Abstract
Early and timely prediction of patient care demand not only affects effective resource allocation but also influences clinical decision-making as well as patient experience. Accurately predicting patient care demand, however, is a ubiquitous challenge for hospitals across the world due, in part, to the demand's time-varying temporal variability, and, in part, to the difficulty in modelling trends in advance. To address this issue, here, we develop two methods, a relatively simple time-vary linear model, and a more advanced neural network model.
Methods
The time-varying linear approach forecasts patient arrivals hourly over a week based on factors such as day of the week and previous 7-day arrival patterns. The neural network method leverages a long short-term memory (LSTM) model, capturing non-linear relationships between past data and a three-day forecasting window. We evaluate the predictive capabilities of the two proposed approaches compared to three existing approaches - a naive approach, a reduced-rank vector autoregressive (VAR) model and the TBATS model.
Results
Using patient care demand data from Rambam Medical Center in Israel, our results show that both proposed models effectively capture hourly variations of patient demand. Additionally, the linear model is more explainable thanks to its simple architecture, whereas, by accurately modelling weekly seasonal trends, the LSTM model delivers lower prediction errors. Taken together, our explorations suggest the utility of machine learning in predicting time-varying patient care demand; additionally, it is possible to predict patient care demand with good accuracy (around 4 patients) three days or a week in advance using machine learning.
Conclusion
Both methods show the ability to capture hourly variability, with the LSTM model having the lowest prediction error. We suggest future research on combining the time-varying linear model based method and the LSTM algorithm with different weights.
Methods
The time-varying linear approach forecasts patient arrivals hourly over a week based on factors such as day of the week and previous 7-day arrival patterns. The neural network method leverages a long short-term memory (LSTM) model, capturing non-linear relationships between past data and a three-day forecasting window. We evaluate the predictive capabilities of the two proposed approaches compared to three existing approaches - a naive approach, a reduced-rank vector autoregressive (VAR) model and the TBATS model.
Results
Using patient care demand data from Rambam Medical Center in Israel, our results show that both proposed models effectively capture hourly variations of patient demand. Additionally, the linear model is more explainable thanks to its simple architecture, whereas, by accurately modelling weekly seasonal trends, the LSTM model delivers lower prediction errors. Taken together, our explorations suggest the utility of machine learning in predicting time-varying patient care demand; additionally, it is possible to predict patient care demand with good accuracy (around 4 patients) three days or a week in advance using machine learning.
Conclusion
Both methods show the ability to capture hourly variability, with the LSTM model having the lowest prediction error. We suggest future research on combining the time-varying linear model based method and the LSTM algorithm with different weights.
| Original language | English |
|---|---|
| Number of pages | 8 |
| Journal | BMC Medical Informatics and Decision Making |
| Publication status | Submitted - 7 Oct 2024 |