Background: Hospital surge capacity (HSC) is dependent on the ability to increase or conserve resources. The hospital surge model put forth by the Agency for Healthcare Research and Quality (AHRQ) estimates the resources needed by hospitals to treat casualties resulting from 13 national planning scenarios. However, emergency planners need to know which hospital resource are most critical in order to develop a more accurate plan for HSC in the event of a disaster.

Objective: To identify critical hospital resources required in four specific catastrophic scenarios; namely, pandemic influenza, radiation, explosive, and nerve gas.

Methods: We convened an expert consensus panel comprised of 23 participants representing health providers (i.e., nurses and physicians), administrators, emergency planners, and specialists. Four disaster scenarios were examined by the panel. Participants were divided into 4 groups of five or six members, each of which were assigned two of four scenarios. They were asked to consider 132 hospital patient care resources- extracted from the AHRQ’s hospital surge model- in order to identify the ones that would be critical in their opinion to patient care. The definition for a critical hospital resource was the following: absence of the resource is likely to have a major impact on patient outcomes, i.e., high likelihood of untoward event, possibly death. For items with any disagreement in ranking, we conducted a facilitated discussion (modified Delphi technique) until consensus was reached, which was defined as more than 50% agreement. Intraclass Correlation Coefficients (ICC) were calculated for each scenario, and across all scenarios as a measure of participant agreement on critical resources. For the critical resources common to all scenarios, Kruskal-Wallis test was performed to measure the distribution of scores across all scenarios.

Results: Of the 132 hospital resources, 25 were considered critical for all four scenarios by more than 50% of the participants. The number of hospital resources considered to be critical by consensus varied from one scenario to another; 58 for the pandemic influenza scenario, 51 for radiation exposure, 41 for explosives, and 35 for nerve gas scenario. Intravenous crystalloid solution was the only resource ranked by all participants as critical across all scenarios. The agreement in ranking was strong in nerve agent and pandemic influenza (ICC= 0.7 in both), and moderate in explosives (ICC= 0.6) and radiation (ICC= 0.5).

Conclusion: In four disaster scenarios, namely, radiation, pandemic influenza, explosives, and nerve gas scenarios; supply of as few as 25 common resources may be considered critical to hospital surge capacity. The absence of any these resources may compromise patient care. More studies are needed to identify critical hospital resources in other disaster scenarios.

Objective: To identify and prioritize potential Emergency Department (ED) and hospital-based interventions which could mitigate the impact of crowding during patient surge from a widespread infectious respiratory disease outbreak and determine potential data sources that may be useful for triggering decisions to implement these high priority interventions.

Design: Expert panel utilizing Nominal Group Technique to identify and prioritize interventions, and in addition, determine appropriate “triggers” for implementation of the high priority interventions in the context of four different infectious respiratory disease scenarios that vary by patient volumes (high versus low) and illness severity (high versus low).

Setting: One day in-person conference held November, 2011.

Participants: Regional and national experts representing the fields of public health, disease surveillance, clinical medicine, ED operations, and hospital operations.

Main Outcome Measure: Prioritized list of potential interventions to reduce ED and hospital crowding, respectively. In addition, we created a prioritized list of potential data sources which could be useful to trigger interventions.

Results: High priority interventions to mitigate ED surge included standardizing admission and discharge criteria and instituting infection control measures. To mitigate hospital crowding, panelists prioritized mandatory vaccination and an algorithm for antiviral use. Data sources identified for triggering implementation of these interventions were most commonly ED and hospital utilization metrics.

Conclusions: We developed a prioritized list of potentially useful interventions to mitigate ED and hospital crowding in various outbreak scenarios. The data sources identified to “trigger” the implementation of these high priority interventions consist mainly of sources available at the local, institutional level.