Abstract
Obstetric care refers to the care provided to patients during ante-, intra-, and postpartum periods. Predicting length of stay (LOS) for these patients during their hospitalizations can assist healthcare organizations in allocating hospital resources more effectively and efficiently, ultimately improving maternal care quality and reducing costs to patients. In this paper, we investigate the extent to which LOS can be forecast from a patient's medical history. We introduce a machine learning framework to incorporate a patient's prior conditions (e.g., diagnostic codes) as features in a predictive model for LOS. We evaluate the framework with three years of historical billing data from the electronic medical records of 9188 obstetric patients in a large academic medical center. The results indicate that our framework achieved an average accuracy of 49.3%, which is higher than the baseline accuracy 37.7% (that relies solely on a patient's age). The most predictive features were found to have statistically significant discriminative ability. These features included billing codes for normal delivery (indicative of shorter stay) and antepartum hypertension (indicative of longer stay).
Original language | English (US) |
---|---|
Title of host publication | MEDINFO 2017 |
Subtitle of host publication | Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics |
Editors | Adi V. Gundlapalli, Jaulent Marie-Christine, Zhao Dongsheng |
Publisher | IOS Press BV |
Pages | 1019-1023 |
Number of pages | 5 |
ISBN (Electronic) | 9781614998297 |
DOIs | |
State | Published - 2017 |
Event | 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 - Hangzhou, China Duration: Aug 21 2017 → Aug 25 2017 |
Publication series
Name | Studies in Health Technology and Informatics |
---|---|
Volume | 245 |
ISSN (Print) | 0926-9630 |
ISSN (Electronic) | 1879-8365 |
Other
Other | 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 |
---|---|
Country/Territory | China |
City | Hangzhou |
Period | 8/21/17 → 8/25/17 |
Funding
This research was sponsored in part by NIH grants R01LM010207 and R00LM011933.
Keywords
- Electronic health records
- Length of stay
- Obstetrics
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
- Health Information Management