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Forecasting daily patient outflow from a ward having no real-time clinical data

Gopakumar, Shivapratap, Tran, Truyen, Luo, Wei, Phung, Quoc-Dinh and Venkatesh, Svetha 2016, Forecasting daily patient outflow from a ward having no real-time clinical data, JMIR medical informatics, vol. 4, no. 3, pp. 1-16, doi: 10.2196/medinform.5650.

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Title Forecasting daily patient outflow from a ward having no real-time clinical data
Author(s) Gopakumar, Shivapratap
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Journal name JMIR medical informatics
Volume number 4
Issue number 3
Article ID e25
Start page 1
End page 16
Total pages 16
Publisher JMIR Publications
Place of publication Toronto, Ont.
Publication date 2016
ISSN 2291-9694
Keyword(s) discharge planning
patient flow
predictive models
Summary OBJECTIVE: Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data.

METHODS: We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features.

RESULTS: Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014.

CONCLUSIONS: In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments.
Language eng
DOI 10.2196/medinform.5650
Field of Research 080109 Pattern Recognition and Data Mining
111716 Preventive Medicine
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085642

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.