The document discusses predicting patient discharge from hospital wards with no real-time clinical data. It presents a random forest model using features like ward admissions/discharges, patient characteristics, and time series patterns. The random forest model improved discharge predictions over baseline ARIMA and naive models, with a 25% reduction in error versus naive forecasts. Seasonality like day-of-week was found to influence predictions, suggesting administrative factors impact discharges. The proposed commonly available data-driven approach could integrate into hospital systems to help manage bed occupancy.

1. Forecasting patient outflow from wards having
no real-time clinical data
Shivapratap Gopakumar
Truyen Tran, Wei Luo, Dinh Phung, Svetha Venkatesh
PPattern RRecognition aand DData AAnalytics
School of Information Technology
Deakin University, Australia
ICHI’16
Chicago

2. Introduction
Demand for Healthcare services increasing
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“There is growing concern in various countries that the methods of providing
health care services are, if not already, approaching a level that will not be
sustained by the population.”
Mackay 2005; WHO report; European Commission report
Inpatient beds reduced by 2% since the last decade
 Increased levels of bed occupancy = high throughput to contain
costs
Efficient bed management is key to avoid bed crisis

3. Predicting discharge from ward
 Little attention for predicting
discharges from general wards
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 Daily discharge rate = indicator of
efficiency
Ward Manager
Recovery ward
Current demand
Past experience
Number of beds needed
Can we provide a good estimate for total
next-day discharges from the ward?
 Significance: Relieve emergency
access block !

4. Challenges
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No real-time clinical data.
Case-mix of patients in
ward.
 Non-linear hospital
dynamics.
Variation in data
Discharge pattern for each week
Each colour represents a week

5. Related Work
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 Majority of studies on flow
in Emergency department.
 Other studies target wards
with real-time clinical data.
 To the best of our
knowledge, this is the first
study for open ward with
no real-time clinical data

6. Data
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Tables in hospital database
Cohort details: Jan 2010 – Dec 2014
Min = 8.6 mins
Max = 44 days

7. Data: Patterns
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Weekly discharge pattern Monthly discharge pattern
Daily discharges Time series decomposed to:
• Trend: long time change in mean level
• Seasonality: seasonal variations in the data
• Noise

8. Baseline Model: ARIMA
Autoregressive integrated moving average (ARIMA)
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 able to capture trends and seasonal variations and update
the changes over time.
Forecasted Discharge at time t
sum of recent discharges sum of recent forecast errors

9. Our contribution:
Feature engineering and random forest
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 Random Forest: creates an ensemble of decision trees
Tree 1
Tree n
 Tree bagging + random feature selection
= good prediction with great control on overfitting

10. 10
Our contribution:
Feature engineering and random forest
We derived three groups of features from Ward data:
 Ward level, Patient level, Time series
 Ward-level features:
 Admissions: in past 7 days
Discharges: in past 7 days
Occupancy: in the previous day

11. 11
Our contribution:
Feature engineering and random forest
 Patient-level features:
 Type of admission: 5 categories
 Unit referred from : 49 categories
 Patient class: 21 categories
 Age: 8 categories
# Wards visited: 4 categories
Elapsed length of stay for each patient

12. 12
Our contribution:
Feature engineering and random forest
 Time-series features:
Seasonality: Current day-of-week, month, time-series
decomposition
 Trend: Polynomial regression

13. Experiment
• Baseline models: ARIMA, Naïve forecast (median
discharge)
• Compared with Random forest with our feature set
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14. Experiment: Measuring performance
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 Mean Forecast Error:
 Mean Absolute Error:
 Root mean square error:
 Symmetric Mean Absolute Percentage Error:
= True discharge at t = Forecasted discharge at t

15. Results
Random forest predictions:
 25% improvement over Naive forecasting
 17% improvement over ARIMA
 Least error for each day-of-week
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RMSE

16. Discussion
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Seasonality:
time-series decomposition
Number of patients in
ward, the previous dayPatients with only 1
ward visited before
current.
Number of males in
ward# dishcharges on prev
14th
dayForecasted trend using
polynomial regression
“Public Standard”
Discharges21 days
before
Elapse patient length
of stay

17. Discussion
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RMSE
Fridays are easiest to predict
Saturdays are hardest to predict

18. Conclusion
1. Pronounced weekly patterns, as discussed in other studies
 suggests discharges are heavily influenced by
administrative reasons and staffing
1. Forecast performance is not as good as emergency/acute
care studies.
 But no clinical data available.
1. Proposed model built from commonly available data.
 Can be easily integrated into existing systems.
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19. Thank you
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20. References
• A. Kalache and A. Gatti, “Active ageing: a policy framework.” Advances in gerontology, vol. 11, pp. 7–18,
2002.
• M. Mackay and M. Lee, “Choice of models for the analysis and forecasting of hospital beds,” Health Care
Management Science, vol. 8, no. 3, pp. 221–230, 2005.
• M. Connolly, C. Deaton, M. Dodd, J. Grimshaw, T. Hulme, S. Everitt, and S. Tierney, “Discharge
preparation: Do healthcare professionals differ in their opinions?” Journal of interprofessional care, vol. 24,
no. 6, pp. 633–643, 2010.
• M. V. Shcherbakov, A. Brebels, N. L. Shcherbakova, A. P. Tyukov, T. A. Janovsky, and V. A. Kamaev, “A
survey of forecast error measures,” World Applied Sciences Journal, vol. 24, pp. 171–176, 2013.
• J. S. Peck, J. C. Benneyan, D. J. Nightingale, and S. A. Gaehde, “Predicting emergency department inpatient
admissions to improve same-day patient flow,” Academic Emergency Medicine, vol. 19, no. 9, pp. E1045–
E1054, 2012.
• S. Barnes, E. Hamrock, M. Toerper, S. Siddiqui, and S. Levin, “Real-time prediction of inpatient length of
stay for discharge prioritization” Journal of the American Medical Informatics Association, 2015.
• M. J. Kane, N. Price, M. Scotch, and P. Rabinowitz, “Comparison of arima and random forest time series
models for prediction of avian influenza h5n1 outbreaks,” BMC bioinformatics, vol. 15, p. 276, 2014.
• W. Luo, J. Cao, M. Gallagher, and J. Wiles, “Estimating the intensity of ward admission and its effect on
emergency department access block,” Statistics in medicine, vol. 32, no. 15, pp. 2681–2694, 2013.
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21. Image credits
• Noun project:
– Benpixels
– Vinod Krishna
– Icon Fair
– Nikita Kozin
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