پیش بینی داده ها برای مدیریت منابع انسانی در بیمارستان های هوشمند
Abstract
1- Introduction
2- Related work
3- ElHealth model
4- Evaluation methodology
5- Performance evaluation and results analysis
6- Conclusion and future works
References
Abstract
Hospitals play an important role towards ensuring proper health treatment to human beings. One of the major challenges faced in this context refers to the increasingly overcrowded patients queues, which contribute to a potential deterioration of patients health conditions. One of the reasons of such an inefficiency is a poor allocation of health professionals. In particular, such allocation process is usually unable to properly adapt to unexpected changes in the patients demand. As a consequence, it is frequently the case where underused rooms have idle professionals whilst overused rooms have less professionals than necessary. Previous works addressed this issue by analyzing the evolution of supply (doctors) and demand (patients) so as to better adjust one to the other, though none of them focused on proposing effective counter-measures to mitigate poor allocations. In this paper, we build upon the concept of smart hospitals and introduce elastic allocation of human resources in healthcare environments (ElHealth), an IoT-focused model able to monitor patients usage of hospital rooms and to adapt the allocation of health professionals to these rooms so as to meet patients needs. ElHealth employs data prediction techniques to anticipate when the demand of a given room will exceeds its capacity, and to propose actions to allocate health professionals accordingly. We also introduce the concept of multi-level predictive elasticity of human resources (which is an extension of the concept of resource elasticity, from cloud computing) to manage the use of human resources at different levels of a healthcare environment. Furthermore, we devise the concept of proactive human resources elastic speedup (which is an extension of the speedup concept, from parallel computing) to properly measure the gain of healthcare time with dynamic parallel use of human resources within hospital environments. ElHealth was thoroughly evaluated based on simulations of a hospital environment using data from a Brazilian polyclinic, and obtained promising results, decreasing the waiting time by up to 96.71%.
Introduction
Internet of Things (IoT) is a concept where physical, digital, and virtual objects (i.e., things) are connected through a network structure and are part of the Internet activities in order to exchange information about themselves and about objects and things around them (Singh and Kapoor, 2017). IoT enables devices to interact not only with each other but also with services and people on a global scale (Akeju et al., 2018). The development of this paradigm is in constant growth due to the continuous efforts of the research community and due to its usefulness to a wide range of domains, such as airports, military, and healthcare (Singh and Kapoor, 2017; Sarhan, 2018). A particularly relevant scenario for IoT is healthcare (da Costa et al., 2018). According to Pinto et al. (2017), IoT promises to revolutionize healthcare applications by promoting more personalized, preventive, and collaborative ways of caring for patients. In particular, IoT-assisted patients can be supervised uninterruptedly using wearable devices, thus allowing risky situations to be detected and appropriately treated right away (Darshan and Anandakumar, 2015; Srinivas et al., 2018). Moreover, IoT provides a means for health systems to extract and analyze data, which can then be combined with machine learning techniques to early detect health disorders (Singh, 2018; Moreira et al., 2019).