In the realm of clinical laboratory operations, turnaround time is more than an efficiency metric. It influences diagnostic decisions, patient flow, bed utilisation, clinician confidence and the laboratory’s reputation for reliability.
Yet the analytical test itself is often only a small part of the total journey. A specimen may spend more time waiting for collection, transport, accessioning, centrifugation, analyser loading or result verification than it spends being processed. Value Stream Mapping (VSM) makes this hidden time visible by combining specimen flow, information flow, queues, process times and customer requirements on one page.
Research in clinical laboratories has shown that Lean methods, including VSM, can produce substantial gains. One published study reduced average turnaround time from 180 to 95 minutes in haematology and from 268 to 208 minutes in biochemistry after identifying non-value-adding factors and redesigning the future state. PubMed summarises the study here.
The fundamental purpose is not to make people work faster. It is to design a more dependable system in which the right specimen, information and decision move through the laboratory with less waiting and rework.
1. Select the Right Clinical Laboratory Scope
A laboratory contains several value streams, not one universal process. Outpatient blood collection, emergency department testing, microbiology cultures, anatomic pathology and send-away testing have different demand profiles, constraints and customer expectations.
Begin with a clearly defined specimen family and measurable start and end points.
A practical scope might be:
- Start: specimen collection timestamp
- End: verified result available in the laboratory information system and electronic health record
- Specimen family: routine emergency department CBC and chemistry specimens
- Customer: emergency clinician and patient
- Primary CTQ: verified result within 120 minutes of collection
- Exclusions: specialised send-away tests, inpatient rounds and tests requiring extended incubation
Avoid mapping the entire laboratory initially. A focused family makes demand, takt time, bottlenecks and variation easier to see. Once the method is proven, expand the map to connected streams.
Capture both the Voice of the Customer: for example, “results must support treatment decisions within two hours”: and the Voice of the Process, using actual timestamps rather than assumptions.
2. Build the Current-State Map at the Gemba
A credible current-state map is built by following specimens, not merely interviewing department managers. Observe the process across collection, transport, accessioning, preparation, analysis, verification and release.
Record:
- Collection and labelling time
- Time the specimen enters transport
- Arrival and accessioning time
- Centrifugation and aliquoting time
- Analyzer loading and result generation
- Manual review or autoverification decision
- Final result release time
- Queue size, batch size, defects and staffing at each step
Include information flow. An order missing a priority flag, an incomplete clinical note or a delayed LIS message can create the same operational effect as a physical queue.
A current-state VSM should show where the specimen is, where the information is, who owns the next decision and how long the specimen waits between activities.
3. Fully Worked Example: Routine ED Specimens
Consider a hypothetical hospital core laboratory processing 240 routine ED specimen sets per day during a 10-hour productive window.
Demand and takt time
Available productive time:
- 10 hours × 60 minutes = 600 minutes
- Daily demand = 240 specimen sets
- Takt time = 600 ÷ 240 = 2.5 minutes per specimen set
Takt time is the required production rhythm. It does not mean every specimen must be completed in exactly 2.5 minutes; it indicates the average rate at which completed results must leave the system to meet demand.
Current-state observations
| Process step | Touch/process time | Average waiting time | Primary resource |
|---|---|---|---|
| Collection and patient identification | 4.0 min | 18 min | 3 phlebotomists |
| Labelling and order check | 1.5 min | : | Phlebotomist |
| Internal transport | 2.5 min | 25 min | Courier route |
| Accessioning | 3.0 min | 32 min | 2 accession staff |
| Centrifuge and aliquot | 5.0 min | 28 min | 2 processors |
| Analyzer testing | 6.0 min | 46 min | 2 analyzer channels |
| Result review and verification | 2.0 min | 24 min | 1 senior technologist |
| Electronic release | 0.5 min | : | LIS workflow |
| Total | 24.5 min | 173 min | 9.0 FTE across the window |
The current average lead time is therefore:
24.5 + 173 = 197.5 minutes, or approximately 198 minutes.
Process cycle efficiency is:
24.5 ÷ 197.5 × 100 = 12.4%
This means the specimen is actively being processed for only about one-eighth of its total time in the value stream. The largest constraint is the combined analyser and verification queue, not the laboratory’s nominal testing capability.
The data also shows why adding staff without analysing the flow may have limited effect. Collection, accessioning and processing each hand work to the next stage in batches, creating uneven arrivals and excessive work in process.
4. Identify the Eight Wastes in the Laboratory
The eight DOWNTIME wastes appear clearly in this example:
- Defects: incomplete labels, mismatched orders and specimens requiring recollection.
- Overproduction: processing non-urgent specimens in large batches before the next step is ready.
- Waiting: specimens held for courier rounds, centrifuges, analyzer availability or senior review.
- Non-utilised talent: experienced technologists spending time searching for missing information rather than applying clinical expertise.
- Transportation: repeated movement between collection points, accessioning benches and analyzers.
- Inventory: racks of unprocessed specimens and a growing verification queue.
- Motion: staff walking to locate tubes, labels, forms or equipment.
- Extra-processing: duplicate data entry, manual result checks that could be safely autoverified and repeated status enquiries.
The customer defines value. In this case, value is not the movement of a tube or the completion of an internal checklist. It is the production and delivery of a reliable, clinically usable result.
5. Design the Future State
The future-state map should reduce waiting while protecting analytical quality, patient identification and regulatory requirements.
Concrete countermeasures include:
- Electronic order and barcode verification at collection to reduce labelling defects and rework.
- A dedicated ED specimen lane with a visual priority rule for time-sensitive samples.
- Smaller transport intervals, changing from four large courier rounds to an hourly pull route.
- FIFO racks and controlled WIP limits between accessioning, preparation and analysis.
- Standard work for centrifugation and aliquoting, with specimens released continuously rather than held for batch completion.
- Autoverification rules for defined low-risk result ranges, leaving exceptions for senior review.
- An Andon-style visual signal on the laboratory dashboard when a queue exceeds its trigger level or a result approaches the CTQ limit.
- Cross-training two accession and processing staff so capacity can be shifted to the active bottleneck.
- Daily review of the Voice of the Process, including median TAT, 90th-percentile TAT, WIP and first-pass yield.
Autonomation, or Jidoka, can support this design when instruments and software detect abnormal conditions, stop unsafe progression and alert the responsible team rather than silently passing a problem forward.
6. Current State Versus Future State
The following future-state results assume the same daily demand and no increase in total staffing.
| Metric | Current state | Future state | Improvement |
|---|---|---|---|
| Average lead time | 198 min | 82 min | 58.6% reduction |
| Process time | 24.5 min | 22.0 min | 10.2% reduction |
| Process cycle efficiency | 12.4% | 26.8% | More than doubled |
| 90th-percentile TAT | 276 min | 125 min | 54.7% reduction |
| Results within 120 min | 61% | 93% | +32 percentage points |
| Average WIP | 76 specimens | 29 specimens | 61.8% reduction |
| Relabel/rework rate | 3.8% | 1.1% | 71.1% reduction |
| Staffing | 9.0 FTE | 9.0 FTE | Redeployed, not expanded |
The future state does not eliminate necessary clinical review. It removes avoidable queues, clarifies priority rules and places decision-making closer to the point where information is available.
7. Kaizen Sequencing Plan
Sequence improvement so that each intervention supports the next:
Week 1: Stabilise and measure
Confirm operational definitions, baseline TAT, specimen family, CTQ requirements and timestamp reliability. Use a process cycle efficiency calculator to establish the baseline.
Weeks 2–3: Remove visible flow barriers
Implement barcode checks, FIFO racks, WIP limits, standard work and revised transport intervals. These are practical Lean countermeasures that can be tested quickly.
Weeks 4–6: Address the bottleneck
Analyse analyzer loading, exception rates and verification queues. Apply root-cause tools from the Analyse Phase of DMAIC, using Pareto analysis, process stratification, box plots or hypothesis testing where the data supports it.
Weeks 7–8: Pilot and verify
Run the future state on one ED shift or specimen family. Compare median and 90th-percentile TAT, defect rate, throughput, staffing load and patient-impact measures.
Weeks 9–12: Control and sustain
Create a control plan, visual dashboard, escalation triggers and ownership routine. Review approval checkpoints carefully: governance is essential, but excessive approval layers can recreate bottlenecks.
Build the Capability to Improve Clinical Processes
A laboratory VSM is most effective when it connects Lean flow thinking with Six Sigma measurement and statistical analysis. Teams need to distinguish common-cause variation from special-cause variation, separate a bottleneck from a staffing symptom and verify that improvements do not compromise quality or safety.
The Lean Six Sigma Practitioner’s Guide provides a broader framework for selecting tools across Define, Measure, Analyse, Improve and Control. For professionals leading cross-functional laboratory projects, CSSC-accredited Green Belt training develops the practical capability to map processes, analyse data, lead kaizen activity and sustain measurable gains.
Choose the Lean Six Sigma certification level that matches your role, then apply the method to one clinical value stream with real data, visible ownership and a clear customer CTQ.
Kaizen. Kai-Care. Kai-Done. ( Lean Six Sigma)
