The Devil’s in the Details (of the Data)

When I teach lean concepts, it’s easy to talk about the concepts of timing the process steps. Most people nod their heads and say, yes, they get it. The idea is easy – each step has a start and a stop.

But when I work with teams, it doesn’t seem to be so easy to work though the data identification step.  Here are some things that have been identified as process steps, but there’s no time collection:

  • Patient arrival in physician office
  • Time patient gets to exam room
  • Time doctor gets to exam room
  • Time patient leaves office


  • Patient arrival for procedure
  • Patient ready for procedure
  • Patient departs after procedure

It’s important to note that everyone is DOING everything very well, we just don’t have time data for the steps.

When you want to look at your process from a lean perspective, it’s difficult to analyze the process if you don’t know how long each step takes. Naturally, I often get push-back from the team: Patients don’t like to sign in, it takes too much time to track patient movement, we don’t have people who can run around with clipboards writing times down, etc.

At this point, I bring out my example from one of the departments I have worked with. They had all the same objections, but they really wanted to understand the flow of patients in their department. So, they created a flow sheet that included each high-level step, that would travel with the chart (which travelled with the patient) in which each “stop” would include a simple initial and time noted. They created an operational definition of WHO would note the time for each step, and added that onto the worksheet.

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Then they took it up one level – if there was a delay, and the cause was known, they asked if they could include that on the worksheet. They agreed to use the worksheet for tracking for one week, and then we would analyze the results.

Lo and behold, not only was the timing extremely interesting, but the delay reasons were fascinating. The delays were not where we thought they would be. And, we found that the primary obstacle was different than all of the team’s guesses, prior to data collection. The department was able to move forward with proposed changes, and they use the worksheet once a month as an audit.

Would it be great to have an automated time-tracker, like a computer system and radio-frequency tracking? Sure it would. Did the department want to wait for a systems review, IT request, and approval process to get going on their improvement? No they didn’t.

I applaud departments like that one who want to understand the current state before plunging into “quick fixes.” In this case, the improvements were more effective, and more sustainable, because they didn’t let the short-term pain of focused data collection become a barrier to moving forward.

I wonder if anyone else has interesting stories to tell about challenges in the data collection phase?

Comments 2

  1. Rob Marshall

    Hi all

    I have experienced something very similar in a financial institution where the problem was excess overtime being paid to process instructions at the end of the day. The process was very simple in that it was
    1. Receive the instruction
    2. Verify the signature,
    3. Process the instruction

    The ‘Lets fix it based on gut feel’ improvement didn’t work much to, nearly, everyone’s surprise! at which point my team was asked to take a look at the problem.

    We constructed a value stream map which instantly gave a different picture in terms of where the inventory was building up, it showed that the greatest amount of inventory was at the ‘Verify Signature’ process step at which point we created a data collection plan around what time the instruction was received, what time it was passed to signature verification and what time it was passed back to the department so that the instruction could be processed. The results were eye opening in that most signatures were waiting between 2 and 4 hours to be verified and returned to the department, the process of verifying the signature took 2 minutes.

    Using this data we sat down with the team leader from the signature authorisation department who agreed that this was not acceptable, but only because the data backed up our cause. Various improvements were put in place and reduced the turn around time of the signature verification to 20 minutes on average

  2. Yitz

    We experience similar issues all the time, there just doesn’t seem to be adequate or accurate data collection for each process step in health care .

    For example we conducted an analysis in one of our indigent clinics measuring the time it took a patient from registration to discharge. We had two issues with the data we received First while we had a system to track the time what we found was that the staff was not entering the information in a timely manner. Second data was not being collected at all the process points we would like to be tracked

    The team I was working with in this case understood the need to collect the data, and worked to ensure we had 2 weeks worth of usable data

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