## 3 Results
### Process Turnaround Time (TAT) Analysis  
The previous added-value approach has not considered waiting times of the process, which was the main type of waste identified in the VSM. The Turnaround Time (TAT) indicator not only includes the testing process time (equipment and direct labour time), but also identifies waiting times. The obtained results when TAT is calculated for HIV and COVT-COVIDGG testing, in the pre-TLA (VSM analysis) and post-TLA (VSD analysis) scenarios are shown in Figure 6. After Value-Stream Design, the TAT decreased from pre- to post-TLA by up to 87.3% and by up to 19.3% for HIV and COVT-COVIDGG samples. This improvement is particularly significant for HIV samples, mainly due to the elimination of the 24 h storage in the refrigerator. Currently, the majority of tests can be completed within a single 7 h working shift. Figure 4: COVT-COVIDGG serologic sampling: Results comparison between pre-TLA and post-TLA in terms of added-value. (**a**) Number of activities comparison; (**b**) Indicators of activities percentage. Figure 5: COVT-COVIDGG serologic sampling: Results comparison between pre-TLA and post-TLA in terms of added-value. (**a**) Total Process Time (TPT) comparison; (**b**) Percentage in Total Process Time (TPT) comparison. Figure 6: Total process Turnaround Time (TAT) for HIV and COVT-COVIDGG testing in pre-TLA and post-TLA scenarios. The time recordings obtained when results are analysed considering the TAT contributor, i.e., Total Equipment Time (TET), personnel time registered as Total Direct Labour Time (TDLT) and Waiting Time (WT) are included in Table 3. The reductions in percentage for HIV analysis are 97.6% in TET (Total Equipment Time), 13.6% in TDLT (Total Direct Labour Time) and 35.6% in TWT (Total Waiting Time). Considering COVT-COVIDG, the improvements achieved in time reduction were 3.7% in TET, 11.5% in TDLT and 21.7% in TWT. However, in both cases, more than 80% of TAT is due to waiting times--the main waste identified in the laboratory workflow. These waiting times are related to equipment or personnel. Waiting times in equipment are a consequence of inefficient sample distribution to equipment, bottlenecks due to peak arrival of samples between 09:30 AM and 11:30 AM, etc. Personnel waiting times are mainly due to the following factors:  
1. Lack of synchronisation in the flow between the sample incoming area and the pre-analytical COBAS p612 (F. Hoffmann-La Roche AG, Basel, Switzerland) equipment, which requires manual sample pick up and transport between the sections;
2. Need for finished sample re-racking at COBAS e801 (F. Hoffmann-La Roche AG, Basel, Switzerland) exit before re-entering the COBAS p612 (F. Hoffmann-La Roche AG, Basel, Switzerland) equipment for sample archiving;
3. Existing waiting time from the moment the result is registered in the laboratory information system until the final clinical review. The waiting times were reduced in the post-TLA scenarios for both processes, HIV and COVT-COVIDG, decreasing by 35.0% and 21.7%, respectively, but they were not completely eliminated (see Table 3). Therefore, Kaizen ideas were proposed to optimise this issue. Waiting times are higher for COVID-19 testing, although, in both cases, the main contributor is the final clinical review by the microbiologist. We performed a root cause and longitudinal analysis for the years 2021 and 2022 in terms of waiting time, concluding that the sequencing, levelling and complexity of microbiologist work, depending on the patient, inevitably led to waiting times until the result was validated. However, areas for improvement have been identified related to the laboratory information system where the doctor validates the result, simplifying the existing filtering and manual process. Likewise, one possibility to drastically reduce waiting times until clinical validation and free up resources would be to analyse the possibility of self-validation of standard test results.