Managing Interhospital Referrals During a COVID-19 Patient Surge in Japan: Creating Available Beds by Exchanging Patients.

A COVID-19 patient surge in Japan from July to September 2021 caused a mismatch between patient severity and bed types because hospital beds were fully occupied and patient referrals between hospitals stagnated. Japan's predominantly private healthcare system lacks effective mechanisms to coordinate healthcare providers to address the mismatch. To address the surge, in August 2021, Tokyo Saiseikai Central Hospital started a scheme to exchange patients with other hospitals to mitigate the mismatch. In this article, we outline a retrospective observational study using medical records from a tertiary care medical center that treated severe COVID-19 cases. We describe daily patient admissions to our hospital's COVID-19 beds from July to September 2021, and compared the moving average of daily admissions before and after the exchange scheme was introduced. Bed occupancy reached nearly 100% in late July when the patient surge began and continued to exceed 100% in August when the surge peaked. However, the average daily admission did not decrease in August compared with July: the median daily admission (25th to 75th percentile) during each period was 2 (1 to 2.5) in late July and 3 (2 to 4) in August. The number of patients referred in from secondary care hospitals and the number of patients referred out was balanced in August. During the patient surge, the exchange scheme enabled the hospital to maintain and even increase the number of new admissions despite the bed shortage. Coordinating patient referrals in both directions simultaneously, rather than the usual 1-way transfer, can mitigate such mismatches.

Hospital at Home for Elderly COVID-19 Patients: A Preliminary Report with 100 Patients.

Hospital-at-home (HaH) care is useful for patients with COVID-19 and an alternative strategy when hospital capacity is under pressure due to patient surges. However, the efficacy and safety of HaH in elderly patients with COVID-19 remain unknown. In Kyoto city, we conducted a retrospective medical record review of HaH care focused on elderly COVID-19 patients from 4 February to 25 June 2021. Eligible patients were (1) COVID-19 patients aged ≥70 years and those who lived with them or (2) COVID-19 patients aged <70 years with special circumstances and those who lived with them. During the study period, 100 patients received HaH care. Their median age was 76 years (interquartile range 56-83), and 65% were over 70 years. Among 100 patients, 36 (36%) had hypoxia (oxygen saturation ≤ 92%), 21 (21%) received steroid medication, and 34 (34%) received intravenous fluids. Although 22 patients were admitted to the hospital and 3 patients died there, no patients died during HaH care. HaH care may be safe and effective in elderly patients with COVID-19. Our study shows that HaH provides an alternative strategy for treating COVID-19 patients and can reduce the healthcare burden at hospitals.

Reducing patient surge: community based social networks as first responders.

A major challenge for health services worldwide is in providing adequate medical care during mass disasters. The ongoing COVID-19 pandemic highlights this difficulty. Patient surge, a consequence of most types of disasters that contribute to trauma experiences, is a primary factor in disrupting such care as it is composed of worried well persons and those experiencing psychosocial trauma that can severely disrupt and overwhelm effective acute hospital based health care. We review the literature and propose a potential solution framework to reduce such a surge that relies on exploiting community social networks as first responders. We utilize and integrate literature based evidence on patient surge, community disaster behaviors and community based informal social networks to examine reasons for patient surge to hospitals. We then propose that leveraging community based social networks as a potent deterrent for non-critically injured, especially those who have experienced psychosocial trauma or the worried well, from seeking hospital care during ongoing disasters. By emphasizing the social capital inherent in community based social networks, this perspective posits an alternative cost-effective means of reducing patient surge.

Correlation between Emergency Medical Services Suspected COVID-19 Patients and Daily Hospitalizations.

Objective: We sought to determine if Emergency Medical Services (EMS) identified Persons Under Investigation (PUI) for COVID-19 are associated with hospitalizations for COVID-19 disease for the purposes of serving as a potential early indicator of hospital surge. Methods: A retrospective analysis was conducted using data from the Maryland statewide EMS electronic medical records and daily COVID-19 hospitalizations from March 13, 2020 through July 31, 2020. All unique EMS patients who were identified as COVID-19 PUIs during the study period were included. Descriptive analysis was performed. The Box-Jenkins approach was used to evaluate the relationship between EMS transports and daily new hospitalizations. Separate Auto Regressive Integrated Moving Average (ARIMA) models were constructed to transform the data into a series of independent, identically distributed random variables. Fit was measured using the Akaike Information Criterion (AIC). The Box-Ljung white noise test was utilized to ensure there was no autocorrelation in the residuals. Results: EMS units in Maryland identified a total of 26,855 COVID-19 PUIs during the 141-day study period. The median patient age was 62 years old, and 19,111 (71.3%) were 50 years and older. 6,886 (25.6%) patients had an abnormal initial pulse oximetry (<92%). A strong degree of correlation was observed between EMS PUI transports and new hospitalizations. The correlation was strongest and significant at a 9-day lag from time of EMS PUI transports to new COVID-19 hospitalizations, with a cross correlation coefficient of 0.26 (p < .01). Conclusions: A strong correlation between EMS PUIs and COVID-19 hospitalizations was noted in this state-wide analysis. These findings demonstrate the potential value of incorporating EMS clinical information into the development of a robust syndromic surveillance system for COVID-19. This correlation has important utility in the development of predictive tools and models that seek to provide indicators of an impending surge on the healthcare system at large.