A Hospital Bed Allocation Hybrid Model Based on Situation Awareness

Abstract

Bed management is an important area of planning and control for hospitals, as it has the important role of maintaining the balance between patients from the emergency department, patients who have elective surgery or scheduled treatment, and patients who are discharged from the hospital, while maintaining high bed occupancy rates. Effective management of these resources has always been a challenge for managers. In the 1980s and 1990s, thousands of patients had operations canceled due to nonmedical reasons. Due to the constant uncertainty experienced by hospitals today, use of the cognitive model known as situation awareness has been increasing in healthcare. Situation awareness seeks to understand environmental context to design the future, using artificial intelligence techniques. In this context, this article contributes the use of situation awareness in bed management using a hybrid system that combines known techniques of artificial neural networks and multiattribute value theory for decision-making by automating the process of bed allocation. The system was evaluated in a hospital in Porto Alegre, Brazil, yielding a result of 93.5% similarity between the beds determined by the proposed model and those chosen by the hospital manager.

Optimization of Markov Queuing Model in Hospital Bed Resource Allocation

“…With the continuous deepening of medical reform, the competition between hospitals becomes more and more fierce. Hospital managers must consider how to strike a balance between the two in order to improve service quality, maximize returns, maintain a competitive advantage, and take into account social benefits [ 1 – 3 ].…”

Recursive neural networks in hospital bed occupancy forecasting

“…In particular, it can be integrated into an automatic decision model, e.g. similar to the one developed by Grübler et al [40]. Currently, a software with a user-friendly interface and better performance characteristics is being developed as a web-based application for open access.…”

Indicators of surgical patients after the implementation of an Internal Bed Regulation Committee in a university hospital

“…For 2020, 38 medical residency positions were offered to the surgical center area, distributed into the specialties: anesthesiology (5), neurosurgery (1), obstetrics and gynecology (10), ophthalmology (2), otolaryngology (2), general surgery (7), orthopedics and traumatology (4), urology (1), vascular (1) and endovascular (1) surgery, pediatric surgery (1), oral and maxillofacial traumatology (2) and digestive system surgery (1).…”

“…In this context, the bed management system has emerged as an important part of planning the operational capacity, control, and efficient use of resources. This tool promotes the accommodation of patients from urgency and emergency services without jeopardizing meeting the elective demand (3,7) , considering that bed management is conducted by a professional whose instrument of work is the real-time hospital census.…”