Delivering adequate health care in the setting of the ongoing pandemic is challenging. Due to coronavirus disease 2019 (COVID-19), the Tokyo Metropolitan government has been forced to expand their acute health-care capacity corresponding to infectious diseases within a short period. Responding to this situation, health emergency and disaster experts of the Tokyo Disaster Medical Assistance Team took the initiative in creating a brief education course. We established the course for expanding infectious disease care capacity by a dedicated hands-on lecture for health professionals who are unfamiliar with infectious disease care in ordinary circumstances. Our lecture included the typical course of COVID-19, use of personal protective equipment, environmental sterilization, medical-ward zoning, and safe caregiving. Hospitals that received customized lectures reported by means of a questionnaire that the lectures were well suited to their needs. Currently, the health-care system in Tokyo has increased its capacity to meet the demand and has not been affected by COVID-19. Our experience shows that health emergency and disaster experts can assist hospitals in crisis by providing educational materials.

Developing an adequate disaster response capacity involves an assessment of available resources in areas that are vulnerable to disaster. Here, we sought to evaluate the gap between predicted damage in a foreseeable major earthquake versus existing municipality-level resources in Tokyo, Japan. Our study focused on the 53 municipalities in Tokyo to evaluate the relationships between the predicted number of severe casualties per 1,000 population from a future earthquake, community characteristics, and inpatient bed supply in local hospitals. Correlation analysis and supply–demand balance estimations were carried out at the municipality level, and the results were geographically visualized using choropleth maps. The correlation analysis showed that higher casualties were correlated with municipalities with faster population increase, higher taxable incomes, lower unemployment rates, and higher bed volumes in disaster base hospitals. Under a maximal damage scenario in a future earthquake, we predict a shortage of 2,780 beds for the treatment of severe casualties across Tokyo. Even under a scenario of cooperation among neighboring municipalities, a shortage of 7,107 beds would remain. Tokyo is located in a zone where major earthquake damage is anticipated. Cooperation between neighboring municipalities may not suffice to address the undersupply of beds during the acute phase of a disaster. Hence, existing disaster preparedness plans require further reinforcement with a focus on local vulnerabilities.

The integration of external staff into a hospital’s disaster response can present technical challenges. Although hospitals will always prefer to use their own staff in disaster response, there have been many historical examples where external staffing is required. During the 2016 Kumamoto Earthquakes, the Oita Prefectural Hospital required medical professionals to expand disaster response staff. They were able to identify 2 appropriate emergency physicians belonging to a remote hospital who had previously worked at the Oita Prefectural hospital. The physicians were effectively able to supplement the hospital staff, providing care for additional patients, and giving the existing on-duty staff some respite. Based on our experience, we suggest that hospital coalitions and disaster response authorities explore mechanisms of cross-credentialing and cross-training staff to make it easier to share staff in a disaster.

Aim: To evaluate the ease of use, reliability, and duplication risks of a new identification (ID) coding method, which works for mass casualty events such as disaster scenes.Methods: The new ID code consisted of 16 alphanumeric characters: seven characters for the responder's information and nine for the casualty, which can be created in a very low‐tech manner without using any electronic devices. A simulated triage was carried out for virtual causalities by students of the same grade at our university's medical school. Each participant was instructed to triage and create IDs for the same 10 virtual casualties. Eighty‐nine participants created a total of 890 IDs, which were examined for correct coding and ID duplication rates.Results: Despite situations in which the risk of duplication might be considered high, complete duplication of IDs occurred in only one case (0.2%), and the other 888 IDs (99.8%) were unique. The simulation was done in a reasonable amount of time without any confusion.Conclusions: In the mass casualty incident triage simulation, our new coding method proved easy and useful in creating IDs with an extremely low duplication rate. To develop this method for broader use, further evaluation is needed in more simulations and real disaster situations.