Forecasting hospital resource requirements using remote-sensing satellite imagery

Abstract

In recent years, the volume of satellite imagery has also grown significantly, as more satellites were placed in orbit to monitor the status of the Earth. Thus, the availability of this expanding satellite data presents an excellent opportunity for discovering novel approaches, specifically data-driven methodologies. Indeed, the Sentinel-3 Earth observation satellite developed by the European Space Agency as part of the Copernicus Programme was used to capture the land surface temperature data for this project in contrast to the ground data offered by the MET Office, Malta International Airport. Extracting land surface temperature for a particular region using satellite images is quite challenging, which the research project aims to cover. Hospital admissions and patient length of stay are highly dynamic. Different departments, particularly the emergency ward, may experience long and perpetual queues during specific periods. In such cases, the overall workflow for effectively managing and preparing medical departments’ resources has become a daunting task. Not only that, but economists also bear serious concern about the efficient use of scarce resources in hospitals. In certain countries, this issue is also reaching unsustainable levels endured by the general population [1-2]. Therefore, a growing need to forecast hospital resource requirements has become more apparent in recent years. Through sustainable use of modelling, the allocation of resources can be significantly improved so that the number of bed crises could be reduced to a minimum, or even avoided completely [3]. This research study attempts to solve such issues by closely identifying a tentative relationship between fluctuations in land surface temperature in conjunction with hospital discharge data offered by the Directorate for Health Information & Research (DHIR). The data covers anonymous information including the admission and discharge data and the admitting and discharging ward of every patient for a specific period. Land Surface Temperature (LST) data were obtained daily, spanning one year. Moreover, an inset figure was drawn on each LST data frame to cover the geographical region surrounding the Maltese borders solely. This was done to obtain the best possible accuracy when comparing the two datasets simultaneously. Moreover, these satellite images were compared and temperature changes were recorded from time to time. The results indicate an inverse relationship between hospital admissions and temperature increase, which could be helpful for health professionals and policymakers that may seek the impact of movements in temperature concerning hospital admission figures. The satellite data also indicated a positive relationship with ground temperature. It may also be beneficial for further research into the topic in question.