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1.
Am J Disaster Med ; 16(3): 179-192, 2021.
Article in English | MEDLINE | ID: covidwho-1572826

ABSTRACT

OBJECTIVE: Many hospitals were unprepared for the surge of patients associated with the spread of coronavirus disease 2019 (COVID-19) pandemic. We describe the processes to develop and implement a surge plan framework for resource allocation, staffing, and standardized management in response to the COVID-19 pandemic across a large integrated regional healthcare system. SETTING: A large academic medical center in the Cleveland metropolitan area, with a network of 10 regional hospitals throughout Northeastern Ohio with a daily capacity of more than 500 intensive care unit (ICU) beds. RESULTS: At the beginning of the pandemic, an equitable delivery of healthcare services across the healthcare system was developed. This distribution of resources was implemented with the potential needs and resources of the individual ICUs in mind, and epidemiologic predictions of virus transmissibility. We describe the processes to develop and implement a surge plan framework for resource allocation, staffing, and standardized management in response to the COVID-19 pandemic across a large integrated regional healthcare system. We also describe an additional level of surge capacity, which is available to well-integrated institutions called "extension of capacity." This refers to the ability to immediately have access to the beds and resources within a hospital system with minimal administrative burden. CONCLUSIONS: Large integrated hospital systems may have an advantage over individual hospitals because they can shift supplies among regional partners, which may lead to faster mobilization of resources, rather than depending on local and national governments. The pandemic response of our healthcare system highlights these benefits.


Subject(s)
COVID-19 , Surge Capacity , Critical Care , Delivery of Health Care , Hospital Bed Capacity , Humans , Intensive Care Units , Pandemics , SARS-CoV-2
2.
J Nurs Adm ; 51(11): 573-578, 2021 Nov 01.
Article in English | MEDLINE | ID: covidwho-1504564

ABSTRACT

The ability to respond effectively and efficiently during times of crisis, including a pandemic, has emerged as a competency for nurse leaders. This article describes one institution's experience using the American Organization of Nurse Leaders Competencies for Nurse Executives in operationalizing the concept of surge capacity.


Subject(s)
Communication , Health Plan Implementation , Nurse Administrators/organization & administration , Professional Competence , Surge Capacity/organization & administration , COVID-19 , Chicago , Humans , United States
3.
Health Secur ; 19(5): 479-487, 2021.
Article in English | MEDLINE | ID: covidwho-1467289

ABSTRACT

Japan has the highest proportion of older adults worldwide but has fewer critical care beds than most high-income countries. Although the COVID-19 infection rate in Japan is low compared with Europe and the United States, by the end of 2020, several infected people died in ambulances because they could not find hospitals to accept them. Our study aimed to examine the Japanese healthcare system's capacity to accommodate critically ill COVID-19 patients during the pandemic. We created a model to estimate bed and staff capacity at 3 levels of pandemic response (conventional, contingency, and crisis), as defined by the US National Academy of Medicine, and the function of Japan's healthcare system at each level. We then compared our estimates of the number of COVID-19 patients requiring intensive care at peak times with the national health system capacity using expert panel data. Our findings suggest that Japan's healthcare system currently can accommodate only a limited number of critically ill COVID-19 patients. It could accommodate the surge of pandemic demands by converting nonintensive care unit beds to critical care beds and using nonintensive care unit staff for critical care. However, bed and staff capacity should not be expanded uniformly, so that the limited number of physicians and nurses are allocated efficiently and so staffing does not become the bottleneck of the expansion. Training and deploying physicians and nurses to provide immediate intensive care is essential. The key is to introduce and implement the concept and mechanism of tiered staffing in the Japanese healthcare system. More importantly, most intensive care facilities in Japanese hospitals are small-scaled and thinly distributed in each region. The government needs to introduce an efficient system for smooth dispatching of medical personnel among hospitals regardless of their founding institutions.


Subject(s)
COVID-19 , Surge Capacity , Aged , Critical Care , Humans , Intensive Care Units , Japan/epidemiology , Pandemics , SARS-CoV-2 , United States
4.
Anaesthesist ; 70(7): 582-597, 2021 07.
Article in German | MEDLINE | ID: covidwho-1453677

ABSTRACT

BACKGROUND AND OBJECTIVE: During the initial phase of the COVID-19 pandemic the government of the state of Bavaria, Germany, declared a state of emergency for its entire territory for the first time in history. Some areas in eastern Bavaria were among the most severely affected communities in Germany, prompting authorities and hospitals to build up capacities for a surge of COVID-19 patients. In some areas, intensive care unit (ICU) capacities were heavily engaged, which occasionally made a redistribution of patients necessary. MATERIAL AND METHODS: For managing COVID-19-related hospital capacities and patient allocation, crisis management squads in Bavaria were expanded by disaster task force medical officers ("Ärztlicher Leiter Führungsgruppe Katastrophenschutz" [MO]) with substantial executive authority. The authors report their experiences as MO concerning the superordinate patient allocation management in the district of Upper Palatinate (Oberpfalz) in eastern Bavaria. RESULTS: By abandoning routine patient care and building up additional ICU resources, surge capacity for the treatment of COVID-19 patients was generated in hospitals. In parts of the Oberpfalz, ICU capacities were almost entirely occupied by patients with corona virus infections, making reallocation to other hospitals within the district and beyond necessary. The MO managed patient pathways in an escalating manner by defining local (within the region of responsibility of a single MO), regional (within the district), and cross-regional (over district borders) reallocation lanes, as needed. When regional or cross-regional reallocation lanes had to be established, an additional management level located at the district government was involved. Within the determined reallocation lanes, emitting and receiving hospitals mutually agreed on any patient transfer without explicitly involving the MO, thereby maintaining the established interhospital routine transfer procedures. The number of patients and available treatment resources at each hospital were monitored with the help of a web-based treatment capacity registry. If indicated, reallocation lanes were dynamically revised according to the present situation. To oppose further virus spreading in nursing homes, the state government prohibited patient allocation to these facilities, which led to considerably longer hospital length of stay of convalescent elderly and/or dependent patients. In parallel to the flattening of the COVID-19 incidence curve, routine hospital patient care could be re-established in a stepwise manner. CONCLUSION: Patient allocation during the state of emergency by the MO sought to keep up routine interhospital reallocation procedures as much as possible, thereby reducing management time and effort. Occasionally, difficulties were observed during patient allocations crossing district borders, if other MO followed different management principles. The nursing home blockade and conflicting financial interests of hospitals posed challenges to the work of the disaster task force medical officers.


Subject(s)
COVID-19 , Decision Making, Organizational , Pandemics , Surge Capacity/organization & administration , Critical Care , Disease Management , Emergency Service, Hospital , Germany , Humans , Intensive Care Units , Length of Stay , Nursing Homes , Patient Transfer , Research Report , Resource Allocation
5.
Dimens Crit Care Nurs ; 40(6): 345-354, 2021.
Article in English | MEDLINE | ID: covidwho-1450453

ABSTRACT

INTRODUCTION: The ability of an organization to accommodate a large influx of patients during a prolonged period is dependent on surge capacity. The aim of this article is to describe the surge experience with space, supplies, and staff training in response to COVID-19 and provide guidance to other organizations. BACKGROUND: A hospital's response to a large-scale event is greatly impacted by the ability to surge and, depending on the type of threat, to maintain a sustained response. To identify surge capacity, an organization must first consider the type of event to appropriately plan resources. PREPARATION PROCESS: An epidemic surge drill, conducted in 2012, served as a guide in planning for the COVID-19 pandemic. The principles of crisis standards of care and a hospital incident command structure were used to clearly define roles, open lines of communication, and inform our surge plan. Preparation began by collaborating with multidisciplinary groups to acquire the most appropriate space, as well as adequate supplies, and identify and train staff. IMPLEMENTATION: Teams were formed to identify the necessary resources to expand the intensive care unit (ICU) environment quickly and efficiently. Educational training was developed for redeployed staff. OUTCOMES: Beth Israel Deaconess Medical Center experienced the largest surge of ICU patients within a hospital system in the state of Massachusetts. The ICU capacity was expanded by 93% from 77 to 149 beds, and the surge was maintained for approximately 9 weeks. Shadowing experiences before the actual surge were extremely valuable. CONCLUSIONS: Planning for the surge of critically ill patients required a thoughtful, collaborative approach. Ongoing staff support and communication from nursing leadership were necessary to ensure safe, effective care for critically ill patients in a new and dynamic environment.


Subject(s)
COVID-19 , Pandemics , Humans , Intensive Care Units , SARS-CoV-2 , Surge Capacity
6.
J Nurs Adm ; 51(10): 500-506, 2021 Oct 01.
Article in English | MEDLINE | ID: covidwho-1434561

ABSTRACT

Like any disaster, the COVID-19 pandemic has presented significant challenges to healthcare systems, especially the threat of insufficient bed capacity and resources. Hospitals have been required to plan for and implement innovative approaches to expand hospital inpatient and intensive care capacity. This article presents how one of the largest healthcare systems in the United States leveraged existing technology infrastructure to create a virtual hospital that extended care beyond the walls of the "brick and mortar" hospital.


Subject(s)
COVID-19 , Delivery of Health Care/organization & administration , Home Care Services, Hospital-Based/organization & administration , Hospitals , Surge Capacity/organization & administration , Telemedicine/organization & administration , Humans , Quality of Health Care , SARS-CoV-2 , Telemedicine/methods , United States/epidemiology
7.
PLoS One ; 16(9): e0257567, 2021.
Article in English | MEDLINE | ID: covidwho-1430542

ABSTRACT

INTRODUCTION: The COVID-19 pandemic continues to overwhelm health systems across the globe. We aimed to assess the readiness of hospitals in Nigeria to respond to the COVID-19 outbreak. METHOD: Between April and October 2020, hospital representatives completed a modified World Health Organisation (WHO) COVID-19 hospital readiness checklist consisting of 13 components and 124 indicators. Readiness scores were classified as adequate (score ≥80%), moderate (score 50-79.9%) and not ready (score <50%). RESULTS: Among 20 (17 tertiary and three secondary) hospitals from all six geopolitical zones of Nigeria, readiness score ranged from 28.2% to 88.7% (median 68.4%), and only three (15%) hospitals had adequate readiness. There was a median of 15 isolation beds, four ICU beds and four ventilators per hospital, but over 45% of hospitals established isolation facilities and procured ventilators after the onset of COVID-19. Of the 13 readiness components, the lowest readiness scores were reported for surge capacity (61.1%), human resources (59.1%), staff welfare (50%) and availability of critical items (47.7%). CONCLUSION: Most hospitals in Nigeria were not adequately prepared to respond to the COVID-19 outbreak. Current efforts to strengthen hospital preparedness should prioritize challenges related to surge capacity, critical care for COVID-19 patients, and staff welfare and protection.


Subject(s)
COVID-19/epidemiology , Hospitals/statistics & numerical data , Pandemics , Surveys and Questionnaires , Hospitals/supply & distribution , Humans , Nigeria/epidemiology , Surge Capacity
11.
Health Policy ; 125(10): 1291-1296, 2021 10.
Article in English | MEDLINE | ID: covidwho-1368662

ABSTRACT

As of September 1st 2020, over 42 000 COVID-19 cases and 2 800 COVID-19-related deaths have been confirmed in Ontario, Canada. Testing enables quick identification of cases, which results in effective contact tracing and containment of virus spread. Faced with a lack of surge capacity in the public health laboratory system at the start, health officials implemented changes to testing and laboratory infrastructure to significantly expand testing capacity to include 1) the centralization of resources; and 2) the integration of private and independent labs into the COVID-19 testing program. With these changes, testing capacity has grown from approximately 4,000/day in March to 32,000/day by the end of August, 2020. Eligibility criteria for testing has expanded to increase sensitivity and include testing of asymptomatic individuals. Along with previous outbreaks, the COVID-19 pandemic has highlighted the need for integration of testing surge capacity in public health systems before outbreaks occur. This paper details the development and implementation of a COVID-19 testing program in Ontario from January 2020 to September 2020 during the first-wave of the pandemic. The goal of this analysis is to explore the historical precedence, present influences, and future implications of the program.


Subject(s)
COVID-19 , Pandemics , COVID-19 Testing , Humans , Ontario , SARS-CoV-2 , Surge Capacity
12.
Int J Environ Res Public Health ; 18(15)2021 07 22.
Article in English | MEDLINE | ID: covidwho-1346473

ABSTRACT

The management of emergencies consists of a chain of actions with the support of staff, stuff, structure, and system, i.e., surge capacity. However, whenever the needs exceed the present resources, there should be flexibility in the system to employ other resources within communities, i.e., flexible surge capacity (FSC). This study aimed to investigate the possibility of creating alternative care facilities (ACFs) to relieve hospitals in Bangkok, Thailand. Using a Swedish questionnaire, quantitative data were compiled from facilities of interest and were completed with qualitative data obtained from interviews with key informants. Increasing interest to take part in a FSC system was identified among those interviewed. All medical facilities indicated an interest in offering minor treatments, while a select few expressed interest in offering psychosocial support or patient stabilization before transport to major hospitals and minor operations. The non-medical facilities interviewed proposed to serve food and provide spaces for the housing of victims. The lack of knowledge and scarcity of medical instruments and materials were some of the barriers to implementing the FSC response system. Despite some shortcomings, FSC seems to be applicable in Thailand. There is a need for educational initiatives, as well as a financial contingency to grant the sustainability of FSC.


Subject(s)
Disaster Planning , Surge Capacity , Emergencies , Feasibility Studies , Humans , Thailand
13.
Value Health ; 24(11): 1570-1577, 2021 11.
Article in English | MEDLINE | ID: covidwho-1340749

ABSTRACT

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.


Subject(s)
COVID-19/economics , Computer Simulation/standards , Resource Allocation/methods , Surge Capacity/economics , COVID-19/prevention & control , COVID-19/therapy , Humans , Resource Allocation/standards , Surge Capacity/trends
14.
Am J Bioeth ; 21(8): 37-39, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1334114
16.
HERD ; 13(3): 8-10, 2020 07.
Article in English | MEDLINE | ID: covidwho-1309890
19.
JMIR Public Health Surveill ; 7(6): e27888, 2021 06 09.
Article in English | MEDLINE | ID: covidwho-1278298

ABSTRACT

BACKGROUND: Prior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe. OBJECTIVE: Our goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions. METHODS: We developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory's COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground. RESULTS: Forecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs. CONCLUSIONS: Real-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.


Subject(s)
COVID-19/therapy , Delivery of Health Care , Health Planning/methods , Hospitalization , Intensive Care Units , Pandemics , Respiration, Artificial , COVID-19/mortality , Equipment and Supplies , Forecasting , Hospitals , Humans , Length of Stay , Models, Statistical , New Mexico , Public Health , SARS-CoV-2 , Surge Capacity
20.
Med Care ; 59(5): 371-378, 2021 05 01.
Article in English | MEDLINE | ID: covidwho-1254915

ABSTRACT

BACKGROUND: Planning for extreme surges in demand for hospital care of patients requiring urgent life-saving treatment for coronavirus disease 2019 (COVID-19), while retaining capacity for other emergency conditions, is one of the most challenging tasks faced by health care providers and policymakers during the pandemic. Health systems must be well-prepared to cope with large and sudden changes in demand by implementing interventions to ensure adequate access to care. We developed the first planning tool for the COVID-19 pandemic to account for how hospital provision interventions (such as cancelling elective surgery, setting up field hospitals, or hiring retired staff) will affect the capacity of hospitals to provide life-saving care. METHODS: We conducted a review of interventions implemented or considered in 12 European countries in March to April 2020, an evaluation of their impact on capacity, and a review of key parameters in the care of COVID-19 patients. This information was used to develop a planner capable of estimating the impact of specific interventions on doctors, nurses, beds, and respiratory support equipment. We applied this to a scenario-based case study of 1 intervention, the set-up of field hospitals in England, under varying levels of COVID-19 patients. RESULTS: The Abdul Latif Jameel Institute for Disease and Emergency Analytics pandemic planner is a hospital planning tool that allows hospital administrators, policymakers, and other decision-makers to calculate the amount of capacity in terms of beds, staff, and crucial medical equipment obtained by implementing the interventions. Flexible assumptions on baseline capacity, the number of hospitalizations, staff-to-beds ratios, and staff absences due to COVID-19 make the planner adaptable to multiple settings. The results of the case study show that while field hospitals alleviate the burden on the number of beds available, this intervention is futile unless the deficit of critical care nurses is addressed first. DISCUSSION: The tool supports decision-makers in delivering a fast and effective response to the pandemic. The unique contribution of the planner is that it allows users to compare the impact of interventions that change some or all inputs.


Subject(s)
COVID-19 , Health Planning Guidelines , Health Services Needs and Demand , Hospitals , Surge Capacity , Workforce , Critical Care Nursing , England , Equipment and Supplies, Hospital , Health Personnel , Hospital Bed Capacity , Humans
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