Risk Management asks and answers FIVE critical questions.

1. How would error in this test harm patient?
   
   • What would it cost the patient and healthcare system for unnecessary repeat lab tests and perhaps more expensive, invasive and harmful for erroneously ‘high’ results – or, heaven forbid, for delayed, missed or incorrect patient diagnosis with related patient harm
   • How can you answer that question?  While it is difficult, there is a model established in “The impact of calibration error in medical decision making” available from: https://www.nist.gov/system/files/documents/director/planning/report04-1.pdf.  The authors correlated the reported calcium value to the tests and procedures ordered by clinicians with their costs by examining 89,000 patient charts from the Mayo. Looking only at erroneously high results, they determined that “an analytical bias of 0.5 mg/dL, which was the approximate upper bound identified during interviews, the potential health care cost increase ranged from $34 to $89 per patient having a calcium test.”
2. How much error is allowed? 
   • This the one question that both systems rely on.  However, some practitioners of statistical QC omit this question and examine only QC charts.
3. With patients/yr of _________,  how many errors are acceptable?  Risk management expresses this as Medically Incorrect Results (MIRs) per year or per failure event.
4. Is risk acceptable with current accuracy  and precision?
   • Lab professionals using RiskGATOR™ software typically demand and attain a clinical standard of 1 MIR.   
   • Statistical QC calculates sigma metrics or Total Error.
     1. Sigma metrics do not change with patient volume.
     2. It is unrealistic to express error rates per million patient results.
     3. Total Error assumes acceptable error rates of 2.5% or 5% or 10%.
     4. Both these metrics usually allow more than one MIR/year and do not change with clinical significance of the analyte or patient volume.
5. Would Daily QC alert staff if risk failed?
   • RiskGATOR™ simulates a shift in the mean to fail the acceptable risk standard, typically one MIR per failure event.  It demonstrates how the popular practice of expanding chart SD values inflates the actual QC rules applied. Studies show that more than 30% of QC in practice would never detect failure.
   • Statistical QC assumes that current measured mean and SD values are applied to QC charts and that appropriate QC rules would detect failure.
   • This ADLM award winning poster “Impact of Seven Incremental Scenarios of QC Strategies” demonstrates that merely selecting Westgard QC rules and sigma frequency does little to reduce the number of patients exposed to risk. RiskGATOR™ single rules are more effective than Westgard rules.

There are a limited number of opportunities to become a beta tester for RiskGATOR™ software.  Apply here.

We are planning a Risk Management MasterClass.  Register now to receive dates and specifics.  

Please return to the Linkedin post to add your comments and questions!

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