Predicting Discharge Disposition Using Patient Complaint Notes in Electronic Medical Records

Mohamad Salimi, Alla Rozovskaya


Abstract
Overcrowding in emergency rooms is a major challenge faced by hospitals across the United States. Overcrowding can result in longer wait times, which, in turn, has been shown to adversely affect patient satisfaction, clinical outcomes, and procedure reimbursements. This paper presents research that aims to automatically predict discharge disposition of patients who received medical treatment in an emergency department. We make use of a corpus that consists of notes containing patient complaints, diagnosis information, and disposition, entered by health care providers. We use this corpus to develop a model that uses the complaint and diagnosis information to predict patient disposition. We show that the proposed model substantially outperforms the baseline of predicting the most common disposition type. The long-term goal of this research is to build a model that can be implemented as a real-time service in an application to predict disposition as patients arrive.
Anthology ID:
W18-2316
Volume:
Proceedings of the BioNLP 2018 workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | BioNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
142–146
Language:
URL:
https://aclanthology.org/W18-2316
DOI:
10.18653/v1/W18-2316
Bibkey:
Cite (ACL):
Mohamad Salimi and Alla Rozovskaya. 2018. Predicting Discharge Disposition Using Patient Complaint Notes in Electronic Medical Records. In Proceedings of the BioNLP 2018 workshop, pages 142–146, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Predicting Discharge Disposition Using Patient Complaint Notes in Electronic Medical Records (Salimi & Rozovskaya, 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-2316.pdf