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

Shivapratap Gopakumar, Truyen Tran, Wei Luo, Quoc Dinh Phung, Svetha Venkatesh

Research output: Contribution to journalArticleResearchpeer-review


Background: Modeling patient flow is crucial in understanding resource demand and prioritization. We study patient outflow
from an open ward in an Australian hospital, where currently bed allocation is carried out by a manager relying on past experiences
and looking at demand. Automatic methods that provide a reasonable estimate of total next-day discharges can aid in efficient
bed management. The challenges in building such methods lie in dealing with large amounts of discharge noise introduced by
the nonlinear nature of hospital procedures, and the nonavailability of real-time clinical information in wards.
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.
Original languageEnglish
Article numbere25
Pages (from-to)1-16
Number of pages16
JournalJMIR Medical Informatics
Issue number3
Publication statusPublished - 2016

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