AACC 2017 Poster: Managing Risk with Acceptable Risk Criteria and Mathematically-Optimized Risk Evaluation

AACC 2017 Poster: Managing Risk with Acceptable Risk Criteria and Mathematically-Optimized Risk Evaluation

Title:  Managing Risk with Acceptable Risk Criteria and Mathematically-Optimized Risk Evaluation

Authors: Zoe Brooks, Kim Przekop, George Sweeney, John Hopkins

Permanent Number: B-202

Poster Session Day:  Wednesday, August 2, 2017

Poster Session Number / Day:  Session B: 9:30 am – 5:00 pm

Attendance Time:  12:30 pm – 1:00 pm

Objectives:

  1. To quantify the impact of setting Acceptable Risk Criteria as the acceptable number of medically unacceptable results/year, rather than statistical metrics
  2. To present the Margin for Error (ME) – the number of standard deviations the mean can shift before risk becomes unacceptable –
  3. To demonstrate how, unlike sigma metrics, Margin for Error responds to patient volume and acceptable risk criteria
  4. To illustrate how Mathematically-OptimiZed Risk Evaluation can evaluate risk and design QC processes to vary with defined acceptable risk criteria
  5. To identify opportunities to lower patient risk exposure and existing clinical cost of lab errors

Background:

To evaluate risk, laboratories must “compare the estimated risk against given risk criteria to determine the acceptability of the risk” (CLSI EP 23-A). Acceptable risk criteria are implied in acceptable sigma values or error rates. Selection of Acceptable Risk Criteria as the number and cost of medically-unreliable results drives the perceived and factual acceptability of patient risk. Acceptable risk criteria set standards of acceptable analytical process quality and QC process effectiveness.

Methods:

  1. We created a calcium data set with of recently 1. measured mean; 2. measured SD; 3. Peer Mean equivalent to a sigma values or z-value of 5.7, based on a TEa CLIA
  2. We entered the data in CatalystQC software with 3 different Acceptable Risk Criteria
  3. The QC data were compared to Acceptable Risk Criteria of 5% allowable error; 2 sigma, 3 sigma and 1 Medically-Unreliable Result/year
  4. We set the clinical/legal cost of each MUR at $100 based on the NIST study
  5. We compared the interpretation of acceptability of quality and probable action based on the selected Acceptable Risk Criteria

Results:

Below is a table of the results of the study. The data used is the same within each case, only the Acceptable Risk Criteria is changed within each case.

Discussion:

Risk management and IQCP present new challenges and new opportunities. CLSI EP 23-A states that risk evaluation is the “process of comparing the estimated risk against given risk criteria to determine the acceptability of the risk.” Laboratories must “Evaluate the potential costs both in terms of the patient’s well-being and in terms of financial liability of the treating parties vs known benefits to the patient.” Laboratory methods have been previously considered acceptable if QC charts showed no rejects and summary results passed generally accepted standards if 5% allowable error, or had an acceptable sigma metric of 2 or 3 sigma at a regular/monthly review.

To meet risk management standards, laboratories must measure and evaluate risk as the number and clinical/legal cost of results that exceed TEa limits. However, z-values (sigma values) do not vary with either patient volume or the acceptable risk criteria. Here we present the Margin for Error (ME) – the number of standard deviations the mean can shift before risk becomes unacceptable.

We see that by only changing acceptable risk criteria there is a significant change in the amount of patient risk and clinical cost allowed before QC will alert staff to stop and take action.

Conclusion:

  1. Comparing the results of evaluation with statistical and clinical acceptable risk criteria demonstrates that clinical acceptable risk criteria will play a major role in clinical acceptability, patient risk, and cost due to medically unacceptable errors.
  2. The process of Mathematically-OptimiZed Risk Evaluation should be further evaluated

 

 

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