Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2022 12:00 AM
End Date
7-1-2022 12:00 AM
Description
Managing Intensive Care Units (ICUs) in hospitals is a highly challenging endeavor. In particular, decisions such as admitting elective patients and discharging patients from the ICU have to be taken under a high level of uncertainty since the occupancy of ICUs does not only depend on these decisions but also on unknown parameters such emergency patient arrivals and lengths of stay of the patients in the ICU. In this paper, we develop a framework for supporting ICU occupation management by quantifying the impact of admission and discharge decisions on the probability of reaching critical ICU occupancy levels in a given planning horizon. A key component of this framework is the use of data-driven approaches for obtaining probability distributions for the parameters affected by uncertainty. In particular, we use standardized treatment and patient health state data to create patient-specific length-of-stay distributions with a Machine Learning approach. These patient-individual distributions are then validated and/or adjusted by medical experts. The validated distributions form the input to a Monte-Carlo Simulation that is used to approximate the probability distributions of the daily ICU occupancy levels resulting from ICU admission and discharge decisions. We experimentally evaluate our framework in a counterfactual simulation based on one year of historical data from 2019 from a medium-sized ICU in a German hospital. In that evaluation, we use a simple ICU management policy based on the probabilistic occupancy forecasts aiming at reducing the risk of running out of ICU capacity. The results show that following this policy would have avoided hitting critical occupancy levels by around 70% and would have had a smoothing effect on ICU occupancy levels.
Monte-Carlo Simulation Based on Patient-Individual Distributions for Supporting Intensive Care Occupancy Management
Online
Managing Intensive Care Units (ICUs) in hospitals is a highly challenging endeavor. In particular, decisions such as admitting elective patients and discharging patients from the ICU have to be taken under a high level of uncertainty since the occupancy of ICUs does not only depend on these decisions but also on unknown parameters such emergency patient arrivals and lengths of stay of the patients in the ICU. In this paper, we develop a framework for supporting ICU occupation management by quantifying the impact of admission and discharge decisions on the probability of reaching critical ICU occupancy levels in a given planning horizon. A key component of this framework is the use of data-driven approaches for obtaining probability distributions for the parameters affected by uncertainty. In particular, we use standardized treatment and patient health state data to create patient-specific length-of-stay distributions with a Machine Learning approach. These patient-individual distributions are then validated and/or adjusted by medical experts. The validated distributions form the input to a Monte-Carlo Simulation that is used to approximate the probability distributions of the daily ICU occupancy levels resulting from ICU admission and discharge decisions. We experimentally evaluate our framework in a counterfactual simulation based on one year of historical data from 2019 from a medium-sized ICU in a German hospital. In that evaluation, we use a simple ICU management policy based on the probabilistic occupancy forecasts aiming at reducing the risk of running out of ICU capacity. The results show that following this policy would have avoided hitting critical occupancy levels by around 70% and would have had a smoothing effect on ICU occupancy levels.
https://aisel.aisnet.org/hicss-55/hc/process/6