Digital transformation to mitigate emergency situations: increasing opioid overdose survival rates through explainable artificial intelligence

Abstract

Purpose

The global health crisis represents an unprecedented opportunity for the development of artificial intelligence (AI) solutions. This paper aims to integrate explainable AI into the decision-making process in emergency scenarios to help mitigate the high levels of complexity and uncertainty associated with these situations. An AI solution is designed to extract insights into opioid overdose (OD) that can help government agencies to improve their medical emergency response and reduce opioid-related deaths.

Design/methodology/approach

This paper employs the design science research paradigm as an overarching framework. Open-access digital data and AI, two essential components within the digital transformation domain, are used to accurately predict OD survival rates.

Findings

The proposed AI solution has two primary implications for the advancement of informed emergency management. Results show that it can help not only local agencies plan their resources for timely response to OD incidents, thus improving survival rates, but also governments to identify geographical areas with lower survival rates and their primary contributing factor; hence, they can plan and allocate long-term resources to increase survival rates and help in developing effective emergency-related policies.

Originality/value

This paper illustrates that digital transformation, particularly open-access digital data and AI, can improve the emergency management framework (EMF). It also demonstrates that the AI models developed in this study can identify opioid OD trends and determine the significant factors improving survival rates.

Citation

Johnson, M., Albizri, A., Harfouche, A. and Tutun, S. (2023), "Digital transformation to mitigate emergency situations: increasing opioid overdose survival rates through explainable artificial intelligence", Industrial Management & Data Systems, Vol. 123 No. 1, pp. 324-344. https://doi.org/10.1108/IMDS-04-2021-0248