Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories.

OBJECTIVES: To assist with planning hospital resources, including critical care (CC) beds, for managing patients with COVID-19. METHODS: An individual simulation was implemented in Microsoft Excel using a discretely integrated condition event simulation. Expected daily cases presented to the emergency department were modeled in terms of transitions to and from ward and CC and to discharge or death. The duration of stay in each location was selected from trajectory-specific distributions. Daily ward and CC bed occupancy and the number of discharges according to care needs were forecast for the period of interest. Face validity was ascertained by local experts and, for the case study, by comparing forecasts with actual data. RESULTS: To illustrate the use of the model, a case study was developed for Guy's and St Thomas' Trust. They provided inputs for January 2020 to early April 2020, and local observed case numbers were fit to provide estimates of emergency department arrivals. A peak demand of 467 ward and 135 CC beds was forecast, with diminishing numbers through July. The model tended to predict higher occupancy in Level 1 than what was eventually observed, but the timing of peaks was quite close, especially for CC, where the model predicted at least 120 beds would be occupied from April 9, 2020, to April 17, 2020, compared with April 7, 2020, to April 19, 2020, in reality. The care needs on discharge varied greatly from day to day. CONCLUSIONS: The DICE simulation of hospital trajectories of patients with COVID-19 provides forecasts of resources needed with only a few local inputs. This should help planners understand their expected resource needs.

Timing social distancing to avert unmanageable COVID-19 hospital surges.

Following the April 16, 2020 release of the Opening Up America Again guidelines for relaxing coronavirus disease 2019 (COVID-19) social distancing policies, local leaders are concerned about future pandemic waves and lack robust strategies for tracking and suppressing transmission. Here, we present a strategy for triggering short-term shelter-in-place orders when hospital admissions surpass a threshold. We use stochastic optimization to derive triggers that ensure hospital surges will not exceed local capacity and lockdowns are as short as possible. For example, Austin, Texas-the fastest-growing large city in the United States-has adopted a COVID-19 response strategy based on this method. Assuming that the relaxation of social distancing increases the risk of infection sixfold, the optimal strategy will trigger a total of 135 d (90% prediction interval: 126 d to 141 d) of sheltering, allow schools to open in the fall, and result in an expected 2,929 deaths (90% prediction interval: 2,837 to 3,026) by September 2021, which is 29% of the annual mortality rate. In the months ahead, policy makers are likely to face difficult choices, and the extent of public restraint and cocooning of vulnerable populations may save or cost thousands of lives.

Swift and Low-Cost Surge in Chemical Threat Response: Is It Possible? The Experience of a French Hospital.

In 2016 France hosted the European football championship. In a context of an increased terrorist threat, Chemical, Bacteriological, Radiological, Nuclear (CBRN) attacks were considered possible. Three days prior to the beginning of the event, the Health Authorities required that a medium sized hospital close to a major potential target, prepare a chemical decontamination centre. Despite a low level of preparedness, little external help, and very few extra resources, an efficient decontamination chain (all premises necessary for the management of contaminated victims: from the entrance gate to the post-decontamination dressing cabins) was set up in 15 days (12 days after the unrealistic deadline). Numerous practical measures allowed three persons in CBRN personal protective equipment (PPE) to manage the whole chain, providing a maximum flow of 24 persons/hour. Volunteers were trained in PPE dressing, undressing and in decontamination procedures. This experience, offers a novel paradigm in managing chemical decontamination, in terms of attitude, and with adaptations to overcome practical constraints. It demonstrates that it is possible to set up a decontamination chain rapidly at very low cost. This provides an attractive option for less advanced countries and in humanitarian contexts. Some additional refinements, enhancements may be considered to further improve results. (Disaster Med Public Health Preparedness. 2018;12:649-656).

Microsimulation of financial impact of demand surge on hospitals: the H1N1 influenza pandemic of fall 2009.

OBJECTIVE: Microsimulation was used to assess the financial impact on hospitals of a surge in influenza admissions in advance of the H1N1 pandemic in the fall of 2009. The goal was to estimate net income and losses (nationally, and by hospital type) of a response of filling unused hospital bed capacity proportionately and postponing elective admissions (a "passive" supply response). METHODS: Epidemiologic assumptions were combined with assumptions from other literature (e.g., staff absenteeism, profitability by payer class), Census data on age groups by region, and baseline hospital utilization data. Hospital discharge records were available from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (NIS). Hospital bed capacity and staffing were measured with the American Hospital Association's (AHA) Annual Survey. RESULTS: Nationwide, in a scenario of relatively severe epidemiologic assumptions, we estimated aggregate net income of $119 million for about 1 million additional influenza-related admissions, and a net loss of $37 million for 52,000 postponed elective admissions. IMPLICATIONS: Aggregate and distributional results did not suggest that a policy of promising additional financial compensation to hospitals in anticipation of the surge in flu cases was necessary. The analysis identified needs for better information of several types to improve simulations of hospital behavior and impacts during demand surges.

Effect of a mass casualty incident: clinical outcomes and hospital charges for casualty patients versus concurrent inpatients.

OBJECTIVES: A mass casualty incident (MCI) may strain a health care system beyond surge capacity, affecting patterns of care for casualties and other patients. Prior studies of MCIs have assessed clinical care for casualty patients, but have not examined outcomes or expenditures for noncasualty inpatients in the same time period. METHODS: This was a retrospective analysis of administrative hospital claims in a state where an MCI with over 200 casualties occurred; two hospitals that admitted casualties of >5% of their inpatient capacity were studied. The "surge period" was defined as 7 days after the MCI. Using diagnostic codes, patients admitted on the MCI day with diagnoses of burns or inhalation injury were included in the "MCI surge cohort." Patients admitted within a time frame of 7 days prior to 7 days after the MCI who were inpatients during the surge period were included in the "non-MCI surge cohort." The authors compared the MCI and non-MCI surge cohorts to a mutually exclusive reference cohort (all inpatients during 6 weeks prior to the MCI), regarding key outcomes of hospital length of stay (LOS) and hospital charges adjusted for age, sex, race/ethnicity, and severity of illness. RESULTS: Fifty-five patients met criteria for the MCI surge cohort, 1,369 for the non-MCI surge cohort, and 5,980 for the reference group. Compared with the reference group and adjusted for covariates, the mean (±SD) hospital LOS was 4.90 (±1.85) days longer for the MCI surge cohort (95% confidence interval [CI] = 1.67 to 8.84) and 1.34 (±0.16) days longer for the non-MCI surge cohort (95% CI = 1.00 to 1.65). The MCI cohort also had significantly longer mean hospital LOS than the non-MCI surge cohort (difference = 3.56 days; 95% CI = 0.36 to 7.36). Also adjusted for covariates, mean (±SD) total hospital charges for the MCI surge cohort were $22,349 (±$8,342) greater than for the reference group (95% CI = $8,182 to $39,485). Mean (±SD) charges for the non-MCI surge cohort were $4,028 (±$633) greater than for the reference group (95% CI = $2,792 to $5,196). The MCI cohort also had higher mean total charges than the non-MCI surge cohort (difference = $18,321; 95% CI = $4,488 to $34,980). CONCLUSIONS: When adjusted for severity of illness, casualty patients and noncasualty patients receiving concurrent hospital care have significantly longer LOS and higher charges than typical hospital patients at times unaffected by MCIs. Spillover effects from MCIs for noncasualty patients have not been previously described and have implications for clinical and hospital management in MCI and other high-surge circumstances.