Discussions started by Zoe Brooks

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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 http://rmw.awesomenumbers.org/scientificadvisoryboard 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 http://rmw.awesomenumbers.org/scientificadvisoryboard

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.
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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 http://rmw.awesomenumbers.org/scientificadvisoryboard

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Aug. 15, 2018   Zoe Brooks

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Every Number Needs ONE

 

Yesterday I challenged you to:  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.

Today’s exercise is to:  Select a monthly summary QC report, from your Peer QC provider or your own internal spreadsheets.   Now ask a colleague (or look in the mirror) and comment below.  Look at the Bias, Total Error, Sigma Value, SDI and CVI.   Do you really know what they mean?  If you asked 10 people, how many gave the same answer?

Before you begin to assess a new method or examine QC data, you should be able to state for each statistical indicator: If it is below 'x' I will think or do 'this' and "if it is above 'y' I will think or do 'that'."

Laboratory professionals currently 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 http://rmw.awesomenumbers.org/scientificadvisoryboard

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Zoe Brooks (with Tennyson)
by Zoe Brooks - Wednesday, 15 August 2018, 9:02 AM
 

There is only ONE truth

 

There are many statistical processes to estimate bias or calculate sigma, but there is only ONE truth in the patient population.  What do you think?  What do you do?  What is the best way to measure the truth? 

It makes sense that if PATIENT samples produce the same results on four different instruments, then those instruments have the same bias, the same sigma value, and require the same quality control process to detect failure of acceptable risk criteria.

It does NOT make sense that each laboratory professional is free to choose different QC samples with no obligation to prove that they reflect patients, and then calculate bias as variation between the current measured mean and 

[A] the peer group mean,
[B] the package insert mean, or
[C] the lab’s own historical mean or
[D] none of the above:  don’t calculate bias at all - estimate it from any proficiency program using any samples at any time.

Aug. 14, 2018.   Zoe Brooks 

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Take poll below and please discuss.

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