Staffing to Workload in Phlebotomy Areas: Direct Effort

By Mike Baisch • March 1, 2018

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In the previous series of posts regarding staffing to workload, we discussed the details of how to perform a staffing-to-workload analysis using the generic background of a small outpatient clinic lab. The intent of those posts was to establish a base knowledge of the three components of a staffing-to-workload plan, how to gather the information need to calculate each component’s staffing needs, and how to display that information.

This next series of posts will deal with scenarios that may not always fall into the “standard model” for calculating a staffing plan. The first of these scenarios addresses one of the laboratory service areas that have direct patient contact: phlebotomy.

Direct Effort

In a phlebotomy setting, direct effort is defined by the time spent with a patient or patient sample. These tasks typically include performing a blood draw, handing out collection containers, rooming patients for procedures, urine drug-testing collections, and specimen processing. As always, direct observation of the phlebotomy area and discussions with the phlebotomy staff will clearly define those tasks. With the direct-effort tasks defined, we now need to know how long it takes each time we perform a task and how often we do them.

Direct-Effort Volume

While the testing laboratories tend to work in "volume of tests" or "volume to specimens," phlebotomy’s primary unit of volume is patients. Additionally, because phlebotomy has direct patient contact, there is also a difference in the staff's turnaround-time expectations. In the outpatient setting, what is the maximum wait time for patients after they present for the phlebotomy service? In the inpatient setting, what is the maximum wait time for a collection after the physician has requested it (assuming it is not a timed collection or a stat collection—both of which will be addressed later)? This additional requirement changes how the patient volumes need to be collected and organized. For example, if we assume a maximum of a 15-minute patient wait in the outpatient setting, then we should collect and display our data in 15-minute intervals.

The next step is finding the patient data. Some of the software systems used in performing phlebotomy collections can be queried for this information. There are three ideal components to a report generated out of a phlebotomy system:

• Date/Time of the service is the key to understanding at what point in the defined task the date/time stamp represents. Is it when patients present, when they are drawn, or when they are dismissed? This understanding will be needed later when calculating the direct effort.
• Type of service the patient received (blood draw, container hand-out, rooming for other service, etc.). This information is phlebotomy’s version of a test mix. Some software will even record the type of collection performed (PIC line, arterial, pediatric, etc.) or the container type that was provided (urine, stool, sputum, etc.).
• Size of service is helpful later when performing the task timings. This helps us set up a validated timing plan to make sure we capture patient draws that cover the range, paying particular attention to the collections that fall into our range of the most common number of containers, thus, not letting large or small draws unduly weight our timings and direct-effort calculations.

The table below is an example of the phlebotomy system report:

The most critical component of the report is the date/time stamp of the patient service. It provides us with our actual patient arrival pattern, which is the primary driver for the direct-effort staffing levels for phlebotomy services. The service type and the draw size can be derived through process observation if not available from the phlebotomy system.

Once the raw data report has been generated, it needs to be summarized into a table that displays the patient arrival patterns by day of week and time of day. The table below is an example of what a data summary could look like. It shows what an average Tuesday would look like for an outpatient draw area between 8 a.m. and 10 a.m. (Between 8 a.m. and 8:14 a.m., three patients presented. Between 8:15 a.m. and 8:29 a.m., 5 patients presented.)

I should note that you will want to base your averages on at least six weeks of information, though more is always better. Be mindful to watch for holidays, as they can significantly affect the averages for the day of the holiday and the days before and after the holiday.

Direct-Effort Timings

Start by observing a few phlebotomists performing the task in question to gain a thorough understanding of when the phlebotomist begins the task and at what point he/she would be ready to begin the task again. This allows for a single event to define both the beginning and the end of a process. Choosing separate events to define the beginning and the end of a task carries the risk of missing staff effort and underestimating staffing needs.

To demonstrate this logic, let’s focus on the task of performing an outpatient phlebotomy draw. In the diagram below, there are three different task durations defined:

• Blue defines the duration from when the phlebotomist greets a patient at the desk until the phlebotomist is available to greet another patient at the desk.
• Grey defines the duration from when the phlebotomist greets the patient at the desk until the patient is dismissed from the draw room.
• Red defines the duration from when the phlebotomist rooms the patient until the patient is dismissed.

The phlebotomist is represented by the figure in green, while the patient is represented by the figure in purple.

So, what does this all mean? If we choose to set the draw-task duration using the red definition, each phlebotomist could see up to 15 patients in one hour. If we choose the blue definition, each phlebotomist could see up to 10 patients in one hour.

The difference between the two durations is 5 patients per hour, or between 30 to 40 patients per phlebotomist each day. This means our patients will be waiting longer than we want, and our staff will be rushed and stressed. By using a single point to define the beginning and end of the process, a phlebotomist is ready to greet a patient, and we can capture all the time involved in an outpatient blood draw.

Direct-Effort Calculations

We now need to combine our volume report and our task timings to calculate our direct-effort staffing needs. For example, we will assume an outpatient phlebotomy service that performs three direct tasks:

• Blood draws that take 6 minutes per patient.
• Container issuing that takes 4 minutes per patient (includes collection instructions).
• Container returning that takes 2 minutes per patient.

The table below displays an average Tuesday for the outpatient phlebotomy area, with the left side showing the patient volume and the right side showing the phlebotomy effort needed to complete the patient volume tasks (Volume × Timing). Whenever the total direct effort time in a 15-minutes period exceeds 15 minutes, it indicates more than one phlebotomist is needed. To convert the total direct-effort time into staff needs, divide the total direct-effort time by 15, as each segment is broken down into 15-minute intervals.

For a better visualization, the headcount is displayed below in a line chart. This helps us to understand the peaks and valleys in phlebotomy service demands:

These calculations would then be repeated for the remaining days of the week.

Summary

In phlebotomy, the direct-effort staffing needs are defined by the arrival of the patients for phlebotomy services. As the service level expectations of phlebotomy require a quick turnaround, we see a lot of peaks and valleys in the staffing-level needs. This leads to a staffing question about staffing to the peaks, the valleys, or somewhere in-between. However, before making those decisions, we need to consider what the indirect-tasks effort will be and what the other operational needs will be. Those components of a staffing-to-workload analysis may help us to fill in the valleys in the direct-effort plan and better inform the decision on the appropriate staffing levels. The next blog post will continue with the analysis by outlining indirect-effort tasks in a phlebotomy work unit.

Mike Baisch

Mike Baisch is a Systems Engineer within the Department of Laboratory Medicine and Pathology at Mayo Clinic. He partners with the various testing laboratories on their quality and process improvement projects including staffing to workload, root cause analysis, and standard work & training. Mike has worked at Mayo Clinic since 2005 and holds a degree in Industrial Engineering. Outside of work, Mike enjoys cooking and sampling cuisine from around the world, home brewing, and gardening.