DocumentCode :
1784947
Title :
Admission duration model for infant treatment (ADMIT)
Author :
Feldman, Keith ; Chawla, Nitesh V.
Author_Institution :
Dept. of Comput. Sci. & Eng, Univ. of Notre Dame, Notre Dame, IN, USA
fYear :
2014
fDate :
2-5 Nov. 2014
Firstpage :
583
Lastpage :
587
Abstract :
In today´s healthcare environment, nurses play an integral role in determining patient outcomes. This role becomes especially clear in intensive care units such as the Neonatal Intensive Care Unit (NICU). In the NICU, critically ill infants rely almost completely on the care of these nurses for survival. Given the importance of their role, and the volatile conditions of the infants, it is imperative that nurses be able to focus on the infants in their charge. In order to provide this level of care there must be an appropriate infant to nurse ratio each day. However traditional staffing models often utilize a number of factors, including historical census counts, which when incorrect leave a NICU at risk of operating barely reaching, or even below the recommended staffing level. This work will present the novel ADMIT (Admission Duration Model for Infant Treatment) model, which yields personalized length of stay estimates for an infant, utilizing data available from time of admission to the NICU.
Keywords :
electronic health records; health care; paediatrics; patient care; ADMIT; Neonatal Intensive Care Unit; admission duration model-for-infant treatment; critically ill infants; healthcare environment; historical census counts; infant-to-nurse ratio; intensive care units; patient outcomes; personalized length-of-stay estimates; recommended staffing level; time-of-admission; traditional staffing models; Boosting; Correlation; Data models; Diseases; Hospitals; Pediatrics; Predictive models;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2014 IEEE International Conference on
Conference_Location :
Belfast
Type :
conf
DOI :
10.1109/BIBM.2014.6999225
Filename :
6999225
Link To Document :
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