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Do 70% of medical decisions depend on laboratory results?

Do 70% of medical decisions depend on laboratory results?

The votes are in on LinkedIn, and the belief still stands, despite the lack of evidence and several articles to the contrary.  As scientists, should we not demand proof?

Check “The ‘70% claim’: what is the evidence base?”  or “Dispelling the 70% Claim with Laboratory’s True Value”

This interesting publication by Quest promotes the claim, but provides no proof. “70% of medical decisions are based on lab results.   Solutions to optimize operations, improve quality and lower costs at hospital clinical labs.”

Where do we go from here?  How would one quantify the value of laboratory results?  I would like to believe this is true, but I need proof.  I would happily help crunch and publish data if anyone wants to publish.  

Laboratory results undeniably have value.  They also carry risk and cost to patients and the healthcare system. 



COVID-19 Testing – Impact of Prevalence, Sensitivity, and Specificity on Patient Risk and Cost

COVID-19 Testing – Impact of Prevalence, Sensitivity, and Specificity on Patient Risk and Cost

COVID-19 Testing – Impact of Prevalence, Sensitivity, and Specificity on Patient Risk and Cost
Zoe C. Brooks * and Saswati Das

To evaluate the test methods, sensitivity (percent positive agreement – PPA) and specificity (percent negative agreement – PNA) are the most common metrics utilized, followed by the positive and negative predictive value (PPV and NPV), the probability that a positive or negative test result represents a true positive or negative patient. In this paper, we illustrate how patient risk and clinical costs are driven by false-positive and false-negative results. We demonstrate the value of reporting PFP (probability of false-positive results), PFN (probability of false-negative results), and costs to patients and healthcare. These risk metrics can be calculated from the risk drivers of PPA and PNA combined with estimates of prevalence, cost, and Reff number (people infected by one positive SARS COV-2).

Accepted for publication in AJCP, July 2020

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Mathematically-OptimiZed Risk Evaluation    ASCP Poster 2016

Mathematically-OptimiZed Risk Evaluation ASCP Poster 2016

ASCP Poster 2016
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.

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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.

Kim A. Przekop MBA MLS(ASCP)CM,   Zoe C. Brooks ART

See/download full poster