Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/22238
Title: Predictors of Disagreement Between Diagnoses From Consult Requesters and Consultation-Liaison Psychiatry.
Authors: Otani, Victor;Otani, Thaís;Freirias, Andrea;Calfat, Elie;Aoki, Patricia;Cross, Sean;Sumskis, Susan;Kanaan, Richard A A;Cordeiro, Quirino;Uchida, Ricardo
Affiliation: Department of Psychiatry, Instituto Superior de Medicina, Sao Paulo, Brazil
The University of Melbourne, Heidelberg, Victoria, Australia
Austin Health, Heidelberg, Victoria, Australia
Department of Mental Health, Santa Casa Medical School
Maudsley Simulation, South London & Maudsley Foundation NHS Trust, Lambeth Hospital, London, United Kingdom
School of Nursing, Faculty of Science, Medicine, and Health University of Wollongong, Wollongong
Department of Mental Health, Santa Casa Medical School
Issue Date: Dec-2019
Citation: The Journal of nervous and mental disease 2019; 207(12): 1019-1024
Abstract: We evaluated disagreement between reported symptoms and a final diagnosis of depression, anxiety, withdrawal, psychosis, or delirium through regression models assessing individual and combined diagnoses. Highest disagreement rates were reported for services classified as others (88.2%), general surgery (78.5%), and bone marrow transplant (77.7%). Disagreement rates varied widely across different diagnoses, with anxiety having the highest disagreement rate (63.3%), whereas psychosis had the lowest disagreement rate (10.6%). When evaluating kappa coefficients, the highest agreement occurred with diagnoses of withdrawal and psychosis (0.66% and 0.51%, respectively), whereas anxiety and depression presented the lowest values (0.31% and 0.11%, respectively). The best-performing predictive model for most outcomes was random forest, with the most important predictors being specialties other than the ones focused on single systems, older age, lack of social support, and the requester being a resident. Monitoring disagreement rates and their predictors provides information that could lead to quality improvement and safety programs.
URI: http://ahro.austin.org.au/austinjspui/handle/1/22238
DOI: 10.1097/NMD.0000000000001018
ORCID: 0000-0003-0992-1917
PubMed URL: 31790047
Type: Journal Article
Appears in Collections:Journal articles

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