Scalability Failure Case Study: COVID-19 Test

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By day, I coach SaaS founder CEOs on how to scale. By night, I volunteer as an emergency worker in the Seattle metro area. Normally, those two worlds do not intersect, but they did in 2020 with COVID-19.

COVID-19 is many things, including one of the biggest scalability challenges in medical history. There are lessons to be learned here that are absolutely transferable to other domains, including SaaS.

A few days ago, The Washington Post came out with an article detailing the behind-the-scenes happenings at the United States CDC (Centers for Disease Control). The article focused on what happened in Q1, 2020.

Why did the U.S. take 46 days to get a working COVID-19 test when the WHO (World Health Organization) had published the specifications for a test on day one? Why did Thailand have a working test within a day or two of the WHO spec being published, but the CDC took 46 days?

I’ll start by sharing the facts of the situation (as reported by The Washington Post) and then my take on those facts from a scalability perspective.

  1. The genetic code for COVID-19 was posted online by Chinese public health officials on January 12, 2020.
  2. The WHO published their “open source” specification for a COVID-19 test on January 13, 2020.
  3. The CDC declined to use the test specified by the WHO (the majority of the rest of the world used it).
  4. The CDC wanted to develop a more sophisticated test that could detect not just COVID-19 but other viruses in the family (such as SARS) and future mutations. The thinking was that this would be a superior test, better able to discern COVID-19 from other respiratory illnesses and able to catch mutations early.
  5. The initial CDC tests were not manufactured at a pharmaceutical-grade production facility.
  6. The initial tests were manufactured in a CDC research lab by the team that created the test.
  7. Initial tests failed in the field (tests were run against a provided “negative sample”… the tests showed positive when they weren’t supposed to).
  8. The “negative sample” was contaminated with the simulated virus (thus, it was inadvertently a “positive sample”).
    * I ran this last one by a friend who’s a microbiologist. He was stunned at the incompetence. It’s the equivalent of an infectious disease doctor not washing their hands between seeing patients.
  9. The part of the test that failed was the part that detected SARS and potential future mutations of the COVID-19 virus. The COVID-19 part worked just fine.
  10. The CDC got its first test to work in the field on February 28, 2020 (46 days after the WHO and the rest of the world).
  11. The FDA (Food and Drug Administration) agreed to allow hospitals to create their own tests (including using the WHO spec) on February 29, 2020 (47 days after the WHO test was open sourced).
  12. Many of those in charge at the CDC had no experience in creating virus tests and relied on the expertise of subordinates.
  13. The head of the COVID-19 test creation team had no experience in designing coronavirus tests.

My Analysis

In my view, there were three key failures:

  1. Wrong Perspective
  2. Wrong Objective
  3. Wrong Team

Let me explain.

1. Wrong Perspective

One of my clients used to describe leadership decision-making as follows:

See
Think
Act

How you “see” a situation, influences what you “think” about the situation, which in turn influences how you decide to “act.”

The first big failure at the CDC was a failure to “see” the COVID-19 pandemic as a “scalability race” problem.

One competitor in the race is the virus. The virus is trying to see how fast it can spread, infect, and kill people.

The other competitor is the CDC in trying to see how fast it can detect people with the virus, isolate them from people without the virus, and prevent infections and death.

The unanswered question in January 2020 was: Which “competitor” can scale faster?

In a scalability race against a virus, there are two milestones.

“Time to market” and “time to scale.” Of these two, the time to scale was the more important one in this instance.

The peculiar thing is that neither milestone seemed to be the focal point for the CDC’s efforts.

2. Wrong Objective

The CDC’s strategy seemed to imply a goal of maintaining proprietary control of the COVID-19 test and creating a test that was superior to the one that the rest of the world was using.

In my opinion, this was the wrong objective.

If the time to market was the objective, the CDC would have allowed and encouraged medical providers to use the WHO open-source test. This would have been the fastest time-to-market option.

Instead, hospitals were prohibited from using the WHO open-source test and from creating their own tests based on the virus genome sequencing which was published openly online.

Had the CDC team been focused on the time to scale, they could have taken the WHO test, validated it, and quickly licensed it to a dozen independent pharmaceutical companies to manufacture the tests concurrently.

(This is what South Korea did… their CDC quickly approved the simplest test possible, and they immediately outsourced to four to five companies for mass production. South Korea had been unprepared before, and they lost lives in both the SARS and MERS — Middle East respiratory syndrome — epidemics.)

3. Wrong Team

If the CDC had seen the COVID-19 pandemic as a scalability challenge, they would have had supply chain and scalability experts as part of the COVID-19 test working group.

Someone with supply chain scalability expertise would ask questions like:

  • Can this test scale, in terms of manufacturing?
  • Will there be a shortage of reagents?
  • What is the high vs. low estimate on the number of tests needed under a variety of scenarios?
  • Does in-house manufacturing work across the range of scenarios?
  • Does requiring an extra reagent or two to detect COVID-19 mutations make sense?
  • How many patient samples can we process in-house?
  • What is our daily processing throughput limit?
  • What is our plan to scale the processing of inbound patient samples?
  • What are our system bottlenecks and constraints?
  • Where can we build flexible capacity into the system?
  • Which steps have hard constraints that we need to optimize around?
  • Do we have the staffing to scale up on test manufacturing?
  • Do we have the equipment to scale up on test manufacturing?
  • Do we have the staffing to scale up on processing patient samples?
  • Do we have the equipment to scale up on processing patient samples?
  • What are all the components of the test?
  • What is the order lead time to obtain more supplies of the test components?
  • Has anyone called our suppliers to see what their maximum production capacity is currently?
  • Do we need additional suppliers?
  • What are the transport and storage requirements for patient samples?
  • If there is a temperature control requirement, do we have enough refrigerator capacity?
  • What supplies do we need to process patient samples?
  • Which manufacturers do those come from?
  • What are the lead times on ordering first and additional production runs?
  • Should we place orders now as a hedge in case we are at the high end of the range for forecasted test demand?
  • What is our worst-case scenario, and can we handle it?

I can’t find any publicly available evidence that these questions were thoughtfully considered by CDC leadership.

The head of the CDC commented publicly that in regards to COVID-19 testing in early 2020, he relied on the expertise of his subordinates. In my view, this is an excuse.

All organization leaders and CEOs must rely on their subordinates. Ideally, a CEO’s direct reports are all more experienced in their respective functional and technical areas than the CEO.

However, there is a difference between delegating work versus abdicating bottom-line responsibility.

When you run an organization, if something goes wrong, it may not be your fault, but it damn well sure is your responsibility.

There is one primary role a CEO must fill, even when leading a team of subordinates that all have more expertise:

Ask tough questions.

The three tough questions that were not asked effectively by the head of the CDC were:

  1. Are we seeing this situation in the right way?
  2. Are we focused on the right goal?
  3. Do we have the right people involved?

When you look at your market opportunity, are you asking the same tough questions of yourself and your team?

When you look at product development, are you hyper-focused on creating a better app than your competitors or a better outcome for your customers? The two are not the same thing. Which perspective makes more sense at the current stage of the market?

Are you focused on the right goal? Should you focus on getting the right product/market fit? The right sales message? The right scaling strategy? There is a right time and place for everything. Which goal makes sense for the current time and place?

Successfully achieving the wrong goal gets you nowhere.

Given your goal, do you have the right people involved? As CEO, you can’t be an expert in everything. What you must be an expert in is knowing the limits of your own skills, knowledge, and abilities, and augmenting that with the expertise of others.

If the head of the CDC is weak in manufacturing and lab-capacity scalability, that’s okay. But, he should have the self-awareness to know his own limits and get someone on the team who does know those areas.

Are you making the same mistake? What are your weak areas? Do you recruit others to compensate for your weaknesses? If not, that’s a mistake.

All learning comes from making mistakes. However, it’s far cheaper to learn from someone else’s mistakes than your own.

P.S. Click Here to read the Washington Post article I referenced earlier.

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