Please use this identifier to cite or link to this item: http://hdl.handle.net/11434/1456
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMcKenzie, Dean-
dc.contributor.authorFahey, Michael-
dc.contributor.authorGwini, Stella-
dc.contributor.authorHan Lin, Catherine-
dc.contributor.authorThomas, Christopher-
dc.contributor.authorHanlon, Gabrielle-
dc.contributor.authorBarrett, Jonathan-
dc.contributor.otherMeyer, Denny-
dc.date.accessioned2018-07-27T02:44:10Z-
dc.date.available2018-07-27T02:44:10Z-
dc.date.issued2018-06-
dc.identifier.urihttp://hdl.handle.net/11434/1456-
dc.description.abstractIntroduction Tests that screen for disease must maximize sensitivity (true positives) and specificity (true negatives), so as not to misdiagnose, potentially miss life-threatening disorders or increase patient distress, as well as health care costs. Sensitivity and specificity alone may, however, be misunderstood by patients, and indeed clinicians themselves, especially when disease prevalence is low in the population. Patients testing positive are usually more interested in the probability that they actually have the disease1; known as the positive predictive value. This probability is obtainable using a ‘simple’ Bayesian probability formula, yet it is rarely presented in brochures and other material provided to and/or readily accessible to patients. Professor Gerd Gigerenzer1 and others have conducted many studies, including randomized trials, on how best to present screening information to facilitate understanding and more informed choices. Aims To present simple and understandable methods of illustrating screening test probabilities, including positive predictive values. Methodology Tables and probability trees, that include frequencies, appear to be very informative. These and similar methods could usefully and readily be employed in patient brochures1 and consultations. Such techniques could also be implemented in relevant new and existing patient databases and registries, to provide local, individual and updated probabilities3. Results Illustrative published medical data and custom graphs will be presented.en_US
dc.subjectSensitivityen_US
dc.subjectTrue Positivesen_US
dc.subjectSpecificityen_US
dc.subjectTrue Negativesen_US
dc.subjectMisdiagnosisen_US
dc.subjectHeatlhcare Costsen_US
dc.subjectPositive Predictive Valueen_US
dc.subjectPatient Brochures'en_US
dc.subjectIllustrative Published Medical Dataen_US
dc.subjectCustom Graphsen_US
dc.subjectDisease Probabilityen_US
dc.subjectBayesian Probability Formulaen_US
dc.subjectEpworth Research Institute, Epworth HealthCare, Victoria, Australiaen_US
dc.subjectMonash-Epworth Rehabilitation Research Centre, Epworth HealthCare, Melbourne, Australiaen_US
dc.subjectCritical Care Clinical Institute, Epworth HealthCare, Victoria, Australiaen_US
dc.titleCommunicating risk to patients.en_US
dc.typeConference Posteren_US
dc.description.affiliatesSwinburne University of Technology, Victoria, Australiaen_US
dc.description.affiliatesBarwon Healthen_US
dc.description.affiliatesDeakin University, Victoria, Australiaen_US
dc.description.affiliatesUniversity of Melbourne, Victoria, Australia.en_US
dc.description.conferencenameEpworth HealthCare Research Week 2018en_US
dc.description.conferencelocationEpworth Research Institute, Victoria, Australiaen_US
dc.type.contenttypeTexten_US
Appears in Collections:Critical Care
Rehabilitation
Research Week

Files in This Item:
There are no files associated with this item.


Items in Epworth are protected by copyright, with all rights reserved, unless otherwise indicated.