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M.O.R.E. Quality™ is a new disruptive Quality Control risk management solution. It auto-designs a statistical QC process that is verified to prevent reporting an unacceptable number of medically-unreliable results.


All laboratories using the AWEsome Numbers M.O.R.E. Quality software platform, receive clear concise reports that:

  • enhance, not replace, existing statistical QC software programs
  • recommend and verify a full Daily QC process that will send a QC reject flag if the current number of medically-unreliable results (MURs) reflected by a QC sample exceeds the defined acceptable risk criteria
  • convey acceptability of risk in plain language in full sentences, not statistical jargon
  • go beyond sigma to quantify the number and cost of medically-unreliable results based your QC data (risk drivers)
  • send consistent alerts to improve the accuracy and/or precision of the analytical process
  • project the clinical and financial benefits of the M.O.R.E. quality management processes

Risk evaluation is the process of comparing the estimated risk against given risk criteria to determine the acceptability of the risk (ISO 14971).

Mathematically-OptimiZed Risk Evaluation™ applies new algorithms that include the acceptable number of Medically-Unreliable Results (MURs) per year reflected by each QC sample. Replacing traditional subjective acceptable statistical limits such as 5%, or 2 or 3 or other sigma value with the number and cost of MURs reveals opportunities to significantly reduce risk exposure and real costs.

You cannot eliminate the risk [4] of incorrect results being created. You can, however, identify unacceptable risk levels [5] and guide staff to prevent patient harm. [6] with the systematic implementation of Mathematically-OptimiZed Risk Evaluation™.

Mathematically-OptimiZed Risk Evaluation™ guides staff across multiple sites, departments and anlytical processes to pro-actively identify and minimize hazards [1] that may create failures [2] in statistical quality control processes. Laboratory systems create numerical errors. Reagents, calibrators, instruments, people and the environment change frequently, as does therefore the number of incorrect results [3] created.

M.O.R.E. Quality™ uses NEW mathematical algorithms to detect unacceptable risk of patients being harmed by results that fail medically allowable error limits.

For numerical results, the number and acceptability of incorrect results is monitored with statistical quality control processes [7] measuring stable quality control samples that behave the same as patient samples. M.O.R.E. Quality software algorithms auto-design and verify QC processes to guide staff to maintain medical goals and acceptable risk criteria [8] defined by the laboratory director.

This page and document provides a checklist of regulatory and best-practice recommendations:

Failure to:

  1. verify that the QC sample reflects patients
  2. define a locally-approved medical goal
  3. define acceptable risk criteria
  4. evaluate patient risk at monthly/regular QC review
  5. follow best practice for Daily QC design
  6. verify the ability of Daily QC processes to detect medical errors
  7. verify staff competency to mitigate patient risk


[Ref] CLSI Laboratory QualityControl based on Risk Management, Approved Guideline, CLSI Document EP23-A, Wayne PA, Clinical and Laboratory Standards Institute, 2011

  1. Hazard Potential source of harm (ISO/IEC Guide 51).
  2. Failure – In the broadest sense, a case when the system does not meet the user’s expectation;
    1. NOTE 1: This includes the inability to perform its intended functions satisfactorily or within specified performance limits;
    2. Failure event – An event, such as calibration, new reagent lot, or instrument wear, which creates unacceptable current risk.
    3. Failure mode – Manner by which a failure is observed; generally describes the way the failure occurs and its impact on equipment operation.
  3. Incorrect result – Result that does not meet the requirements for its intended medical use;
    1. NOTE 1: In the case of quantitative test procedures, a result with a failure of measurement that exceeds a limit based on medical utility;
    2. NOTE 2: (NA to statistical QC) In the case of qualitative test procedures, a result that is contrary to a true value of the measurand
  4. Risk – Combination of the probability of occurrence of harm and the severity of that harm (ISO/IEC Guide 51).
  5. Acceptable risk – A state achieved in a measuring system where all known potential events have a degree of likelihood for or a level of severity of an adverse outcome small enough such that, when balanced against all known benefits— perceived or real — patients, physicians, institutions, and society are willing to risk the consequences.
  6. Harm – Physical injury or damage to the health of people, or damage to property or the environment (ISO/IEC Guide 51)
  7. Quality Control Strategy
    – The QC strategy using QC samples should include the following for each measuring system:
    1. The frequency of QC sample test events
    2. The type and number of QC samples tested per test event
    3. The statistical QC Limits used to evaluate the results
    4. The frequency of periodic review for detecting shifts and trends
    5. The actions taken when results exceed acceptable limits
  8. Risk evaluation – process of comparing the estimated risk against given risk criteria to determine the acceptability of the risk (ISO 14971).

How do you get from here to M.O.R.E.?

  1. Browse around this site to become familiar with the concept
  2. Challenge your staff or colleagues to take surveys and compare results
  3. Join us for a free 20 minute quality chat
  4. Book an AWEsome introductory Quality Consulting Program (i-QCP)
    – a risk-free opportunity to prove to yourself that either:
    [A] your existing QC system is effective to detect medically allowable error and maintain acceptable patient risk, or
    [B] you can reduce patient risk and cost of incorrect laboratory errors by upgrading your existing statistical quality control processes to risk management.

This structured consulting program includes instructional videos, data analysis and modeling and live online report presentation with Q & A. A certified M.O.R.E. Quality Consultant will guide your QC team through the changes in both mindset and mathematics from statistical QC to Risk Management. The program includes, and requires, an enrollment in the online course “Leading the way to M.O.R.E. Quality” (4 PACE Credits) for the QC Team Leader.

At the end of the introductory Quality Consulting Program, the QC Team Leader will be able to:

  1. upload the factual Risk Drivers (QC standards, data and practice) that determine patient risk;
  2. interpret reports of risk metrics and risk drivers to evaluate:
    1. analytical process quality vs. medical goals and acceptable risk levels
    2. the ability of the QC procedures to detect medically allowable error
    3. opportunities to reduce risk exposure
  3. demonstrate the impact of choices in TEa limits from CLIA, biological variation and medical goals
  4. compare existing QC practices to best practice of risk management
  5. measure potential reduction in risk and cost of simulated failure with Mathematically-Optimized QC strategies in selected analytical and QC processes
  6. project overall potential to reduce risk exposure, standardize QC processes and improve patient care