title: A standard approach to measurement uncertainties for scientists and engineers in medicine.
abstract: The critical nature of health care demands high performance levels from medical equipment. To ensure these performance levels are maintained, medical physicists and biomedical engineers conduct a range of measurements on equipment during acceptance testing and on-going quality assurance programs. Wherever there are measurements, there are measurement uncertainties with potential conflicts between the measurements made by installers, owners and occasionally regulators. Prior to 1993, various methods were used to calculate and report measurement uncertainties. In 1993, the International Organization for Standardization published the Guide to the Expression of Uncertainty in Measurement (GUM). The document was jointly published with six international organizations principally involved in measurements and standards. The GUM is regarded as an international benchmark on how measurement uncertainty should be calculated and reported. Despite the critical nature of these measurements, there has not been widespread use of the GUM by medical physicists and biomedical engineers. This may be due to the complexity of the GUM. Some organisations have published guidance on the GUM tailored to specific measurement disciplines. This paper presents the philosophy behind the GUM, and demonstrates, with a medical physics measurement example, how the GUM recommends uncertainties be calculated and reported.
authors.affiliation: School of Electrical and Information Engineering (Applied Physics), University of South Australia, Mawson Lakes, Australia. kent.gregory@state.sa.gov.au
authors.forename: K
authors.initials: K
authors.lastname: Gregory
authors.forename: G
authors.initials: G
authors.lastname: Bibbo
authors.forename: J E
authors.initials: JE
authors.lastname: Pattison
mesh_terms: D000465:Algorithms; D001698:Biomedical Engineering / Q000379:methods* / Q000592:standards*; D003627:Data Interpretation, Statistical; D017408:Guidelines as Topic; D008954:Models, Biological; D015233:Models, Statistical; D012586:Science / Q000379:methods* / Q000706:statistics & numerical data*; D035501:Uncertainty