Mark Zimmerman1, Caroline Balling2, Iwona Chelminski2, Kristy Dalrymple2. 1. Department of Psychiatry and Human Behavior, Brown University School of Medicine, Rhode Island Hospital, Providence, RI. Reprint requests to Mark Zimmerman, MD, 146 West River Street, Providence, RI 02904, United States. Electronic address: mzimmerman@lifespan.org. 2. Department of Psychiatry and Human Behavior, Brown University School of Medicine, Rhode Island Hospital, Providence, RI. Reprint requests to Mark Zimmerman, MD, 146 West River Street, Providence, RI 02904, United States.
Abstract
BACKGROUND: Data mining efforts have been applied to research data bases to develop statistical models for predicting outcomes. Electronic medical records have the potential to enable efforts to apply statistical techniques to mine large clinical data bases. Of course, such prediction algorithms will only be as good as the data that is available to input. The question that we address in the present report from the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) project is how much information might be gained from dimensional ratings of symptom severity over and above that which is accounted for when determining symptom presence. Such results could have implications for how medical record documentation should be established. METHODS: Patients were evaluated with a semi-structured interview, and the presence of each symptom of major depressive disorder (MDD) was recorded. Patients were also rated on the Clinical Global Index of Severity (CGI-S). RESULTS: A multiple regression analysis entering the presence of MDD symptoms as predictors of the CGI had a cumulative R2 of 0.26. A multiple regression analysis entering all symptom severity ratings as predictors of the CGI had a cumulative R2 of 0.40. LIMITATIONS: The study was based on patients presenting for outpatient treatment to a single clinical practice. Symptoms that are not diagnostic criteria for MDD were not examined. DISCUSSION: Research institutions interested in using data mining statistical approaches of electronic medical records should consider having the clinicians rate whether symptoms are mild, moderate or severe and not just whether they are present or absent.
BACKGROUND: Data mining efforts have been applied to research data bases to develop statistical models for predicting outcomes. Electronic medical records have the potential to enable efforts to apply statistical techniques to mine large clinical data bases. Of course, such prediction algorithms will only be as good as the data that is available to input. The question that we address in the present report from the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) project is how much information might be gained from dimensional ratings of symptom severity over and above that which is accounted for when determining symptom presence. Such results could have implications for how medical record documentation should be established. METHODS:Patients were evaluated with a semi-structured interview, and the presence of each symptom of major depressive disorder (MDD) was recorded. Patients were also rated on the Clinical Global Index of Severity (CGI-S). RESULTS: A multiple regression analysis entering the presence of MDD symptoms as predictors of the CGI had a cumulative R2 of 0.26. A multiple regression analysis entering all symptom severity ratings as predictors of the CGI had a cumulative R2 of 0.40. LIMITATIONS: The study was based on patients presenting for outpatient treatment to a single clinical practice. Symptoms that are not diagnostic criteria for MDD were not examined. DISCUSSION: Research institutions interested in using data mining statistical approaches of electronic medical records should consider having the clinicians rate whether symptoms are mild, moderate or severe and not just whether they are present or absent.
Authors: Maikel Luis Kolling; Leonardo B Furstenau; Michele Kremer Sott; Bruna Rabaioli; Pedro Henrique Ulmi; Nicola Luigi Bragazzi; Leonel Pablo Carvalho Tedesco Journal: Int J Environ Res Public Health Date: 2021-03-17 Impact factor: 3.390