Predicting a Need for Financial Assistance in Emergency Department Care
Identifying patients with a low likelihood of paying their bill serves the needs of patients and providers alike: aligning government programs with their target beneficiaries while minimizing patient frustration and reducing waste among emergency physicians by streamlining the billing process. The g...
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2021-05-01
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doaj-3f38aa25bbbf4ffb99b7ca42328c76f42021-05-31T23:32:35ZengMDPI AGHealthcare2227-90322021-05-01955655610.3390/healthcare9050556Predicting a Need for Financial Assistance in Emergency Department CareSamuel Davis0Sara Nourazari1Rachel Granovsky2Nasser Fard3Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USADepartment of Health Care Administration, California State University, Long Beach, CA 90840, USASchool of General Studies, Columbia University, New York, NY 10027, USADepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USAIdentifying patients with a low likelihood of paying their bill serves the needs of patients and providers alike: aligning government programs with their target beneficiaries while minimizing patient frustration and reducing waste among emergency physicians by streamlining the billing process. The goal of this study was to predict the likelihood of patients paying the balance of their emergency department visit bill within 90 days of receipt. Three machine learning methodologies were applied to predict payment: logistic regression, decision tree, and random forest. Models were trained and performance was measured using 1,055,941 patients with non-zero balances across 27 EDs from 1 August 2015 to 31 July 2017. The decision tree accurately predicted 87% of unsuccessful payments, providing significant opportunities to identify patients in need of financial assistance.https://www.mdpi.com/2227-9032/9/5/556healthcare financehealth equityemergency departmentpredictive modelingMedicaid |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Samuel Davis Sara Nourazari Rachel Granovsky Nasser Fard |
spellingShingle |
Samuel Davis Sara Nourazari Rachel Granovsky Nasser Fard Predicting a Need for Financial Assistance in Emergency Department Care Healthcare healthcare finance health equity emergency department predictive modeling Medicaid |
author_facet |
Samuel Davis Sara Nourazari Rachel Granovsky Nasser Fard |
author_sort |
Samuel Davis |
title |
Predicting a Need for Financial Assistance in Emergency Department Care |
title_short |
Predicting a Need for Financial Assistance in Emergency Department Care |
title_full |
Predicting a Need for Financial Assistance in Emergency Department Care |
title_fullStr |
Predicting a Need for Financial Assistance in Emergency Department Care |
title_full_unstemmed |
Predicting a Need for Financial Assistance in Emergency Department Care |
title_sort |
predicting a need for financial assistance in emergency department care |
publisher |
MDPI AG |
series |
Healthcare |
issn |
2227-9032 |
publishDate |
2021-05-01 |
description |
Identifying patients with a low likelihood of paying their bill serves the needs of patients and providers alike: aligning government programs with their target beneficiaries while minimizing patient frustration and reducing waste among emergency physicians by streamlining the billing process. The goal of this study was to predict the likelihood of patients paying the balance of their emergency department visit bill within 90 days of receipt. Three machine learning methodologies were applied to predict payment: logistic regression, decision tree, and random forest. Models were trained and performance was measured using 1,055,941 patients with non-zero balances across 27 EDs from 1 August 2015 to 31 July 2017. The decision tree accurately predicted 87% of unsuccessful payments, providing significant opportunities to identify patients in need of financial assistance. |
topic |
healthcare finance health equity emergency department predictive modeling Medicaid |
url |
https://www.mdpi.com/2227-9032/9/5/556 |
work_keys_str_mv |
AT samueldavis predictinganeedforfinancialassistanceinemergencydepartmentcare AT saranourazari predictinganeedforfinancialassistanceinemergencydepartmentcare AT rachelgranovsky predictinganeedforfinancialassistanceinemergencydepartmentcare AT nasserfard predictinganeedforfinancialassistanceinemergencydepartmentcare |
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