AI Article Synopsis
- The study aims to identify patients who are unlikely to pay their emergency department bills, improving the experience for both patients and healthcare providers.
- Three machine learning methods (logistic regression, decision tree, and random forest) were used to analyze over a million patients' data to predict payment likelihood within 90 days.
- The decision tree model proved effective, accurately predicting 87% of cases where patients would not pay, highlighting opportunities to better assist those needing financial help.
Article Abstract
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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150762 | PMC |
| http://dx.doi.org/10.3390/healthcare9050556 | DOI Listing |
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