What, Why, Where, When, Who and How
What is M.O.R.E. Quality™?
M.O.R.E. Quality™ is a new perspective and process for laboratory quality control that holds promise to reduce existing error rates by 50%.
It clearly connects QC results to the number and clinical/legal cost of patient results that are medically-unreliable. Automating QC design and validation helps laboratories save time and meet regulations. M.O.R.E. Quality brings structure and standardization throughout multiple instruments, departments or sites. It creates a common language for everyone who cares about laboratory quality.
M.O.R.E. Quality concepts and software enhance what people fundamentally know and do now. They update, not replace, statistical quality control to risk management.
“M.O.R.E.” stands for “Mathematically-OptimiZed Risk Evaluation™” – a software-driven series of proprietary algorithms that auto-evaluate risk and design a QC process that is verified to alert staff if risk is unacceptable.
M.O.R.E. Quality begins with education and understanding. Standardized training and competency programs ensure the ability of all staff to respond appropriately to risk metrics and action flags.
Mathematically-OptimiZed Risk Evaluation™ is a series of proprietary algorithms that:
1. convert specific factual risk drivers from stable QC samples
a. to risk metrics that assess the quality of laboratory methods
b. relative to locally-approved medical goals and acceptable patient risk levels
2. select, and will ultimately automate, the mathematically-optimum QC strategy for each analytical process
a. to continuously maintain acceptable risk
M.O.R.E. Quality goes beyond sigma to report the number and cost medically-unreliable. Unlike statistical QC, there are no estimates, no assumptions, no opinions; just the facts.
“M.O.R.E. Quality” means less risk.
It first and foremost just what it sounds like:
a process to achieve a greater amount of quality (more things that meet the needs of the customer.) If your customers need a product, or your doctors need a test result, that meets [these specifications], then more of the products/results you produce will meet those standards. When lab results fail to meet the needs of the clinicians, patients are exposed to risk of delayed or incorrect diagnosis or treatment.
The concepts, mathematics and software have evolved over many years.
NOTE: Mathematically-OptimiZed Risk Evaluation™ applies only to methods that: [A] create numerical results, and [B] require stable sample/surrogate/external QC samples.
Everybody is a risk assessor. Risk Assessment is the first specific fundamental skill required for everyone on the ‘Tree of M.O.R.E. Quality.” Whether you are at the lab bench examining daily QC results to make the real-time decision to release or delay patient results, or the coordinator of a regional program evaluating 1,000 analytical processes across a dozen labs, you need to answer ONE question: “Is risk acceptable?”
For many patients, physicians, administrators, funding agencies, all you need is a Pass/Fail assessment – and enough understanding to evaluate the fundamental concepts and software that drive that decision. Risk Graphics and Risk Metrics help you make that decision with confidence.
Risk Controllers are the front line laboratory staff, supervisors and managers who follow the laboratory’s policies, processes and procedures to create and evaluate QC sample and patient results. (My personal heroes.) Their actions are guided by the mathematical algorithms in software and competency level with the analytical and QC processes.
Risk Managers use verified processes to create risk metrics to guide the QC process and quantify the number and cost of patients who are currently, or potentially, at risk of medically-unreliable results (MURs). They interpret risk metrics and create procedures to continuously maintain acceptable risk.
Risk Masters collaborate to understand the logic and math required to provide the education and tools to effectively implement risk management across multiple sites and regions. They create and verify processes to manage acceptable risk levels in the realm of 1 error/year as a stable operating level, with QC processes verified to detect 1 error per day.
The laboratory director manages the team and sets the strategy.
“It is your responsibility to ensure that your laboratory develops and uses a quality system approach to laboratory testing that provides accurate and reliable patient test results.” (CLIA) Incorrect result – result that does not meet the requirements for its intended medical use; NOTE 1: In the case of quantitative test procedures, a result with a failure of measurement that exceeds a limit based on medical utility; (CLIA EP 23-A)
- Understand the process thoroughly.
- Verify Risk Assessor competency
- Set medical goals and acceptable risk levels for each QC sample
- Verify staff competency or automated entry of 13 current factual Risk Drivers every 2-4 weeks.
Just the facts
- Act on action flags to update the QC process, improve accuracy or precision
- Reduce patient risk!
- Save time! Meet Regulations! Clearly, Simply, Practically
- Based on studies to date, M.O.R.E. Quality™ promises to reduce laboratory error by 50%, preventing harm to millions of people and saving billions in healthcare dollars.
For the first time – now you need to QC the QC! The principles of risk management bring fundamental change to the process of laboratory quality control. For the first time, laboratories will be required to prove that their QC processes will detect and prevent clinically significant errors.
M.O.R.E. Quality let’s you:
Report FEWER errors
Upgrade your Statistical QC to Verified Risk Management
Review hundreds or thousands of methods in minutes
Auto identify methods that fail quality goals (e.g., TEa limits) or need improvement
Auto direct trouble shooting
Auto-design and VERIFY effective QC Processes
"At the least, the ability of the QC procedures to detect medically allowable error should be evaluated." CLSI EP23-A
Monthly Review: QC summary statistics and the QC process effectiveness are typically checked every 2-4 weeks to verify analytical process quality and QC process effectiveness.
Once a laboratory sets locally-approved medical goals and acceptable risk levels by clinical setting, M.O.R.E. Quality Software can auto-design and verify QC processes (for the majority of laboratory methods) to alert staff to stop if a QC sample reflects patient risk of 1 error/year at monthly review. This replaces the statistical assumption of 5% acceptable risk from the 1970’s and manufacturing assumptions of acceptable 2 or 3 sigma levels from the 1980’s.
The pie charts show current risk levels of less than 1% in two different groups of laboratories. The upper group used an early precursor of M.O.R.E. Quality to compare Total Error to Total Allowable Error monthly.
When you converting sigma at monthly review to percent errors, one must question why laboratories ‘allow’ thousands of errors /year while the vast majority of methods are capable of producing less than one error per year.
At monthly review, IF current risk is acceptable, software recommends a Mathematically-OptimiZed QC strategy that is verified to meet the defined acceptable risk level. Between regular reviews, use the Mathematically-OptimiZed QC process to detect if the error rate rises to the acceptable risk level, e.g., 1 error/day.
While unexpected changes of laboratory methods are rare, they certainly do occur. Every penny and second spent on quality control is intended to detect these errors.
When challenged with a simulated failure, most QC processes FAILED to detect simulated failure in one QC run.
Wherever you are evaluating quality of numerical results. Risk-Based Quality Report Cards™ condenses hundreds or thousands of QC sample results to a bar chart of five Risk-Based Quality Grades™.
You can create charts by department, instrument, analyte, laboratory, region. Methods that fail or require quality improvement are easily identified.
Risk-Based Quality (R-B-Q) Report Card – A bar chart of many analytical processes that enables evaluation of acceptable risk by analyte, laboratory, region, instrument model, etc..