Moving Forward from COVID-19: Organizational Dimensions of Effective Hospital Emergency Management.

Federal investment in emergency preparedness has increased notably since the 9/11 attacks, yet it is unclear if and how US hospital readiness has changed in the 20 years since then. In particular, understanding effective aspects of hospital emergency management programs is essential to improve healthcare systems' readiness for future disasters. The authors of this article examined the state of US hospital emergency management, focusing on the following question: During the COVID-19 pandemic, what aspects of hospital emergency management, including program components and organizational characteristics, were most effective in supporting and improving emergency preparedness and response? We conducted semistructured interviews of emergency managers and leaders at 12 urban and rural hospitals across the country. Through qualitative analysis of content derived from examination of transcripts from our interviews, we identified 7 dimensions of effective healthcare emergency management: (1) identify capable leaders; (2) assure robust institutional support; (3) design effective, tiered communications systems; (4) embrace the hospital incident command system to delineate roles and responsibilities; (5) actively promote collaboration and team building; (6) appreciate the necessity of training and exercises; and (7) balance structure and flexibility. These dimensions represent the unique and critical intersection of organizational factors and emergency management program characteristics at the core of hospital emergency preparedness and response. Extending these findings, we provide several recommendations for hospitals to better develop and sustain what we call a response culture in supporting effective emergency management.

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases.

Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.