Posts made by Zoe Brooks

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Another interesting approach:

Skeptics about the FAIR model love to scoff at quantitative risk analysis and dismiss it as mere “guesswork.” I have encountered this assertion several times while conducting analyses and I welcome the challenge each time; I view it as an invitation to a discussion.

Generally, it is during the data collection or the results phase of the risk analysis process that the buzzwords “guesswork” or “guessing” are voiced. The conversation often unfolds somewhere along these lines: 

“This is all just guesswork.” – Skeptic 

“Hmm, that is a FAIR concern. Let’s step back a moment and reflect on the process... We’ve engaged XYZ  Subject Matter Experts (SMEs) to ask how often threats are trying to harm asset A, and evaluated the controls around asset A to determine how likely they will be able to overcome the controls and successfully cause harm. Each range is supported by a rationale that documents our assumptions as well as any industry references, if applicable. Do you think that was a beneficial exercise?” – Believer (a.k.a. Me)

“Yes, but… it is still just guessing.” – Skeptic

“OK. Let me ask a similar question. Have you ever engaged XYZ business SMEs to gather estimates for lost revenue, the number of people who would be involved in responding to an event, how long they would spend responding, crisis communication costs, etc.?” – Me 

“No.” – Skeptic

“Wait. Then how are you selecting a risk rating now?” – Me  


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I am impressed!  I dug deeply into this many years ago, but just skimmed this one.

Do you agree that this fundamental understanding of distribution is critical as we get into the true mathematics of how to manipulate data into meaningful information?

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Put True and Acceptable Risk in Policy

Laboratory directors globally are striving to meet the criteria of ISO 15189-2012 and CLSI EP 23A.   Join the Scientific Advisory Board to follow the first ‘trailblazers’ through the process of Mathematically-OptimiZed Risk Evaluation©.  Validate your own QC processes and lead others to improve patient care and save clinical cost.  The other benefit is that setting these five values and adopting these processes will make your life SO MUCH easier and let you sleep well at night!  

The steps you need to follow are spelled out remarkably clearly.  The goal is “automated selection and reporting of results process by which patient examination results are sent to the laboratory information system and compared with laboratory-defined acceptance criteria, and in which results that fall within the defined criteria are automatically included in patient report formats without any additional intervention.”

When the QC sample is proven to reflect patient samples, and the medical director sets these five values, the required facts can be gathered from the instrument or LIS and the entire QC process automated as required.  Beware however … you cannot manage cost with sigma.

When you join, you will receive a series of emails and be enrolled in a free online course.  Our sole objective is to improve patient care.

Aug. 19, 2018

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Opinion or Fact

Risk management uses different numbers and different logic than traditional statistical quality control. In Mathematically-OptimiZed Risk Evaluation©, there are no estimates or assumptions – just Opinions and Facts. 

The only OPINIONS that matter are those of the Medical Director (in collaboration with Patients, Institutions, Physicians and Society.) His/her medical and financial opinions go into the policy manual.

FACTS are measured and can be independently verified. Each fact has its own unique “What, Why, Where, Who, When and How.”

These Opinions and Facts can be mathematically evaluated using new risk management algorithms to automate continuous ability of methods and staff to meet defined acceptable risk criteria. You can also monitor existing cost of error and cost of error if the method fails.

Join the Scientific Advisory Board to learn M.O.R.E. and evaluate this new process

Are you a teacher? Talk to me in the fall about how this simplifies understanding!   It takes 9 verifiable 'risk drivers' to create the 'risk metrics' and action flags that effectively meet acceptable risk criteria; sigma is calculated from 3 estimates.

Aug 17, 2018

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A Number is s thing OF AWE

This simple ‘touchstone of truth’ about numbers revolutionized how I was able to get students in the Clinical Laboratory Data Analysis course to understand the meaning and interrelationships of the numbers that drive and represent patient risk. 

Try this:  Select a QC chart and ask a selection of staff or colleagues to tell you the What, Why, Where, When Who and How of the values on the chart and QC rules applied.

1. Every number is "one" .... one single piece of information.

Every number:

  • conveys one unique piece of information (What it is)
  • comes from one specific place (e.g. 1 instrument within 1 laboratory) (Where it was created)
  • at a specific time or over a defined time period (When)
  • by an identifiable person or persons (Who)
  • following a test procedure (How)


Laboratory professionals are currently taught differing theories of the meaning of fundamental concepts.  They look at the same number and words, but reach vastly different conclusions and take different action. Let's face that truth - and change it!

Join the Scientific Advisory Board


Aug. 15, 2018   Zoe Brooks

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