Implementation of Electronic Patient Discharge: A Successful Strategy to Increase Hospital Bed Capacity During the COVID-19 Pandemic

Background: The ongoing COVID-19 pandemic increased the need for inpatient beds, indicating the need for hospitals to increase the efficiency of beds. Objectives: This study aimed to increase hospital bed capacity using the implementation of Electronic Patient Discharge (EPD). Methods: This qualitative-quantitative study was conducted in a tertiary care hospital using the pre-and post-intervention designs, and the main outcome was patient discharge time. By applying the Six Sigma model, including definition, measurement, analysis, improvement, and control, the patient discharge process was assessed and improved by some interventions such as EPD. All hospitalized patients with COVID-19 from 21 March 2020 to 22 July 2021 were examined for the post-intervention. In addition, data were collected from the hospital information system (HIS). Results: By the use of EPD, patient discharge time decreased to 47.70% (from 10.19 h to 5.33 h) (P < 0.000). According to the Sigma level, the yield and defects per million opportunities of the discharge process also increased to 55%. Conclusion: Six Sigma methodology can be an effective change management tool to improve discharge time to cover the demand created during pandemics. According to the results of the present study and the obtained saved time, one bed is added to the hospital capacity for every five discharges.

Integrating Deep Reinforcement Learning Networks with Health System Simulations.

Background and motivation: Combining Deep Reinforcement Learning (Deep RL) and Health Systems Simulations has significant potential, for both research into improving Deep RL performance and safety, and in operational practice. While individual toolkits exist for Deep RL and Health Systems Simulations, no framework to integrate the two has been established. Aim: Provide a framework for integrating Deep RL Networks with Health System Simulations, and to ensure this framework is compatible with Deep RL agents that have been developed and tested using OpenAI Gym. Methods: We developed our framework based on the OpenAI Gym framework, and demonstrate its use on a simple hospital bed capacity model. We built the Deep RL agents using PyTorch, and the Hospital Simulation using SimPy. Results: We demonstrate example models using a Double Deep Q Network or a Duelling Double Deep Q Network as the Deep RL agent. Conclusion: SimPy may be used to create Health System Simulations that are compatible with agents developed and tested on OpenAI Gym environments. GitHub repository of code: https://github.com/MichaelAllen1966/learninghospital

Successful Hospital Evacuation After the Kumamoto Earthquakes, Japan, 2016

Two major earthquakes struck Kumamoto Prefecture in Japan in April 2016. Disaster response was immediately provided, including disaster medical services. Many hospitals were damaged and patients needed immediate evacuation to alternative facilities. The hospital bed capacity of Kumamoto Prefecture was overwhelmed, and transportation of more than 100 patients was needed. Hospital evacuation was carried out smoothly with the coordinated efforts of multiple agencies. The overall operation was deemed a success because patients were transported in a timely manner without any significant adverse events. Upon repair of facilities in Kumamoto Prefecture, patients were returned safely to their previous facilities. The management of inpatients after this natural disaster in Kumamoto Prefecture can serve as a model for hospital evacuation with multi-agency coordination in the future. Future efforts are needed to improve interfacility communications immediately following a natural disaster. (Disaster Med Public Health Preparedness. 2017;11:517–521)

The Nevill Hall Virtual Inpatient System

A novel way of managing hospital bed capacity.