Limiting severe outcomes and impact on intensive care units of moderate-intermediate 2009 pandemic influenza: role of infectious diseases units

Flow-Sizing Critical Care Resources

OBJECTIVES

To describe the factors affecting critical care capacity and how critical care organizations (CCOs) within academic centers in the U.S. flow-size critical care resources under normal operations, strain, and surge conditions.

DATA SOURCES

PubMed, federal agency and American Hospital Association reports, and previous CCO survey results were reviewed.

STUDY SELECTION

Studies and reports of critical care bed capacity and utilization within CCOs and in the United States were selected.

DATA EXTRACTION

The Academic Leaders in the Critical Care Medicine Task Force established regular conference calls to reach a consensus on the approach of CCOs to "flow-sizing" critical care services.

DATA SYNTHESIS

The approach of CCOs to "flow-sizing" critical care is outlined. The vertical (relation to institutional resources, e.g., space allocation, equipment, personnel redistribution) and horizontal (interdepartmental, e.g., emergency department, operating room, inpatient floors) integration of critical care delivery (ICUs, rapid response) for healthcare organizations and the methods by which CCOs flow-size critical care during normal operations, strain, and surge conditions are described. The advantages, barriers, and recommendations for the rapid and efficient scaling of critical care operations via a CCO structure are explained. Comprehensive guidance and resources for the development of "flow-sizing" capability by a CCO within a healthcare organization are provided.

CONCLUSIONS

We identified and summarized the fundamental principles affecting critical care capacity. The taskforce highlighted the advantages of the CCO governance model to achieve rapid and cost-effective "flow-sizing" of critical care services and provide recommendations and resources to facilitate this capability. The relevance of a comprehensive approach to "flow-sizing" has become particularly relevant in the wake of the latest COVID-19 pandemic. In light of the growing risks of another extreme epidemic, planning for adequate capacity to confront the next critical care crisis is urgent.

Population Effects of Influenza A(H1N1) Pandemic among Health Plan Members, San Diego, California, USA, October-December 2009

Lacking population-specific data, activity of seasonal and pandemic influenza is usually tracked by counting the number of diagnoses and visits to medical facilities above a baseline. This type of data does not address the delivery of services in a specific population. To provide population-specific data, this retrospective study of patients with influenza-like illness, influenza, and pneumonia among members of a Kaiser Permanente health plan in San Diego, California, USA, during October-December 2009 was initiated. Population data included the number of outpatients accessing healthcare; the number of patients diagnosed with pneumonia; antimicrobial therapy administered; number of patients hospitalized with influenza, influenza-like illness, or pneumonia; level of care provided; and number of patients requiring specialized treatments (e.g., oxygen, ventilation, vasopressors). The rate of admissions specific to weeks and predictions of 2 epidemiologic models shows the strengths and weaknesses of those tools. Data collected in this study may improve planning for influenza pandemics.