forecasting patient outflow from wards having no real-time clinical data
TRANSCRIPT
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 AAnalyticsSchool of Information TechnologyDeakin University, Australia
ICHI’16Chicago
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
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 !
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 weekEach colour represents a week
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
Data
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Tables in hospital database
Cohort details: Jan 2010 – Dec 2014
Min = 8.6 minsMax = 44 days
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
Baseline Model: ARIMAAutoregressive 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
Our contribution:Feature engineering and random forest
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Random Forest: creates an ensemble of decision trees
Tree 1Tree n
Tree bagging + random feature selection
= good prediction with great control on overfitting
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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 daysDischarges: in past 7 daysOccupancy: in the previous day
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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 categoriesElapsed length of stay for each patient
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Our contribution:Feature engineering and random forest
Time-series features:Seasonality: Current day-of-week, month, time-series
decomposition
Trend: Polynomial regression
Experiment
• Baseline models: ARIMA, Naïve forecast (median discharge)
• Compared with Random forest with our feature set13
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
Results
Random forest predictions: 25% improvement over Naive forecasting 17% improvement over ARIMA Least error for each day-of-week
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RM
SE
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 beforeElapse patient length of stay
Discussion
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RM
SE
Fridays are easiest to predict
Saturdays are hardest to predict
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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Thank you
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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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Image credits
• Noun project:– Benpixels– Vinod Krishna– Icon Fair– Nikita Kozin
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