Zachary M Grinspan1,2,3, Anup D Patel4, Baria Hafeez1, Erika L Abramson1,2,3, Lisa M Kern1,3,5. 1. Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY, USA. 2. Department of Pediatrics, Weill Cornell Medicine, New York, NY, USA. 3. New York Presbyterian Hospital, New York, NY, USA. 4. Nationwide Children's Hospital, Columbus, OH, USA. 5. Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
Abstract
OBJECTIVE: Among children with epilepsy, to develop and evaluate a model to predict emergency department (ED) use, an indicator of poor disease control and/or poor access to care. METHODS: We used electronic health record data from 2013 to predict ED use in 2014 at 2 centers, benchmarking predictive performance against machine learning algorithms. We evaluated algorithms by calculating the expected yearly ED visits among the 5% highest risk individuals. We estimated the breakeven cost per patient per year for an intervention that reduced ED visits by 10%. We estimated uncertainty via cross-validation and bootstrapping. RESULTS: Bivariate analyses showed multiple potential predictors of ED use (demographics, social determinants of health, comorbidities, insurance, disease severity, and prior health care utilization). A 3-variable model (prior ED use, insurance, number of antiepileptic drugs [AEDs]) performed as well as the best machine learning algorithm at one center (N = 2730; ED visits among top 5% highest risk, 3-variable model, mean = 2.9, interquartile range [IQR] = 2.7-3.1 vs Random Forest, mean = 2.9, IQR = 2.7-3.1), and superior at the second (N = 784; mean = 2.5, IQR = 2.2-2.9 vs mean = 1.9, IQR = 1.6-2.5). The per-patient-per-year breakeven point using this model to identify high-risk individuals was $958 (95% confidence interval [CI] = $568-$1390) at one center and $1086 (95% CI = $886-$1320) at the second. SIGNIFICANCE: Prior ED use, insurance status, and number of AEDs, taken together, predict future ED use for children with epilepsy. Our estimates suggest a program targeting high-risk children with epilepsy that reduced ED visits by 10% could spend approximately $1000 per patient per year and break even. Further work is indicated to develop and evaluate such programs. Wiley Periodicals, Inc.
OBJECTIVE: Among children with epilepsy, to develop and evaluate a model to predict emergency department (ED) use, an indicator of poor disease control and/or poor access to care. METHODS: We used electronic health record data from 2013 to predict ED use in 2014 at 2 centers, benchmarking predictive performance against machine learning algorithms. We evaluated algorithms by calculating the expected yearly ED visits among the 5% highest risk individuals. We estimated the breakeven cost per patient per year for an intervention that reduced ED visits by 10%. We estimated uncertainty via cross-validation and bootstrapping. RESULTS: Bivariate analyses showed multiple potential predictors of ED use (demographics, social determinants of health, comorbidities, insurance, disease severity, and prior health care utilization). A 3-variable model (prior ED use, insurance, number of antiepileptic drugs [AEDs]) performed as well as the best machine learning algorithm at one center (N = 2730; ED visits among top 5% highest risk, 3-variable model, mean = 2.9, interquartile range [IQR] = 2.7-3.1 vs Random Forest, mean = 2.9, IQR = 2.7-3.1), and superior at the second (N = 784; mean = 2.5, IQR = 2.2-2.9 vs mean = 1.9, IQR = 1.6-2.5). The per-patient-per-year breakeven point using this model to identify high-risk individuals was $958 (95% confidence interval [CI] = $568-$1390) at one center and $1086 (95% CI = $886-$1320) at the second. SIGNIFICANCE: Prior ED use, insurance status, and number of AEDs, taken together, predict future ED use for children with epilepsy. Our estimates suggest a program targeting high-risk children with epilepsy that reduced ED visits by 10% could spend approximately $1000 per patient per year and break even. Further work is indicated to develop and evaluate such programs. Wiley Periodicals, Inc.
Authors: Anup D Patel; Andrea Debs; Debbie Terry; William Parker; Mary Burch; Debra Luciano; Lauren Patton; Jena Brubaker; Julie Chrisman; Kathy Moellman; James Herbst; Daniel M Cohen Journal: Neurol Clin Pract Date: 2021-10
Authors: Elizabeth Golembiewski; Katie S Allen; Amber M Blackmon; Rachel J Hinrichs; Joshua R Vest Journal: JMIR Public Health Surveill Date: 2019-10-07