During the measure phase, the team will:
- Observe and review the current process.
- Measure the current process to established a baseline.
The observation part is critical even for those who are involved in the process daily, since observation can often uncover errors or variations that are not obvious. Observation is also useful to eliminate bias. For example, management's perception of the workload is likely different from the perception of the employees. Observing and reviewing the process together in real-life as a team can eliminate those biases.
After the team has had a chance to observe the process, it can then decide on the appropriate data to measure. Data can be either continuous or discrete. Continuous data are data where the values can be any numeric values and not just whole numbers (eg, time, height, and temperature measurements). Discrete data contain distinct values. Discrete data are usually the result of counting something using whole numbers. Here are some examples of continuous vs. discrete data that are used in the laboratory.
Continuous
| Discrete
|
Average time from receipt to completion for stat samples
| Number of stat samples not completed within 1 hour
|
| Time between completion of stat test by the analyzer and laboratory information system (LIS) release | Number of stat samples not released in LIS within 5 minutes after the test completed by analyzer
|
Total LIS downtime
| Incidences of LIS downtime in one month
|
Average wait time for outpatients prior to specimen collection
| Number of outpatients not drawn within 15 minutes
|
Average response time for physician to return page for critical result
| The number of critical result pages not return by the physician within one hour
|
