Winter Chats. #FixTheSix Human Hazards of Statistical QC
Join our panel for 30 snappy interactive minutes to discover how fixing six common human misunderstandings of statistical QC can reduce the risk of medically-unreliable results reported by up to, or more than, 50%!
Model the impact of in-lab choices. Turn Risk Drivers to Risk Metrics, Evaluations, Action advice and Full QC Design
Recent publications from ISO, CLIA, CLSI EP 23-A, CMS and others describe general guidelines for Risk Management. They also describe specific steps required to ensure acceptable patient risk for numerical laboratory results that are controlled by statistical quality control practices. These steps require subtle yet fundamental changes in existing mathematical algorithms of statistical QC.
January 28th. Hazard #1 is failing to define a locally-approved medical goal for QC.
It makes sense, and CLSI’s Guideline EP23-A clearly states that laboratory tests that are reported as numbers should be controlled to meet medical goals. Without medical goals, what are we aiming at?
Does it make sense that one laboratory will choose a quality standard that allows 4x the variation from the true value as the lab next door – based solely on the personal opinion of various individuals within the laboratory?
Take the survey to the right —>
Feb 2nd. Hazard #2 is failing to set the acceptable risk level.
This is the really NEW part of risk management. How many patients is it acceptable to harm? What is the acceptable level of clinical and legal cost.
Acceptable risk is NOT a statistical calculation, but rather the number and cost of patient results that is acceptable to ‘The PIPS’ (Patients, Institutions, Physicians and Society.)
Does it make sense that medical laboratories choose the acceptable number of results that fail quality standards before they are concerned – based on statistical or manufacturing norms – without consideration of clinical need or cost?
Current statistical standards encourage limits of 5% allowable error, or 2 sigma (2.275%) or 3 sigma (0.135%). Many laboratory methods are capable of maintaining error rates of 1 error/year (Medically-Unreliable Result or MUR) on a routine basis.
Mathematically-OptimiZed Risk Evaluation™ uses NEW statistical algorithms that set acceptable risk as the number and cost of patient errors.
Take the survey to the right —>
Feb 7th. Hazard #3 is failing to evaluate the ability of analytical processes to meet locally-defined quality standards.
Risk evaluation is a mathematical calculation. The probability of a QC result exceeding the medical goal can be calculated as a z-value (the original sigma) based on the mean (average) and standard deviation of results from each QC sample for a specific time period.
Once the laboratory director, with local physicians, sets medical goals and acceptable risk levels for each QC sample, the rest is pure math and logic – the stuff that computers do far better than human beings.
This Winter Chat invites you submit data and evaluate risk. See the difference between traditional statistical quality control and the new process of Mathematically-OptimiZed Risk Evaluation™.
Feb 9th. Hazard #4 is failing to proactively design Daily QC processes that will alert staff to act if risk becomes unacceptable.
Mathematically-OptimiZed daily QC processes can be designed to detect 1 MUR/day, when 5% of 1,000 samples would allow 50 errors before signalling staff to stop.
Feb 14th. Hazard #5 is failing to evaluate the QC process! This is the other NEW part of risk management).
M.O.R.E. Quality™ software simulates a shift that would create an unacceptable number of errors – based on your patient volume, your medical goal and your acceptable risk level. It is remarkably easy – after a short training or retraining program.
Feb 16th. Hazard #6 is failing to verify staff competency to manage patient risk by following action flags and recommendations.
The most important test you can perform in medical laboratory quality management is the DIMS Test “Does It Make Sense?
Every step of Risk Management makes sense.
Mathematically-OptimiZed Risk Evaluation™ has the potential to revolutionize the practice of medical laboratory quality control and cut the number of errors your lab reports in half. This webinar series shows how.
Measure the OUTCOME in patient risk, clinical cost and laboratory efficiency by following the specific steps in CLSI EP 23-A to:
- Define a locally-approved medical goal
- Define acceptable risk criteria
- Evaluate patient risk at monthly/regular QC review
- Proactively design and verify daily QC design
- Verify that Daily QC processes detect medical errors
- Verify staff competency to mitigate patient risk
Feb 21st Overview with Q & A. Prepare abstracts for AACC Poster(s)