Topic outline

  • Online Interactive Workshop - Feb/Mar 2019

    This is your opportunity to be the first to discover the clinical savings and healthcare improvements possible with M.O.R.E. Quality!  Use NEXT GENERATION technology in medical laboratory quality/risk management to project clinical savings throughout the health care system - a test case to study HbA1c

    URL: 1
  • Best Practice Checklist - Risk Management in Lab QC

    Take Survey #1 on Best Practice for Risk Management in Statistical QC. This survey presents 18 clear specific recommendations from ISO, CLSI & CLIA.

    Take Survey #2 that asks "Does It Make Sense to manage risk [this way]? Survey 2 introduces you to the "DIMS" test (Does It Make Sense?). Unlike statistics, the logic and concepts of risk management make sense and are easy to teach and implement consistently. Try it! Look at QC differently!

    Files: 4
  • ASCLS Poster: Acceptability of Current Risk

    ASCLS Poster:  Acceptability of Current Patient Risk

    Poster ASCLS 2016 Acceptability of Current Risk

    Location & Schedule
    All posters were displayed at the Pennsylvania Convention Center  in the same location as the poster sessions of the American Association for Clinical Chemistry (AACC). The schedule for display of your poster will be as follows:  Tuesday, August 2 10:00 am – 4:30 pm; Wednesday, August 3 10:00 am – 4:30 pm

    Zoe Brooks, ART1; Kim Przekop, MBA, MLS(ASCP)CM2; Rania Mohamed El Sharkawy, MD3; Eman El Hadidi,  MD4; Eman Elattar, MD5; Noha S Kandil, MD6; Eman Shaheen, PhD7; Omneya Ahmed Ibrahim, MSc8; Basma Wagdy,  MSc9, Eman Farrag, MSc10 

    1,2 AWEsome Numbers Inc., Ontario, Canada; 3,5,6,8, Medical Research Institute, Alexandria University, Alexandria, Egypt; 4 Ein Shams University, Alexandria, Egypt; 7 Alexandria University, Alexandria, Egypt; 9,10 Private laboratory, Alexandria, Egypt

    Clinical & Laboratory Standards Institute’s (CLSI) Guideline EP23A states that risk is acceptable when it is "small enough such that patients, physicians, institutions, and society are willing to risk the consequences.”

    This study was designed:

    1) To evaluate acceptability of risk in four laboratories for urea, alkaline transaminase (ALT), and alkaline phosphatase (ALP);

    2) to identify the predominant source of analytical faults; and

    3) to compare the effectiveness of existing quality control (QC) processes to those recommended ‘Mathematically-OptimiZed Risk Evaluation™.’

    Each laboratory set total allowable error (TEa) limits according to common practice. We set the acceptable current risk level at 2.275% to reflect the practice of considering a 2-sigma method acceptable. We calculated current risk as the percent, number, and existing cost of medically-unreliable results (MURs) from sigma values for each QC sample based on verified QC data and patient test volumes. We assigned the average cost of errors reported at $10US (90LE) per /MUR. We applied the process of Mathematically-OptimiZed Risk Evaluation™ to assess the acceptability of risk, identify faults and advise action. 

    Eighteen of the 24 QC samples had error rates less than 2.275%; 10 samples reflected methods currently producing <1 medically-unreliable result /year; and 6 samples failed the TEa limits set.   

    Selected TEa limits varied from 11.7% to 30% for ALP. This practice is incongruous with recommendations to set medical goals. 

    Evaluation of risk and recommended action varied between laboratories and between analytes.  Faults creating imprecision were more common than those causing inaccuracy.

    We concluded that the calculated risk level is generally not accepted in healthcare. Adjustment of these procedures is mandatory.

    Mathematically-OptimiZed Risk EvaluationTM detects laboratory risks that are missed by commonly used statistical QC.

    File: 1Forum: 1
  • ASCLS Poster: M.O.R.E. Quality

    ASCLS Poster 2016.   Mathematically-OptimiZed Risk Evaluation™

    Let's discuss this ASCLS poster.  
    Be among the first to see new risk metrics that bring an entirely new perspective to lab QC. 

    Link below to view full poster.

    Poster ASCLS 2016 M.O.R.E. Quality

    Location & Schedule:  Annual Meeting poster sessions will be held in the same location as the poster sessions of the American Association for Clinical Chemistry (AACC). All posters will be displayed at the Pennsylvania Convention Center in the Terrace Ballroom located on the 400 level in the same location as the poster sessions of the American Association for Clinical Chemistry (AACC). The schedule for display of your poster will be as follows:  Tuesday, August 2 10:00 am – 4:30 pm; Wednesday, August 3 10:00 am – 4:30 pm

    Mathematically-OptimiZed Risk EvaluationTM

    Kim A. Przekop MBA MLS(ASCP)CM

    Zoe C. Brooks ART

    Laboratories have used quality control (QC) concepts and theories based on the same statistical calculations and assumptions for decades. Risk management, as stated in Clinical & Laboratory Standards Institute’s (CLSI) EP23A Guideline, adds an 'acceptable risk criteria.'  Now there is a way to comply with EP23A and also save time, reduce risk to patients, and diminish analytical lab errors and their costs. The Mathematically-OptimiZed Risk Evaluation™ (M.O.R.E.) method enhances existing QC concepts, while risk metrics unveil a wealth of new understanding - just 'beyond sigma.' M.O.R.E. is an Excel-based software that can consistently evaluate QC results and propel the QC process to meet locally-defined quality standards. 

    The M.O.R.E. method begins with basic QC values: target and current mean, the QC chart mean, target and current SD, frequency of QC runs, and any QC rules applied. Then, the medical director and/or clinicians sets medical goals and acceptable risk levels for quantitative analytes, while the administrative director sets costs/test and the average cost of harm to the patient if a medically-unreliable result (MUR) is released from the laboratory for those analytes. Medical goals are similar to allowable error limits; however, clinicians set the goals with their patients in mind. The acceptable risk level drives the number of patients a laboratory is willing to expose to an MUR.

    Currently, SQC (Statistical QC) reports a numerical indicator of the level of quality which is subject to variations in calculation and interpretation. The new M.O.R.E. method answers the question, "Is risk acceptable?" with a clear "Yes" or "No." The M.O.R.E. method increases the effectiveness of the QC process and its ability to reduce the number of MURs, and also alerts the laboratorian immediately when the analytical process changes enough to allow more than the acceptable number of MURs to be released.

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