A Monte Carlo approach to the estimation & analysis of uncertainty in clinical laboratory measurement processes

Varun Ramamohan, Vishal Chandrasekar, Jim Abbott, George G. Klee, Yuehwern Yih

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Clinical laboratory testing is a vital component of many stages of the medical decision making process, and therefore information about the quality of the measurement process is critical to the medical decision-making process. A statement of uncertainty of the result of a laboratory test provides this information. To obtain this information, the clinical laboratory measurement process is conceptualized as a self-contained system, the concept of process phases is introduced, and a broadly applicable algorithm describing the modeling and estimation of uncertainty of such processes is developed. The article discusses how performance specifications for individual components can be used to characterize their uncertainty, and uses Monte Carlo simulation to integrate these individual component uncertainties into a net system uncertainty. The proposed approach is illustrated by developing a mathematical model of the serum cholesterol assay analysis procedure. The uses of the model are to: 1) simulate, evaluate and optimize quality control policies without resorting to conducting controlled experiments, 2) obtain performance targets for the measurement process by using uncertainty estimates from the simulation, 3) estimate the contribution of each source of uncertainty to the net system uncertainty, and 4) study the effects of varying the parameters of the system on the net system uncertainty are illustrated with examples.

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalIIE Transactions on Healthcare Systems Engineering
Volume2
Issue number1
DOIs
StatePublished - 2012

Fingerprint

Clinical laboratories
Uncertainty
uncertainty
Decision making
analysis procedure
simulation
Cholesterol
decision making process
quality control
Quality Control
decision-making process
performance
Quality control
Assays
Theoretical Models
Mathematical models
Specifications

Keywords

  • Clinical laboratory testing
  • laboratory measurement processes
  • Monte Carlo simulation
  • serum cholesterol assay
  • uncertainty estimation

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Safety, Risk, Reliability and Quality
  • Safety Research

Cite this

A Monte Carlo approach to the estimation & analysis of uncertainty in clinical laboratory measurement processes. / Ramamohan, Varun; Chandrasekar, Vishal; Abbott, Jim; Klee, George G.; Yih, Yuehwern.

In: IIE Transactions on Healthcare Systems Engineering, Vol. 2, No. 1, 2012, p. 1-13.

Research output: Contribution to journalArticle

Ramamohan, Varun ; Chandrasekar, Vishal ; Abbott, Jim ; Klee, George G. ; Yih, Yuehwern. / A Monte Carlo approach to the estimation & analysis of uncertainty in clinical laboratory measurement processes. In: IIE Transactions on Healthcare Systems Engineering. 2012 ; Vol. 2, No. 1. pp. 1-13.
@article{18a2b42921304a118f66c629d3d3a36b,
title = "A Monte Carlo approach to the estimation & analysis of uncertainty in clinical laboratory measurement processes",
abstract = "Clinical laboratory testing is a vital component of many stages of the medical decision making process, and therefore information about the quality of the measurement process is critical to the medical decision-making process. A statement of uncertainty of the result of a laboratory test provides this information. To obtain this information, the clinical laboratory measurement process is conceptualized as a self-contained system, the concept of process phases is introduced, and a broadly applicable algorithm describing the modeling and estimation of uncertainty of such processes is developed. The article discusses how performance specifications for individual components can be used to characterize their uncertainty, and uses Monte Carlo simulation to integrate these individual component uncertainties into a net system uncertainty. The proposed approach is illustrated by developing a mathematical model of the serum cholesterol assay analysis procedure. The uses of the model are to: 1) simulate, evaluate and optimize quality control policies without resorting to conducting controlled experiments, 2) obtain performance targets for the measurement process by using uncertainty estimates from the simulation, 3) estimate the contribution of each source of uncertainty to the net system uncertainty, and 4) study the effects of varying the parameters of the system on the net system uncertainty are illustrated with examples.",
keywords = "Clinical laboratory testing, laboratory measurement processes, Monte Carlo simulation, serum cholesterol assay, uncertainty estimation",
author = "Varun Ramamohan and Vishal Chandrasekar and Jim Abbott and Klee, {George G.} and Yuehwern Yih",
year = "2012",
doi = "10.1080/19488300.2012.665153",
language = "English (US)",
volume = "2",
pages = "1--13",
journal = "IISE Transactions on Healthcare Systems Engineering",
issn = "2472-5579",
publisher = "Taylor and Francis Ltd.",
number = "1",

}

TY - JOUR

T1 - A Monte Carlo approach to the estimation & analysis of uncertainty in clinical laboratory measurement processes

AU - Ramamohan, Varun

AU - Chandrasekar, Vishal

AU - Abbott, Jim

AU - Klee, George G.

AU - Yih, Yuehwern

PY - 2012

Y1 - 2012

N2 - Clinical laboratory testing is a vital component of many stages of the medical decision making process, and therefore information about the quality of the measurement process is critical to the medical decision-making process. A statement of uncertainty of the result of a laboratory test provides this information. To obtain this information, the clinical laboratory measurement process is conceptualized as a self-contained system, the concept of process phases is introduced, and a broadly applicable algorithm describing the modeling and estimation of uncertainty of such processes is developed. The article discusses how performance specifications for individual components can be used to characterize their uncertainty, and uses Monte Carlo simulation to integrate these individual component uncertainties into a net system uncertainty. The proposed approach is illustrated by developing a mathematical model of the serum cholesterol assay analysis procedure. The uses of the model are to: 1) simulate, evaluate and optimize quality control policies without resorting to conducting controlled experiments, 2) obtain performance targets for the measurement process by using uncertainty estimates from the simulation, 3) estimate the contribution of each source of uncertainty to the net system uncertainty, and 4) study the effects of varying the parameters of the system on the net system uncertainty are illustrated with examples.

AB - Clinical laboratory testing is a vital component of many stages of the medical decision making process, and therefore information about the quality of the measurement process is critical to the medical decision-making process. A statement of uncertainty of the result of a laboratory test provides this information. To obtain this information, the clinical laboratory measurement process is conceptualized as a self-contained system, the concept of process phases is introduced, and a broadly applicable algorithm describing the modeling and estimation of uncertainty of such processes is developed. The article discusses how performance specifications for individual components can be used to characterize their uncertainty, and uses Monte Carlo simulation to integrate these individual component uncertainties into a net system uncertainty. The proposed approach is illustrated by developing a mathematical model of the serum cholesterol assay analysis procedure. The uses of the model are to: 1) simulate, evaluate and optimize quality control policies without resorting to conducting controlled experiments, 2) obtain performance targets for the measurement process by using uncertainty estimates from the simulation, 3) estimate the contribution of each source of uncertainty to the net system uncertainty, and 4) study the effects of varying the parameters of the system on the net system uncertainty are illustrated with examples.

KW - Clinical laboratory testing

KW - laboratory measurement processes

KW - Monte Carlo simulation

KW - serum cholesterol assay

KW - uncertainty estimation

UR - http://www.scopus.com/inward/record.url?scp=84981164778&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84981164778&partnerID=8YFLogxK

U2 - 10.1080/19488300.2012.665153

DO - 10.1080/19488300.2012.665153

M3 - Article

VL - 2

SP - 1

EP - 13

JO - IISE Transactions on Healthcare Systems Engineering

JF - IISE Transactions on Healthcare Systems Engineering

SN - 2472-5579

IS - 1

ER -