Making the hospital smart: using a deep long short-term memory model to predict hospital performance metrics
Industrial Management & Data Systems
ISSN: 0263-5577
Article publication date: 1 April 2022
Issue publication date: 2 November 2022
Abstract
Purpose
Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.
Design/methodology/approach
As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.
Findings
The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.
Practical implications
With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.
Originality/value
To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.
Keywords
Acknowledgements
Qiong Jia and Ying Zhu are contributed equally to this paper and are equal first authors. Their names are listed in alphabetical order.
Funding: This research is supported by the Nanjing Soft Science Research Foundation under Grant No. 2021SR00400030 and the National Natural Science Foundation of China under Grant No. 71702045.
Citation
Jia, Q., Zhu, Y., Xu, R., Zhang, Y. and Zhao, Y. (2022), "Making the hospital smart: using a deep long short-term memory model to predict hospital performance metrics", Industrial Management & Data Systems, Vol. 122 No. 10, pp. 2151-2174. https://doi.org/10.1108/IMDS-12-2021-0769
Publisher
:Emerald Publishing Limited
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