Orchestrating performance of healthcare networks subjected to the compound events of natural disasters and pandemic

Prioritized reconstruction of healthcare facilities after earthquakes based on recovery of emergency services

Earthquakes can disrupt the healthcare system heavily, leading to long wait times and many untreated patients for years after the event. Emergency services, in particular, must return to preearthquake functionality as soon as possible such that patients, especially critically injured ones, can be treated promptly. However, reconstruction and restoration of emergency services can take years. Due to limited reconstruction resources, decision-makers cannot reconstruct all hospitals simultaneously. They are typically forced to prioritize the reconstruction order, and this process is often poorly planned. This article models emergency services as an M/M/s queuing system that accounts for prioritized treatment of critical patients and formulates a greedy algorithm to plan for an effective healthcare system reconstruction. The algorithm finds the reconstruction ordering of hospital buildings such that emergency patients have the shortest time to receiving medical care possible. We show our greedy algorithm's good performance for small problem instances, with average deviations from the optimal solution below 16%. Further, we apply our methodology to a case study of Lima, Peru, under a hypothetical M8.0 earthquake. The application demonstrates that compared to typically implemented policies, a policy guided by our formulation results in shorter time to treatment and reduces the number of untreated patients over the course of the reconstruction period by more than a factor of 3 in a worst-case scenario with 70% hospital capacity disruption. Finally, we demonstrate that our formulation can be integrated into risk analysis through Monte Carlo simulations to inform decision-makers of reconstruction plans after future earthquakes.

Wildfire Threat to Inpatient Health Care Facilities in California, 2022

Objectives. To assess wildfire risks to California inpatient health care facilities in 2022. Methods. Locations of inpatient facilities and associated inpatient bed capacities were mapped in relation to California Department of Forestry and Fire Protection fire threat zones (FTZs), which combine expected fire frequency with potential fire behavior. We computed the distances of each facility to the nearest high, very high, and extreme FTZs. Results. Half (107 290 beds) of California's total inpatient capacity is within 0.87 miles of a high FTZ and 95% (203 665 beds) is within 3.7 miles of a high FTZ. Half of the total inpatient capacity is within 3.3 miles of a very high FTZ and 15.5 miles of an extreme FTZ. Conclusions. Wildfires threaten a large number of inpatient health care facilities in California. In many counties, all health care facilities may be at risk. Public Health Implications. Wildfires in California are rapid-onset disasters with short preimpact phases. Policies should address facility-level preparedness including smoke mitigation, sheltering measures, evacuation procedures, and resource allocation. Regional evacuation needs, including access to emergency medical services and patient transportation, must also be considered. (Am J Public Health. 2023;113(5):555-558. https://doi.org/10.2105/AJPH.2023.307236).

Cascading failures and resilience optimization of hospital infrastructure systems against the COVID-19

The outbreak of the Coronavirus Disease 2019 (COVID-19) has put the resilience of a country's healthcare infrastructure to the most severe test. The challenge of taking emergency measures to optimize the supply of medical resources and effectively meet the medical needs of residents is an important issue that needs to be resolved urgently in the prevention and control of public health emergencies. This paper analyzes cascading failures and optimization of the resilience of the hospital infrastructure system (HIS) with the presence of the COVID-19. It proposes a propagation model to describe the COVID-19 infectious process and establishes a cascading failure model of a HIS to analyze its failure mechanism. It also proposes a method for optimizing the resilience of HIS. Then the supplies and demands in maintaining the operations of HIS are studied, and a restoration strategy is obtained. Finally, simulation analysis of the spread of the COVID-19 is carried out to illustrate the applicability of the proposed method.

A framework to assess potential health system resilience using fuzzy logic

Objectives

To develop and test a framework to assess the potential of public health systems to maintain a resilient performance.

Methods

Quantitative data from public databases and qualitative data from technical reports of Brazilian health authorities were used to develop the framework which was assessed and modified by experts. Fuzzy logic was used for the mathematical model to determine scores for four resilient abilities - monitoring, anticipation, learning, and response - and an aggregated coefficient of resilient potential in health care. The coefficient measures used data from before the coronavirus disease 2019 (COVID-19) pandemic. These were compared with measures of the actual performance of health systems in 10 cities in Brazil during the pandemic.

Results

The coefficient of resilient potential in health care showed that the cities most affected by COVID-19 had lower potential for resilient performance before the pandemic. Some local health systems had adequate response capabilities, but other abilities were not well developed, which adversely affected the management of the spread of COVID-19.

Conclusions

The coefficient of resilient potential in health care is useful to indicate important areas for resilient performance and the different types of resilience capacities that can be considered in different contexts and levels of public health systems. Regular assessment of the potential of health systems for resilient performance would help highlight opportunities for continuous improvement in health system functions during chronic stress situations, which could strengthen their ability to keep functioning in the face of sudden disturbances.

A Perspective towards Multi-Hazard Resilient Systems: Natural Hazards and Pandemics

The concept of resilience has been used extensively across the sciences in engineering and the humanities. It is applied to ecology, medicine, economics, and psychology. The novel coronavirus disease (COVID-19) has posed an extraordinary challenge to the resilience of healthcare systems, communities, and nations and has profoundly altered our previous day-to-day operations. This paper presents a discussion of the definitions and characteristics of resilient systems. Scenarios are utilized to qualitatively explore key relationships, responses, and paths for recovery across different system types. The purpose is to develop an integrated approach that can accommodate simultaneous threats to system resilience, in particular, impacts from a natural hazard in conjunction with COVID-19. This manuscript is the first to advocate for more in-depth and quantitative research utilizing transdisciplinary approaches that can accommodate considerations across our built environment and healthcare system infrastructures in pursuit of designing systems that are resilient to both natural hazards and pandemic impacts.