory information system, accession register and monthly workload total; if they do not, investigate the data before presenting a rate.

Capture reason codes consistently, including insufficient or unsuitable sputum, mislabelling, transport delay, request errors, instrument failure and uninterpretable results. Retain near misses and stockouts as supporting measures, but do not call every disruption an error. Australian staff will usually find local terms such as pathology request, microbiology and authorised report clearer than imported laboratory jargon.

Select indicators that support action

Keep the first dashboard manageable. Select measures tied to decisions the team can make, then add context that prevents a poor result from being judged without explanation. Every indicator needs an owner, source, frequency, denominator and target.

Core monthly measures

Supporting quality measures

Targets should come from service commitments, expected complexity and the laboratory’s own baseline, with clinical and laboratory staff agreeing on what is realistic. A remote laboratory with a once-daily courier should not be assigned the same collection-to-result expectation as an on-site service, although both can have clear internal processing targets. Rapid molecular tests and culture should never share a single target.

Display both numerator and denominator beside every percentage. A 50 per cent invalid rate based on four tests requires very different attention from the same percentage based on 400. Where numbers are small, use counts and suppress or combine categories that could identify a patient, facility or small community.

Build a repeatable monthly data workflow

Extract data from the laboratory information system where possible, supplemented by instrument logs, specimen receipt manifests, rejection records and referral registers. A controlled spreadsheet can work in smaller services, provided one master file feeds the dashboard and formulas are protected from accidental edits. Avoid manually retyping key figures each month.

Use a short data dictionary describing each indicator, its inclusion rules, source fields, exclusions and responsible person. Freeze the extraction date, record corrections and retain an audit trail. Reconcile monthly totals before calculating rates, and follow local privacy, records retention and information-security requirements. Patient identifiers should not appear in the working dashboard.

Agree how referrals are handled, especially when specimens move between a local laboratory and a state reference laboratory. Record receiving and final-report timestamps so the dashboard does not assign another laboratory’s delay to the originating service. The phase-specific checklists can help teams check implementation readiness and identify missing documentation or review steps.

Design the dashboard for Australian conditions

Use Australian date formats, spell authorised consistently and provide plain-language notes beside each measure. A layout that works on a widescreen monitor may fail in a regional laboratory using an older computer, so test both screen and A4 print versions. Do not rely on red and green alone; add labels, patterns or symbols so status remains clear for colour-blind users and in monochrome printouts.

Australia’s public hospital microbiology services, Aboriginal health services and private pathology providers may sit in different referral networks and use different information systems. A specimen may be processed locally, routed to a hub or referred to a state reference laboratory. Compare a service with its own trend and with peers that have comparable testing, staffing and transport conditions, not with every laboratory in the country.

Distance matters from Alice Springs and Darwin to Perth, Cairns and remote communities, where specimens may travel by scheduled road, air or specialist courier and some collections occur only on particular days. Track collection-to-receipt delays separately and use local service knowledge when interpreting them. Aboriginal Medical Services and Aboriginal Controlled Community Health Organisations may support collection and transport, making partnership and respectful, culturally safe review essential.

National TB outcomes are shaped by social and geographic inequity, and Aboriginal and Torres Strait Islander communities are affected disproportionately. Monitor access and process measures with community partners, and interpret site-level data without assuming that a higher rejection rate reflects specimen quality alone. Small cells need strong privacy controls; aggregated equity monitoring should be led with communities, not used to label a service or population.

Review trends and close the quality loop

Place a short monthly review in the laboratory quality meeting. Compare the current month with the previous month and, once available, a rolling three- or six-month trend. Use run charts or another method the team understands, but do not overinterpret small fluctuations. Every exception should lead to a documented process review, not automatic blame directed toward a staff member.

Investigate likely causes across the pathway: courier scheduling, specimen acceptance, request information, analyser performance, internal quality control, staffing, stock availability, result verification and electronic reporting. Record corrective and preventive actions with an owner, due date and evidence of effectiveness. Test the change in the next reporting cycle and retain the outcome in the quality management system, whether that is a nonconformance record, risk register entry or controlled procedure.

Share a one-page summary with collection staff, clinicians and public health partners when appropriate. The dashboard should support discussion of missed opportunities, delayed treatment decisions and system fixes. Feed recurring problems into the relevant Quality Systems Essentials, including safety, personnel, equipment, documentation, assessment and continual improvement, and align actions with the GLI Tool’s phase structure.

Start the first monthly cycle

Begin with a contained pilot: one TB assay, one laboratory or collection network and one reporting month. Agree the indicator definitions, data owner and review meeting, then test the extraction with the people who perform the work. Add historical months only after the definitions and denominators have been verified.

Display the first results with counts, context and one or two clear priorities. Assign an accountable owner to every red or amber item, set a due date and check effectiveness in the next cycle. Publish the dashboard where staff already meet, and protect patient and community confidentiality.

Start the process now with a named laboratory lead, a one-page indicator set and a scheduled monthly review. The first cycle will establish the reliable baseline from which every later improvement can be measured.