Predicting Clinical Deterioration in Hospitals

• URL: http://arxiv.org/abs/2102.05856v1
• Date: Thu, 11 Feb 2021 06:03:36 GMT
• Title: Predicting Clinical Deterioration in Hospitals
• Authors: Laleh Jalali, Hsiu-Khuern Tang, Richard H. Goldstein, Joaqun Alvarez Rodrguez
• Abstract summary: We apply machine learning to electronic medical records to infer if patients are at risk for clinical deterioration. If successful, hospitals can integrate our approach into their existing IT systems and use the alerts generated by the model to prevent ICU transfer, cardiac arrest, or death.
• Score: 0.8329456268842225
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Responding rapidly to a patient who is demonstrating signs of imminent clinical deterioration is a basic tenet of patient care. This gave rise to a patient safety intervention philosophy known as a Rapid Response System (RRS), whereby a patient who meets a pre-determined set of criteria for imminent clinical deterioration is immediately assessed and treated, with the goal of mitigating the deterioration and preventing intensive care unit (ICU) transfer, cardiac arrest, or death. While RRSs have been widely adopted, multiple systematic reviews have failed to find evidence of their effectiveness. Typically, RRS criteria are simple, expert (consensus) defined rules that identify significant physiologic abnormalities or are based on clinical observation. If one can find a pattern in the patient's data earlier than the onset of the physiologic derangement manifest in the current criteria, intervention strategies might be more effective. In this paper, we apply machine learning to electronic medical records (EMR) to infer if patients are at risk for clinical deterioration. Our models are more sensitive and offer greater advance prediction time compared with existing rule-based methods that are currently utilized in hospitals. Our results warrant further testing in the field; if successful, hospitals can integrate our approach into their existing IT systems and use the alerts generated by the model to prevent ICU transfer, cardiac arrest, or death, or to reduce the ICU length of stay.

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