Multi-task Prediction of Patient Workload
This work addresses the challenge of workload prediction for healthcare providers, particularly in chronic disease care, but it is incremental as it builds on existing methods by extending them to multiple facilities.
The paper tackles the problem of predicting patient workload across multiple healthcare facilities, which is critical for managing demand uncertainty in chronic disease treatment, by developing a heuristic cluster-based algorithm and a multi-task learning approach using data from VA facilities across the US, resulting in more accurate predictions.
Developing reliable workload predictive models can affect many aspects of clinical decision making procedure. The primary challenge in healthcare systems is handling the demand uncertainty over the time. This issue becomes more critical for the healthcare facilities that provide service for chronic disease treatment because of the need for continuous treatments over the time. Although some researchers focused on exploring the methods for workload prediction recently, few types of research mainly focused on forecasting a quantitative measure for the workload of healthcare providers. Also, among the mentioned studies most of them just focused on workload prediction within one facility. The drawback of the previous studies is the problem is not investigated for multiple facilities where the quality of provided service, the equipment, and resources used for provided service as well as the diagnosis and treatment procedures may differ even for patients with similar conditions. To tackle the mentioned issue, this paper suggests a framework for patient workload prediction by using patients data from VA facilities across the US. To capture the information of patients with similar attributes and make the prediction more accurate, a heuristic cluster based algorithm for single task learning as well as a multi task learning approach are developed in this research.