Department of Industrial & Systems Engineering, Isfahan University of Technology, Isfahan 841583111, Iran.
Department of Electrical & Computers Engineering, Isfahan University of Technology, Isfahan 841583111, Iran.
J Biomed Inform. 2022 Jun;130:104075. doi: 10.1016/j.jbi.2022.104075. Epub 2022 Apr 28.
To develop an effective Management Information System (MIS) that is empowered by predictive models that can forecast the demand of end-stage cancer home hospitalized patients in individual and population levels, and help palliative care service systems operate smoothly where the demand is highly fluctuating, resources are limited, expensive, and hardly adjustable in a short time, and the backlog and shortage costs are high.
Inspired by real problems faced by a palliative care center providing various medical, nursing, psychological, and social services in a home-based setting, two Long Short-Term Memory (LSTM) based deep learning models are proposed for demand forecasting at both individual and population levels. The individual-level model can predict the type and time of the next service required for a specific patient with a given demographic and health profile, and the population-level model helps with the prediction of next week's demand for various services in a center supporting a specific patient population. Predicted demand informs on optimal resource and operations plan through a well designed MIS.
Experiments were conducted on a dataset consisting of more than 4000 cancer patients with a Palliative Performance Scale (PPS) of 40 and below discharged from hospital to home under a national palliative care center's home hospitalization service in Iran from September 2012 to July 2019. The models outperformed conventional time-series forecasting methods where applicable. Results indicate that the proposed models were capable of forecasting patients' demand with astonishing performances both individually and on larger scales.
Intelligent demand forecasting can help palliative care home hospitalization systems to overcome the challenge of progressive demand growth when a considerable portion of patients are approaching death, followed by a sudden drop in demand when those patients pass away. It helps to improve resource utilization and quality of care concurrently.
开发一个有效的管理信息系统（MIS），该系统采用预测模型，能够预测个体和人群水平上终末期癌症居家住院患者的需求，并帮助姑息治疗服务系统在需求波动大、资源有限、昂贵且难以在短时间内调整、积压和短缺成本高的情况下顺利运行。
受一家姑息治疗中心在居家环境中提供各种医疗、护理、心理和社会服务所面临的实际问题的启发，提出了两种基于长短期记忆（LSTM）的深度学习模型，用于个体和人群水平的需求预测。个体水平模型可以预测特定患者在特定人口统计和健康状况下所需的下一类型和时间的服务，而人群水平模型有助于预测支持特定患者人群的中心下周对各种服务的需求。通过精心设计的 MIS，预测需求为优化资源和运营计划提供信息。
在一个数据集上进行了实验，该数据集由 4000 多名 PPS 为 40 及以下的癌症患者组成，他们从伊朗的一个国家姑息治疗中心的居家住院服务出院，时间为 2012 年 9 月至 2019 年 7 月。在适用的情况下，模型的表现优于传统的时间序列预测方法。结果表明，所提出的模型能够在个体和更大规模上令人惊讶地预测患者的需求。
智能需求预测可以帮助姑息治疗居家住院系统克服当相当一部分患者接近死亡时需求不断增长的挑战，当这些患者去世时需求突然下降。它有助于提高资源利用率和护理质量。
