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Evaluation of Quality OptimiZer software (Awesome Numbers Inc.) to simplify application of CLSI EP 23-A, minimize patient risk and reduce clinical cost
EP22120
Poster Title: Evaluation of Quality OptimiZer software (Awesome Numbers Inc.) to simplify application of CLSI EP 23-A, minimize patient risk and reduce clinical cost
Submitted on 10 Aug 2014
Author(s): Zoe Brooks1, Raymond Gerz2, Nathalie Lepage3, David Plaut4
Affiliations: 1,2. AWEsome Numbers Inc. 3. Children’s Hospital of Eastern Ontario 4. David’s Consulting
This poster was presented at American Association for Clinical Chemistry, Chicago
Poster Views: 1,551
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Poster Information
Abstract: Objectives
1. to evaluate the efficacy of Quality OptimiZerTM software to assess analytical process quality, verify clinical effectiveness of Q.C. processes and recommend a comprehensive Q.C. strategy,
2. to quantify the impact of OptimiZer Q.C. processes on patient risk, clinical and laboratory costs, and
3. to compare Quality OptimiZer reports to EP 23 recommendations.

Relevance:
Ineffective quality control practices expose patients to the risk of incorrect or delayed diagnosis and/or treatment. CLSI EP23 requires labs to “ensure test result quality is appropriate for clinical use;” validate “the ability of the QC procedures to detect medically allowable error;” and assess “potential costs both in terms of the patient’s well-being and financial liability.”


Methodology:
1. We examined analytical processes, Q.C. processes, patient volumes and costs from two laboratories x two instruments x five analytes.
2. For each Q.C. sample, we gathered:
a. the four numbers required to evaluate analytical process quality:
1. Measured mean; 2. Measured SD; 3. Peer mean; 4. TEa limit, and
b. the three numbers that determine Q.C. process effectiveness:
5. Q.C. Chart assigned mean; 6. Assigned SD; and 7. Q.C. rule(s)
3. Quality OptimiZer:
a. rated analytical process quality based on Total Error and Margin for Error
b. recommended a 5-part Q.C. strategy
c. simulated a shift that would cause 5% of results to fail TEa limits.
d. compared the effectiveness of current and recommended QC processes to detect this significant shift
e. quantified patient risk, clinical and laboratory costs of each QC process.
4. We selected one sodium control to illustrate the importance and interaction of the seven numbers required to manage quality.

Results
1. For the selected sodium example:
a. Laboratory practice for all controls on all tests was to use a 1-2 and 9x rule as warnings, and 1-3, 2-2, 2/3-2, R4 rules as rejects.
b. The assigned mean was 0.7 SD below the measured mean; the SD was assigned at 2.1 x the measured SD.
c. The OptimiZer Q.C. strategy would detect a clinically-significant change sooner, prevent risk to 1330 patients, save 66 patients from clinically-misleading results and result in a net saving of $1,062.00.
2. Quality OptimiZer reports satisfied EP 23-A recommendations pertaining to stable sample quality control to:
a. ensure test result quality is appropriate for clinical use,
b. determine statistical limits that will identify unacceptable changes in performance of the measuring system,
c. prove effectiveness of quality control
d. quantify patient risks and costs of control quality,
e. implement and modify a 5-part Q.C. strategy.

Conclusions
1. OptimiZer Q.C. processes fulfilled EP23-A requirements.
2. decreased patient risk, and reduced clinical costs.
3. Error detection is impeded by the common practice of assigning mean and SD values from inappropriate sources and using outdated Q.C. rules.
4. Laboratory quality would benefit from increased staff focus on clinical quality and the interaction of the seven numbers that drive and assess that quality.

Summary:
Quality OptimiZer medical laboratory quality management software simplifies, improves and advances laboratory quality control. Software converts existing data into NEW risk-based grades, star ratings and words that quantify avoidable patient risk and costs, with action flags. Multi-site organizations can quickly identify and improve tests that put patients at risk. This is literally better than Six Sigma!
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