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Tanchuco JJQ, Garcia FB. Mechanical Ventilator Acquisition Strategy in a Large Private Tertiary Medical Center Using Monte Carlo Simulation. ACTA MEDICA PHILIPPINA 2025; 59:57-69. [PMID: 40151228 PMCID: PMC11936771 DOI: 10.47895/amp.vi0.3892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/29/2025]
Abstract
Background and Objective Mechanical ventilators are essential albeit expensive equipment to support critically ill patients who have gone into respiratory failure. Adequate numbers should always be available to ensure that a hospital provides the optimal care to patients but the number of patients requiring them at any one time is unpredictable. Finding therefore the best balance in providing adequate ventilator numbers while ensuring the financial sustainability of a hospital is important. Methods A quantitative method using Monte Carlo Simulation was used to identify the optimal strategy for acquiring ventilators in a large private tertiary medical center in Metro Manila. The number of ventilators needed to provide ventilator needs 90% of the days per month (27/30) was determined using historical data on ventilator use over a period of four years. Four acquisition strategies were investigated: three ownership strategies (outright purchase, installment, and staggered purchase) and a rental strategy. Return on Investment (ROI), Internal Rate of Return (IRR), Modified Internal Rate of Return (MIRR), Net Present Value (NPV), and Payback period (or Breakeven Point) for each strategy were determined to help recommend the best strategy.A qualitative survey was also conducted among doctors, nurses, and respiratory therapists who were taking care of patients hooked to ventilators to find out their experiences comparing hospital-owned and rental ventilators. Results It was found that a total of 11 respirators were needed by the hospital to ensure that enough respirators were available for its patients at least 90% of the days in any month based on the previous four-year period. This meant acquiring three more ventilators as the hospital already owned eight. Among the strategies studied, projected over a 10-year period, the installment strategy (50% down payment with 0% interest over a 5-year period) proved to be the most financially advantageous with ROI = 9.36 times, IRR = 97% per year, MIRR = 26% per year, NPV = ₱39,324,297.60 and Payback period = 1.03 years). A more realistic installment strategy with 15% (paid quarterly or annually) and 25% annual interest rates were also explored with their financial parameters quite like but not as good as the 0% interest. The outright purchase of three ventilators came in lower (ROI = 4.53 times, IRR = 55% per year, MIRR = 19% per year, NPV = ₱38,064,297.60 and Payback period = 1.81 years) followed last by staggered purchase with ROI = 3.56 times, IRR = 64% per year, MIRR = 28% per year, NPV = ₱29,905,438.08, and payback period of 2.06 years. As there was no investment needed for the rental strategy, the only financial parameter available for it is the NPV which came out as ₱21,234,057.60.The qualitative part of the study showed that most of the healthcare workers involved in the care of patients attached to the ventilator were aware of the rental ventilators. The rental ventilators were generally described as of lower functionality and can more easily break down. The respondents almost uniformly expressed a preference for the hospital-owned ventilators. Conclusion This analysis showed that the best ventilator ownership strategy from a purely financial perspective for this hospital is by installment with a 50% down payment and 0% interest. Moderate rates of 15% and 25% interest per year were also good. These were followed by outright purchase and lastly by staggered purchase. The rental strategy gave the lowest cumulative 10-year income compared to any of the ownership strategies, but may still be considered good income because the hospital did not make any investment. However, it seems that most of the healthcare workers involved in taking care of patients on ventilators thought the rental ventilators were of lower quality and preferred the hospital-owned ventilators.
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Affiliation(s)
- Joven Jeremius Q. Tanchuco
- Department of Biochemistry and Molecular Biology, College of Medicine, University of the Philippines Manila
- Department of Medicine, College of Medicine and Philippine General Hospital, University of the Philippines Manila
| | - Fernando B. Garcia
- Department of Health Policy and Administration, College of Public Health, University of the Philippines Manila
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Beishuizen BHH, Stein ML, Buis JS, Tostmann A, Green C, Duggan J, Connolly MA, Rovers CP, Timen A. A systematic literature review on public health and healthcare resources for pandemic preparedness planning. BMC Public Health 2024; 24:3114. [PMID: 39529010 PMCID: PMC11552315 DOI: 10.1186/s12889-024-20629-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 11/05/2024] [Indexed: 11/16/2024] Open
Abstract
BACKGROUND Generating insights into resource demands during outbreaks is an important aspect of pandemic preparedness. The EU PANDEM-2 project used resource modelling to explore the demand profile for key resources during pandemic scenarios. This review aimed to identify public health and healthcare resources needed to respond to pandemic threats and the ranges of parameter values on the use of these resources for pandemic influenza (including the novel influenza A(H1N1)pdm09 pandemic) and the COVID-19 pandemic, to support modelling activities. METHODS We conducted a systematic literature review and searched Embase and Medline databases (1995 - June 2023) for articles that included a model, scenario, or simulation of pandemic resources and/or describe resource parameters, for example personal protective equipment (PPE) usage, length of stay (LoS) in intensive care unit (ICU), or vaccine efficacy. Papers with data on resource parameters from all countries were included. RESULTS We identified 2754 articles of which 147 were included in the final review. Forty-six different resource parameters with values related to non-ICU beds (n = 43 articles), ICU beds (n = 57), mechanical ventilation (n = 39), healthcare workers (n = 12), pharmaceuticals (n = 21), PPE (n = 8), vaccines (n = 26), and testing and tracing (n = 19). Differences between resource types related to pandemic influenza and COVID-19 were observed, for example on mechanical ventilation (mostly for COVID-19) and testing & tracing (all for COVID-19). CONCLUSION This review provides an overview of public health and healthcare resources with associated parameters in the context of pandemic influenza and the COVID-19 pandemic. Providing insight into the ranges of plausible parameter values on the use of public health and healthcare resources improves the accuracy of results of modelling different scenarios, and thus decision-making by policy makers and hospital planners. This review also highlights a scarcity of published data on important public health resources.
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Affiliation(s)
- Berend H H Beishuizen
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
- Department of Primary and Community Care, Radboud University Medical Centre, Nijmegen, The Netherlands.
| | - Mart L Stein
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Joeri S Buis
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Alma Tostmann
- Department of Medical Microbiology, Radboud Centre for Infectious Diseases, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Caroline Green
- School of Computer Science and Insight Centre for Data Analytics, University of Galway, Galway, Ireland
| | - Jim Duggan
- School of Computer Science and Insight Centre for Data Analytics, University of Galway, Galway, Ireland
| | - Máire A Connolly
- School of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Galway, Ireland
| | - Chantal P Rovers
- Department of Internal Medicine, Radboud Centre for Infectious Diseases, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Aura Timen
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
- Department of Primary and Community Care, Radboud University Medical Centre, Nijmegen, The Netherlands
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Marshall M, Parker F, Gardner LM. When are predictions useful? A new method for evaluating epidemic forecasts. BMC GLOBAL AND PUBLIC HEALTH 2024; 2:67. [PMID: 39681892 DOI: 10.1186/s44263-024-00098-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 09/19/2024] [Indexed: 12/18/2024]
Abstract
BACKGROUND COVID-19 will not be the last pandemic of the twenty-first century. To better prepare for the next one, it is essential that we make honest appraisals of the utility of different responses to COVID. In this paper, we focus specifically on epidemiologic forecasting. Characterizing forecast efficacy over the history of the pandemic is challenging, especially given its significant spatial, temporal, and contextual variability. In this light, we introduce the Weighted Contextual Interval Score (WCIS), a new method for retrospective interval forecast evaluation. METHODS The central tenet of the WCIS is a direct incorporation of contextual utility into the evaluation. This necessitates a specific characterization of forecast efficacy depending on the use case for predictions, accomplished via defining a utility threshold parameter. This idea is generalized to probabilistic interval-form forecasts, which are the preferred prediction format for epidemiological modeling, as an extension of the existing Weighted Interval Score (WIS). RESULTS We apply the WCIS to two forecasting scenarios: facility-level hospitalizations for a single state, and state-level hospitalizations for the whole of the United States. We observe that an appropriately parameterized application of the WCIS captures both the relative quality and the overall frequency of useful forecasts. Since the WCIS represents the utility of predictions using contextual normalization, it is easily comparable across highly variable pandemic scenarios while remaining intuitively representative of the in-situ quality of individual forecasts. CONCLUSIONS The WCIS provides a pragmatic utility-based characterization of probabilistic predictions. This method is expressly intended to enable practitioners and policymakers who may not have expertise in forecasting but are nevertheless essential partners in epidemic response to use and provide insightful analysis of predictions. We note that the WCIS is intended specifically for retrospective forecast evaluation and should not be used as a minimized penalty in a competitive context as it lacks statistical propriety. Code and data used for our analysis are available at https://github.com/maximilian-marshall/wcis .
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Affiliation(s)
- Maximilian Marshall
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA.
| | - Felix Parker
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Lauren M Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA
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Turk PJ, Anderson WE, Burns RJ, Chou SH, Dobbs TE, Kearns JT, Lirette ST, McCarter MS, Nguyen HM, Passaretti CL, Rose GA, Stephens CL, Zhao J, McWilliams AD. A regionally tailored epidemiological forecast and monitoring program to guide a healthcare system in the COVID-19 pandemic. J Infect Public Health 2024; 17:1125-1133. [PMID: 38723322 DOI: 10.1016/j.jiph.2024.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 04/02/2024] [Accepted: 04/16/2024] [Indexed: 06/04/2024] Open
Abstract
BACKGROUND During the COVID-19 pandemic, analytics and predictive models built on regional data provided timely, accurate monitoring of epidemiological behavior, informing critical planning and decision-making for health system leaders. At Atrium Health, a large, integrated healthcare system in the southeastern United States, a team of statisticians and physicians created a comprehensive forecast and monitoring program that leveraged an array of statistical methods. METHODS The program utilized the following methodological approaches: (i) exploratory graphics, including time plots of epidemiological metrics with smoothers; (ii) infection prevalence forecasting using a Bayesian epidemiological model with time-varying infection rate; (iii) doubling and halving times computed using changepoints in local linear trend; (iv) death monitoring using combination forecasting with an ensemble of models; (v) effective reproduction number estimation with a Bayesian approach; (vi) COVID-19 patients hospital census monitored via time series models; and (vii) quantified forecast performance. RESULTS A consolidated forecast and monitoring report was produced weekly and proved to be an effective, vital source of information and guidance as the healthcare system navigated the inherent uncertainty of the pandemic. Forecasts provided accurate and precise information that informed critical decisions on resource planning, bed capacity and staffing management, and infection prevention strategies. CONCLUSIONS In this paper, we have presented the framework used in our epidemiological forecast and monitoring program at Atrium Health, as well as provided recommendations for implementation by other healthcare systems and institutions to facilitate use in future pandemics.
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Affiliation(s)
- Philip J Turk
- Northeast Ohio Medical University, 4209 St Rt 44, PO Box 95, Rootstown, OH 44272, USA.
| | | | - Ryan J Burns
- Atrium Health, 1000 Blythe Blvd, Charlotte, NC 28203, USA
| | | | - Thomas E Dobbs
- University of Mississippi Medical Center, 2500 N State St, Jackson, MS 39216, USA
| | - James T Kearns
- NorthShore Medical Group, 2180 Pfingsten Rd, Ste 3000, Glenview, IL 60026, USA
| | - Seth T Lirette
- University of Mississippi Medical Center, 2500 N State St, Jackson, MS 39216, USA
| | | | - Hieu M Nguyen
- Atrium Health, 1000 Blythe Blvd, Charlotte, NC 28203, USA
| | | | | | | | - Jing Zhao
- Janssen Global Services, 700 Dresher Rd, Horsham, PA 19044, USA
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Tarnate PSO, Ong-Lim ALT. Strategic Optimization of Patient Flow and Staffing Schemes during the COVID-19 Pandemic through Operations Management in a Neonatal Intensive Care Unit. ACTA MEDICA PHILIPPINA 2024; 58:90-102. [PMID: 38882916 PMCID: PMC11168953 DOI: 10.47895/amp.v58i7.6334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Background The COVID-19 pandemic posed challenges in making time-bound hospital management decisions. The University of the Philippines -Philippine General Hospital (UP-PGH) is a tertiary COVID-19 referral center located in Manila, Philippines. The mismatch of increasing suspected or confirmed COVID-19 infected mothers with few documented cases of infected infants has caused significant patient overflow and manpower shortage in its NICU. Objective We present an evaluated scheme for NICU bed reallocation to maximize capacity performance, staff rostering, and resource conservation, while preserving COVID-19 infection prevention and control measures. Methods Existing process workflows translated into operational models helped create a solution that modified cohorting and testing schemes. Staffing models were transitioned to meet patient flow. Outcome measurements were obtained, and feedback was monitored during the implementation phase. Results The scheme evaluation demonstrated benefits in (a) achieving shorter COVID-19 subunit length of stay; (b) better occupancy rates with minimal overflows; (c) workforce shortage mitigation with increased non-COVID workforce pool; (d) reduced personal protective equipment requirements; and (e) zero true SARS-CoV-2 infections. Conclusion Designed for hospital operations leaders and stakeholders, this operations process can aid in hospital policy formulation in modifying cohorting schemes to maintain quality NICU care and service during the COVID-19 pandemic.
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Affiliation(s)
- Paul Sherwin O Tarnate
- Division of Infectious and Tropical Diseases in Pediatrics, Department of Pediatrics, Philippine General Hospital, University of the Philippines Manila
| | - Anna Lisa T Ong-Lim
- Division of Infectious and Tropical Diseases in Pediatrics, Department of Pediatrics, Philippine General Hospital, University of the Philippines Manila
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Zheng Q, Zeng Z, Tang X, Ma L. Impact of an ICU bed capacity optimisation method on the average length of stay and average cost of hospitalisation following implementation of China's open policy with respect to COVID-19: a difference-in-differences analysis based on information management system data from a tertiary hospital in southwest China. BMJ Open 2024; 14:e078069. [PMID: 38643008 PMCID: PMC11033667 DOI: 10.1136/bmjopen-2023-078069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 03/27/2024] [Indexed: 04/22/2024] Open
Abstract
OBJECTIVES Following the implementation of China's open policy with respect to COVID-19 on 7 December 2022, the influx of patients with infectious diseases has surged rapidly, necessitating hospitals to adopt temporary requisition and modification of ward beds to optimise hospital bed capacity and alleviate the burden of overcrowded patients. This study aims to investigate the effect of an intensive care unit (ICU) bed capacity optimisation method on the average length of stay (ALS) and average cost of hospitalisation (ACH) after the open policy of COVID-19 in China. DESIGN AND SETTING A difference-in-differences (DID) approach is employed to analyse and compare the ALS and ACH of patients in four modified ICUs and eight non-modified ICUs within a tertiary hospital located in southwest China. The analysis spans 2 months before and after the open policy, specifically from 5 October 2022 to 6 December 2022, and 7 December 2022 to 6 February 2023. PARTICIPANTS We used the daily data extracted from the hospital's information management system for a total of 5944 patients admitted by the outpatient and emergency access during the 2-month periods before and after the release of the open policy in China. RESULTS The findings indicate that the ICU bed optimisation method implemented by the tertiary hospital led to a significant reduction in ALS (HR -0.6764, 95% CI -1.0328 to -0.3201, p=0.000) and ACH (HR -0.2336, 95% CI -0.4741 to -0.0068, p=0.057) among ICU patients after implementation of the open policy. These results were robust across various sensitivity analyses. However, the effect of the optimisation method exhibits heterogeneity among patients admitted through the outpatient and emergency channels. CONCLUSIONS This study corroborates a significant positive impact of ICU bed optimisation in mitigating the shortage of medical resources following an epidemic outbreak. The findings hold theoretical and practical implications for identifying effective emergency coordination strategies in managing hospital bed resources during sudden public health emergency events. These insights contribute to the advancement of resource management practices and the promotion of experiences in dealing with public health emergencies.
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Affiliation(s)
- Qingyan Zheng
- School of Business, Sichuan Unversity, Chengdu, China
- The Hong Kong Polytechnic University, Hong Kong, China
| | - Zhongyi Zeng
- West China School of Nursing, Sichuan University, Chengdu, China
| | - Xiumei Tang
- School of Business, Sichuan Unversity, Chengdu, China
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
- Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China
| | - Li Ma
- School of Business, Sichuan Unversity, Chengdu, China
- West China School of Nursing, Sichuan University, Chengdu, China
- Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China
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Micuda AN, Anderson MR, Babayan I, Bolger E, Cantin L, Groth G, Pressman-Cyna R, Reed CZ, Rowe NJ, Shafiee M, Tam B, Vidal MC, Ye T, Martin RD. Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model. Infect Dis Model 2024; 9:234-244. [PMID: 38303993 PMCID: PMC10831812 DOI: 10.1016/j.idm.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 12/17/2023] [Accepted: 01/06/2024] [Indexed: 02/03/2024] Open
Abstract
This work introduces the Queen's University Agent-Based Outbreak Outcome Model (QUABOOM). This tool is an agent-based Monte Carlo simulation for modelling epidemics and informing public health policy. We illustrate the use of the model by examining capacity restrictions during a lockdown. We find that public health measures should focus on the few locations where many people interact, such as grocery stores, rather than the many locations where few people interact, such as small businesses. We also discuss a case where the results of the simulation can be scaled to larger population sizes, thereby improving computational efficiency.
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Affiliation(s)
- Ashley N. Micuda
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston, ON, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
| | - Mark R. Anderson
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston, ON, Canada
| | - Irina Babayan
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston, ON, Canada
| | - Erin Bolger
- Department of Mathematics and Statistics, Queen's University, Kingston, ON, Canada
- Department of Biology, Queen's University, Kingston, ON, Canada
| | - Logan Cantin
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Gillian Groth
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Ry Pressman-Cyna
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston, ON, Canada
| | - Charlotte Z. Reed
- Department of Mathematics and Statistics, Queen's University, Kingston, ON, Canada
| | - Noah J. Rowe
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston, ON, Canada
| | - Mehdi Shafiee
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston, ON, Canada
- Department of Electrical and Computer Engineering, Nazarbayev University, Nur-Sultan, Kazakhstan
- Energetic Cosmos Laboratory, Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Benjamin Tam
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston, ON, Canada
- Department of Physics, University of Oxford, Oxford, United Kingdom
| | - Marie C. Vidal
- Department of Physics, Stanford University, Stanford, CA, United States
| | - Tianai Ye
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston, ON, Canada
| | - Ryan D. Martin
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston, ON, Canada
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Hosseini-Jebeli S, Tehrani-Banihashemi A, Eshrati B, Mehrabi A, Benis MR, Nojomi M. Hospital capacities and response to COVID-19 pandemic surges in Iran: A quantitative model-based study. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2024; 13:75. [PMID: 38559485 PMCID: PMC10979778 DOI: 10.4103/jehp.jehp_956_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/05/2023] [Indexed: 04/04/2024]
Abstract
The coronavirus 2019 (COVID-19) pandemic resulted in serious limitations for healthcare systems, and this study aimed to investigate the impact of COVID-19 surges on in-patient care capacities in Iran employing the Adaptt tool. Using a cross-sectional study design, our study was carried out in the year 2022 using 1-year epidemiologic (polymerase chain reaction-positive COVID-19 cases) and hospital capacity (beds and human resource) data from the official declaration of the pandemic in Iran in February 2020. We populated several scenarios, and in each scenario, a proportion of hospital capacity is assumed to be allocated to the COVID-19 patients. In most of the scenarios, no significant shortage was found in terms of bed and human resources. However, considering the need for treatment of non- COVID-19 cases, in one of the scenarios, it can be observed that during the peak period, the number of required and available specialists is exactly equal, which was a challenge during surge periods and resulted in extra hours of working and workforce burnout in hospitals. The shortage of intensive care unit beds and doctors specializing in internal medicine, infectious diseases, and anesthesiology also requires more attention for planning during the peak days of COVID-19.
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Affiliation(s)
| | - Arash Tehrani-Banihashemi
- Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Department of Community and Family Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Babak Eshrati
- Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Department of Community and Family Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Ali Mehrabi
- Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Department of Community and Family Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mahshid Roohravan Benis
- Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Department of Community and Family Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Marzieh Nojomi
- Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Department of Community and Family Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
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Redondo E, Nicoletta V, Bélanger V, Garcia-Sabater JP, Landa P, Maheut J, Marin-Garcia JA, Ruiz A. A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis. HEALTHCARE ANALYTICS (NEW YORK, N.Y.) 2023; 3:100197. [PMID: 37275436 PMCID: PMC10212597 DOI: 10.1016/j.health.2023.100197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 04/09/2023] [Accepted: 05/11/2023] [Indexed: 06/07/2023]
Abstract
COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pandemic spread, very few have tried to translate the output of these models into hospital service requirements, particularly in terms of bed occupancy, a key question for hospital managers. This paper proposes a tool for predicting the current and future occupancy associated with COVID-19 patients of a hospital to help managers make informed decisions to maximize the availability of hospitalization and intensive care unit (ICU) beds and ensure adequate access to services for confirmed COVID-19 patients. The proposed tool uses a discrete event simulation approach that uses archetypes (i.e., empirical models of trajectories) extracted from empirical analysis of actual patient trajectories. Archetypes can be fitted to trajectories observed in different regions or to the particularities of current and forthcoming variants using a rather small amount of data. Numerical experiments on realistic instances demonstrate the accuracy of the tool's predictions and illustrate how it can support managers in their daily decisions concerning the system's capacity and ensure patients the access the resources they require.
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Affiliation(s)
- Eduardo Redondo
- Faculty of Business Administration, Université Laval, Quebec (Quebec), G1K 7P4, Canada
- Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), Canada
| | - Vittorio Nicoletta
- Faculty of Business Administration, Université Laval, Quebec (Quebec), G1K 7P4, Canada
- Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), Canada
| | - Valérie Bélanger
- Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), Canada
- Department of Logistics and Operations Management, HEC Montréal, 3000 chemin de la Cote Sainte-Catherine, Montreal (Quebec), H3T 2A7, Canada
| | - José P Garcia-Sabater
- ROGLE, Department of Organización de Empresas, Universitat Politècnica de València, Valencia s/n, 46021 Valencia, Spain
| | - Paolo Landa
- Faculty of Business Administration, Université Laval, Quebec (Quebec), G1K 7P4, Canada
- Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), Canada
| | - Julien Maheut
- ROGLE, Department of Organización de Empresas, Universitat Politècnica de València, Valencia s/n, 46021 Valencia, Spain
| | - Juan A Marin-Garcia
- ROGLE, Department of Organización de Empresas, Universitat Politècnica de València, Valencia s/n, 46021 Valencia, Spain
| | - Angel Ruiz
- Faculty of Business Administration, Université Laval, Quebec (Quebec), G1K 7P4, Canada
- Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), Canada
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Joyce D, De Brún A, Symmons SM, Fox R, McAuliffe E. Remote patient monitoring for COVID-19 patients: comparisons and framework for reporting. BMC Health Serv Res 2023; 23:826. [PMID: 37537615 PMCID: PMC10401771 DOI: 10.1186/s12913-023-09526-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 05/09/2023] [Indexed: 08/05/2023] Open
Abstract
BACKGROUND COVID-19 has challenged health services throughout the world in terms of hospital capacity and put staff and vulnerable populations at risk of infection. In the face of these challenges, many health providers have implemented remote patient monitoring (RPM) of COVID-19 patients in their own homes. However systematic reviews of the literature on these implementations have revealed wide variations in how RPM is implemented; along with variations in particulars of RPM reported on, making comparison and evaluation difficult. A review of reported items is warranted to develop a framework of key items to enhance reporting consistency. The aims of this review of remote monitoring for COVID-19 patients are twofold: (1) to facilitate comparison between RPM implementations by tabulating information and values under common domains. (2) to develop a reporting framework to enhance reporting consistency. METHOD A review of the literature for RPM for COVID-19 patients was conducted following PRISMA guidelines. The Medline database was searched for articles published between 2020 to February 2023 and studies reporting on items with sufficient detail to compare one with another were included. Relevant data was extracted and synthesized by the lead author. Quality appraisal was not conducted as the the articles considered were evaluated as informational reports of clinical implementations rather than as studies designed to answer a research question. RESULTS From 305 studies retrieved, 23 studies were included in the review: fourteen from the US, two from the UK and one each from Africa, Ireland, China, the Netherlands, Belgium, Australia and Italy. Sixteen generally reported items were identified, shown with the percentage of studies reporting in brackets: Reporting Period (82%), Rationale (100%), Patients (100%), Medical Team (91%) Provider / Infrastructure (91%), Communications Platform (100%), Patient Equipment (100%), Training (48%), Markers (96%), Frequency of prompt / Input (96%),Thresholds (82%), Discharge (61%), Enrolled (96%), Alerts/Escalated (78%), Patient acceptance (43%), and Patient Adherence (52%). Whilst some studies reported on patient training and acceptance, just one reported on staff training and none on staff acceptance. CONCLUSIONS Variations in reported items were found. Pending the establishment of a robust set of reporting guidelines, we propose a reporting framework consisting of eighteen reporting items under the following four domains: Context, Technology, Process and Metrics.
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Affiliation(s)
- David Joyce
- Interdisciplinary Research Education and Innovation in Health Systems (IRIS) Centre, School of Nursing, Midwifery and Health Systems, University College Dublin, Dublin, D04 V1W8, Ireland
| | - Aoife De Brún
- Interdisciplinary Research Education and Innovation in Health Systems (IRIS) Centre, School of Nursing, Midwifery and Health Systems, University College Dublin, Dublin, D04 V1W8, Ireland
| | - Sophie Mulcahy Symmons
- Interdisciplinary Research Education and Innovation in Health Systems (IRIS) Centre, School of Nursing, Midwifery and Health Systems, University College Dublin, Dublin, D04 V1W8, Ireland
| | - Robert Fox
- Interdisciplinary Research Education and Innovation in Health Systems (IRIS) Centre, School of Nursing, Midwifery and Health Systems, University College Dublin, Dublin, D04 V1W8, Ireland
| | - Eilish McAuliffe
- Interdisciplinary Research Education and Innovation in Health Systems (IRIS) Centre, School of Nursing, Midwifery and Health Systems, University College Dublin, Dublin, D04 V1W8, Ireland.
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11
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Sili U, Ay P, Bilgin H, Topuzoğlu A, Tükenmez-Tigen E, Ertürk-Şengel B, Yağçı-Çağlayık D, Balcan B, Kocakaya D, Olgun-Yıldızeli Ş, Gül F, Bilgili B, Can-Sarınoğlu R, Karahasan-Yağcı A, Mülazimoğlu-Durmuşoğlu L, Eryüksel E, Odabaşı Z, Direskeneli H, Karakurt S, Korten V. Factors Associated with 28-day Critical Illness Development During the First Wave of COVID-19. INFECTIOUS DISEASES & CLINICAL MICROBIOLOGY 2023; 5:94-105. [PMID: 38633015 PMCID: PMC10985825 DOI: 10.36519/idcm.2023.206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 04/25/2023] [Indexed: 04/19/2024]
Abstract
Objective This study aimed to define the predictors of critical illness development within 28 days postadmission during the first wave of the COVID-19 pandemic. Materials and Methods We conducted a prospective cohort study including 477 PCR-positive COVID-19 patients admitted to a tertiary care hospital in Istanbul from March 12 to May 12, 2020. Results The most common presenting symptoms were cough, dyspnea, and fatigue. Critical illness developed in 45 (9.4%; 95% CI=7.0%-12.4%) patients. In the multivariable analysis, age (hazard ratio (HR)=1.05, p<0.001), number of comorbidities (HR=1.33, p=0.02), procalcitonin ≥0.25 µg/L (HR=2.12, p=0.03) and lactate dehydrogenase (LDH) ≥350 U/L (HR=2.04, p=0.03) were independently associated with critical illness development. The World Health Organization (WHO) ordinal scale for clinical improvement on admission was the strongest predictor of critical illness (HR=4.15, p<0.001). The patients hospitalized at the end of the study period had a much better prognosis compared to the patients hospitalized at the beginning (HR=0.14; p=0.02). The C-index of the model was 0.92. Conclusion Age, comorbidity number, the WHO scale, LDH, and procalcitonin were independently associated with critical illness development. Mortality from COVID-19 seemed to be decreasing as the first wave of the pandemic advanced. Graphic Abstract Graphic Abstract.
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Affiliation(s)
- Uluhan Sili
- Department of Infectious Diseases and Clinical Microbiology,
Marmara University School of Medicine, İstanbul, Turkey
- Equal contribution
| | - Pınar Ay
- Department of Public Health, Marmara University School of
Medicine, İstanbul, Turkey
- Equal contribution
| | - Hüseyin Bilgin
- Department of Infectious Diseases and Clinical Microbiology,
Marmara University School of Medicine, İstanbul, Turkey
- Equal contribution
| | - Ahmet Topuzoğlu
- Department of Public Health, Marmara University School of
Medicine, İstanbul, Turkey
- Equal contribution
| | - Elif Tükenmez-Tigen
- Department of Infectious Diseases and Clinical Microbiology,
Marmara University School of Medicine, İstanbul, Turkey
| | - Buket Ertürk-Şengel
- Department of Infectious Diseases and Clinical Microbiology,
Marmara University School of Medicine, İstanbul, Turkey
| | - Dilek Yağçı-Çağlayık
- Department of Infectious Diseases and Clinical Microbiology,
Marmara University School of Medicine, İstanbul, Turkey
| | - Baran Balcan
- Department of Pulmonary Medicine, Marmara University School of
Medicine, İstanbul, Turkey
| | - Derya Kocakaya
- Department of Pulmonary Medicine, Marmara University School of
Medicine, İstanbul, Turkey
| | - Şehnaz Olgun-Yıldızeli
- Department of Pulmonary Medicine, Marmara University School of
Medicine, İstanbul, Turkey
| | - Fethi Gül
- Department of Anesthesiology and Intensive Care, Marmara
University School of Medicine, İstanbul, Turkey
| | - Beliz Bilgili
- Department of Anesthesiology and Intensive Care, Marmara
University School of Medicine, İstanbul, Turkey
| | - Rabia Can-Sarınoğlu
- Department of Medical Microbiology, Marmara University School of
Medicine, İstanbul, Turkey
| | | | | | - Emel Eryüksel
- Department of Pulmonary Medicine, Marmara University School of
Medicine, İstanbul, Turkey
| | - Zekaver Odabaşı
- Department of Infectious Diseases and Clinical Microbiology,
Marmara University School of Medicine, İstanbul, Turkey
| | - Haner Direskeneli
- Department of Internal Medicine, Marmara University School of
Medicine, İstanbul, Turkey
| | - Sait Karakurt
- Department of Pulmonary Medicine, Marmara University School of
Medicine, İstanbul, Turkey
| | - Volkan Korten
- Department of Infectious Diseases and Clinical Microbiology,
Marmara University School of Medicine, İstanbul, Turkey
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12
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Zimmerman SL, Rutherford AR, van der Waall A, Norena M, Dodek P. A queuing model for ventilator capacity management during the COVID-19 pandemic. Health Care Manag Sci 2023; 26:200-216. [PMID: 37212974 PMCID: PMC10201510 DOI: 10.1007/s10729-023-09632-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 02/13/2023] [Indexed: 05/23/2023]
Abstract
We applied a queuing model to inform ventilator capacity planning during the first wave of the COVID-19 epidemic in the province of British Columbia (BC), Canada. The core of our framework is a multi-class Erlang loss model that represents ventilator use by both COVID-19 and non-COVID-19 patients. Input for the model includes COVID-19 case projections, and our analysis incorporates projections with different levels of transmission due to public health measures and social distancing. We incorporated data from the BC Intensive Care Unit Database to calibrate and validate the model. Using discrete event simulation, we projected ventilator access, including when capacity would be reached and how many patients would be unable to access a ventilator. Simulation results were compared with three numerical approximation methods, namely pointwise stationary approximation, modified offered load, and fixed point approximation. Using this comparison, we developed a hybrid optimization approach to efficiently identify required ventilator capacity to meet access targets. Model projections demonstrate that public health measures and social distancing potentially averted up to 50 deaths per day in BC, by ensuring that ventilator capacity was not reached during the first wave of COVID-19. Without these measures, an additional 173 ventilators would have been required to ensure that at least 95% of patients can access a ventilator immediately. Our model enables policy makers to estimate critical care utilization based on epidemic projections with different transmission levels, thereby providing a tool to quantify the interplay between public health measures, necessary critical care resources, and patient access indicators.
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Affiliation(s)
- Samantha L. Zimmerman
- Department of Mathematics, Simon Fraser University, 8888 University Dr., Burnaby, V5A 1S6 BC Canada
| | - Alexander R. Rutherford
- Department of Mathematics, Simon Fraser University, 8888 University Dr., Burnaby, V5A 1S6 BC Canada
| | - Alexa van der Waall
- Department of Mathematics, Simon Fraser University, 8888 University Dr., Burnaby, V5A 1S6 BC Canada
| | - Monica Norena
- Center for Health Evaluation and Outcome Sciences, 588 - 1081 Burrard Street St. Paul’s Hospital, Vancouver, V6Z 1Y6 BC Canada
| | - Peter Dodek
- Center for Health Evaluation and Outcome Sciences, 588 - 1081 Burrard Street St. Paul’s Hospital, Vancouver, V6Z 1Y6 BC Canada
- Division of Critical Care, Department of Medicine, Faculty of Medicine, The University of British Columbia, 855 W 12th Ave, Vancouver, V5Z 1M9 BC Canada
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13
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Cetinkale Z, Aydin N. Health Care Logistics Network Design and Analysis on Pandemic Outbreaks: Insights From COVID-19. TRANSPORTATION RESEARCH RECORD 2023; 2677:674-703. [PMID: 37153192 PMCID: PMC10149596 DOI: 10.1177/03611981221099015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Health care systems throughout the world are under pressure as a result of COVID-19. It is over two years since the first case was announced in China and health care providers are continuing to struggle with this fatal infectious disease in intensive care units and inpatient wards. Meanwhile, the burden of postponed routine medical procedures has become greater as the pandemic has progressed. We believe that establishing separate health care institutions for infected and non-infected patients would provide safer and better quality health care services. The aim of this study is to find the appropriate number and location of dedicated health care institutions which would only treat individuals infected by a pandemic during an outbreak. For this purpose, a decision-making framework including two multi-objective mixed-integer programming models is developed. At the strategic level, the locations of designated pandemic hospitals are optimized. At the tactical level, we determine the locations and operation durations of temporary isolation centers which treat mildly and moderately symptomatic patients. The developed framework provides assessments of the distance that infected patients travel, the routine medical services expected to be disrupted, two-way distances between new facilities (designated pandemic hospitals and isolation centers), and the infection risk in the population. To demonstrate the applicability of the suggested models, we perform a case study for the European side of Istanbul. In the base case, seven designated pandemic hospitals and four isolation centers are established. In sensitivity analyses, 23 cases are analyzed and compared to provide support to decision makers.
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Affiliation(s)
- Zeynep Cetinkale
- Turkish Airlines, İstanbul,
Turkey
- Department of Industrial Engineering,
Yildiz Technical University, Istanbul, Turkey
- Zeynep Cetinkale,
| | - Nezir Aydin
- Department of Industrial Engineering,
Yildiz Technical University, Istanbul, Turkey
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14
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Garcia-Vicuña D, López-Cheda A, Jácome MA, Mallor F. Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves. PLoS One 2023; 18:e0282331. [PMID: 36848360 PMCID: PMC9970104 DOI: 10.1371/journal.pone.0282331] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 02/13/2023] [Indexed: 03/01/2023] Open
Abstract
Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on not updated published information or historical data. This may lead to unreliable estimates and biased forecasts during new or non-stationary situations. In this paper, we introduce a flexible adaptive procedure using only near-real-time information. Such method requires handling censored information from patients still in hospital. This approach allows the efficient estimation of the distributions of lengths of stay and probabilities used to represent the patient pathways. This is very relevant at the first stages of a pandemic, when there is much uncertainty and too few patients have completely observed pathways. Furthermore, the performance of the proposed method is assessed in an extensive simulation study in which the patient flow in a hospital during a pandemic wave is modelled. We further discuss the advantages and limitations of the method, as well as potential extensions.
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Affiliation(s)
| | - Ana López-Cheda
- Departamento de Matemáticas, Research Group MODES, CITIC, Universidade da Coruña, A Coruña, Spain
| | - María Amalia Jácome
- Departamento de Matemáticas, Research Group MODES, CITIC, Universidade da Coruña, A Coruña, Spain
| | - Fermin Mallor
- Institute of Smart Cities, Public University of Navawordpadrre, Pamplona, Spain
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15
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Aydin N, Cetinkale Z. Simultaneous response to multiple disasters: Integrated planning for pandemics and large-scale earthquakes. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION : IJDRR 2023; 86:103538. [PMID: 36741191 PMCID: PMC9890538 DOI: 10.1016/j.ijdrr.2023.103538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 11/30/2022] [Accepted: 01/09/2023] [Indexed: 06/18/2023]
Abstract
Since the beginning of COVID-19, individuals who have SARS-CoV-2 infectious have brought a heavy burden on the healthcare system. Unavoidably, along with pandemics, large-scale disasters, which are possibly emerging, may double the current health crisis. For a powerful disaster response plan, the health services should be prepared for the overwhelming number of disaster victims and infected individuals The proposed framework determines the appropriate number and location of temporary healthcare facilities for large-scale disasters while considering the burden of ongoing pandemic diseases. In this study, first, a multi-period, mix-integer mathematical model is developed to find the location and number of disaster emergency units and disaster medical facilities. Second, we develop an epidemic compartmental model to stimulate the negative effects of the disaster on disease spread and a mixed-integer mathematical model to find optimal number and the location of pandemic hospitals and isolation centers. To validate the mathematical models, a case study is conducted for a district of Istanbul, Turkey.
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Affiliation(s)
- Nezir Aydin
- Department of Industrial Engineering, Yildiz Technical University, Besiktas, 34349, Istanbul, Turkey
| | - Zeynep Cetinkale
- Department of Industrial Engineering, Yildiz Technical University, Besiktas, 34349, Istanbul, Turkey
- Turkish Airlines, 34149, Yesilkoy, İstanbul, Turkey
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16
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Saqib K, Qureshi AS, Butt ZA. COVID-19, Mental Health, and Chronic Illnesses: A Syndemic Perspective. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3262. [PMID: 36833955 PMCID: PMC9962717 DOI: 10.3390/ijerph20043262] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/10/2023] [Accepted: 02/11/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND The COVID-19 pandemic is an epidemiological and psychological crisis; what it does to the body is quite well known by now, and more research is underway, but the syndemic impact of COVID-19 and mental health on underlying chronic illnesses among the general population is not completely understood. METHODS We carried out a literature review to identify the potential impact of COVID-19 and related mental health issues on underlying comorbidities that could affect the overall health of the population. RESULTS Many available studies have highlighted the impact of COVID-19 on mental health only, but how complex their interaction is in patients with comorbidities and COVID-19, the absolute risks, and how they connect with the interrelated risks in the general population, remain unknown. The COVID-19 pandemic can be recognized as a syndemic due to; synergistic interactions among different diseases and other health conditions, increasing overall illness burden, emergence, spread, and interactions between infectious zoonotic diseases leading to new infectious zoonotic diseases; this is together with social and health interactions leading to increased risks in vulnerable populations and exacerbating clustering of multiple diseases. CONCLUSION There is a need to develop evidence to support appropriate and effective interventions for the overall improvement of health and psychosocial wellbeing of at-risk populations during this pandemic. The syndemic framework is an important framework that can be used to investigate and examine the potential benefits and impact of codesigning COVID-19/non-communicable diseases (NCDs)/mental health programming services which can tackle these epidemics concurrently.
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Affiliation(s)
- Kiran Saqib
- School of Public health Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Afaf Saqib Qureshi
- Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Zahid Ahmad Butt
- School of Public health Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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17
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Li K, Eckel SP, Garcia E, Chen Z, Wilson JP, Gilliland FD. Geographic Variations in Human Mobility Patterns during the First Six Months of the COVID-19 Pandemic in California. APPLIED SCIENCES (BASEL, SWITZERLAND) 2023; 13:2440. [PMID: 39354955 PMCID: PMC11444676 DOI: 10.3390/app13042440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2024]
Abstract
Human mobility influenced the spread of the COVID-19 virus, as revealed by the high spatiotemporal granularity location service data gathered from smart devices. We conducted time series clustering analysis to delineate the relationships between human mobility patterns (HMPs) and their social determinants in California (CA) using aggregated smart device tracking data from SafeGraph. We first identified four types of temporal patterns for five human mobility indicator changes by applying dynamic-time-warping self-organizing map clustering methods. We then performed an analysis of variance and linear discriminant analysis on the HMPs with 17 social, economic, and demographic variables. Asians, children under five, adults over 65, and individuals living below the poverty line were found to be among the top contributors to the HMPs, including the HMP with a significant increase in the median home dwelling time and the HMP with emerging weekly patterns in full-time and part-time work devices. Our findings show that the CA shelter-in-place policy had varying impacts on HMPs, with socially disadvantaged places showing less compliance. The HMPs may help practitioners to anticipate the efficacy of non-pharmaceutical interventions on cases and deaths in pandemics.
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Affiliation(s)
- Kenan Li
- Department of Epidemiology and Biostatistics, Saint Louis University, St. Louis, MO 63104, USA
| | - Sandrah P. Eckel
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA
| | - Erika Garcia
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA
| | - John P. Wilson
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA
- Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - Frank D. Gilliland
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA
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18
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Hu B, Jiang G, Yao X, Chen W, Yue T, Zhao Q, Wen Z. Allocation of emergency medical resources for epidemic diseases considering the heterogeneity of epidemic areas. Front Public Health 2023; 11:992197. [PMID: 36908482 PMCID: PMC9998515 DOI: 10.3389/fpubh.2023.992197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 02/06/2023] [Indexed: 02/26/2023] Open
Abstract
Background The resources available to fight an epidemic are typically limited, and the time and effort required to control it grow as the start date of the containment effort are delayed. When the population is afflicted in various regions, scheduling a fair and acceptable distribution of limited available resources stored in multiple emergency resource centers to each epidemic area has become a serious problem that requires immediate resolution. Methods This study presents an emergency medical logistics model for rapid response to public health emergencies. The proposed methodology consists of two recursive mechanisms: (1) time-varying forecasting of medical resources and (2) emergency medical resource allocation. Considering the epidemic's features and the heterogeneity of existing medical treatment capabilities in different epidemic areas, we provide the modified susceptible-exposed-infected-recovered (SEIR) model to predict the early stage emergency medical resource demand for epidemics. Then we define emergency indicators for each epidemic area based on this. By maximizing the weighted demand satisfaction rate and minimizing the total vehicle travel distance, we develop a bi-objective optimization model to determine the optimal medical resource allocation plan. Results Decision-makers should assign appropriate values to parameters at various stages of the emergency process based on the actual situation, to ensure that the results obtained are feasible and effective. It is necessary to set up an appropriate number of supply points in the epidemic emergency medical logistics supply to effectively reduce rescue costs and improve the level of emergency services. Conclusions Overall, this work provides managerial insights to improve decisions made on medical distribution as per demand forecasting for quick response to public health emergencies.
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Affiliation(s)
- Bin Hu
- School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Guanhua Jiang
- School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Xinyi Yao
- School of Management, Xuzhou Medical University, Xuzhou, China
| | - Wei Chen
- School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Tingyu Yue
- School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Qitong Zhao
- Department of Logistics and Supply Chain Management School of Business, Singapore University of Social Science, Singapore, Singapore
| | - Zongliang Wen
- School of Management, Xuzhou Medical University, Xuzhou, China.,Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
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19
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Yin X, Büyüktahtakın IE, Patel BP. COVID-19: Data-Driven optimal allocation of ventilator supply under uncertainty and risk. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023; 304:255-275. [PMID: 34866765 PMCID: PMC8632406 DOI: 10.1016/j.ejor.2021.11.052] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 11/26/2021] [Indexed: 05/06/2023]
Abstract
This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.
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Affiliation(s)
- Xuecheng Yin
- Yale School of Public Health, New Haven, CT, United States
| | - I Esra Büyüktahtakın
- Systems Optimization and Data Analytics Lab (SODAL), Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Bhumi P Patel
- Systems Optimization and Data Analytics Lab (SODAL), Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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20
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Bozkir CDC, Ozmemis C, Kurbanzade AK, Balcik B, Gunes ED, Tuglular S. Capacity planning for effective cohorting of hemodialysis patients during the coronavirus pandemic: A case study. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023; 304:276-291. [PMID: 34744293 PMCID: PMC8556688 DOI: 10.1016/j.ejor.2021.10.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 10/20/2021] [Indexed: 05/09/2023]
Abstract
Planning treatments of different types of patients have become challenging in hemodialysis clinics during the COVID-19 pandemic due to increased demands and uncertainties. In this study, we address capacity planning decisions of a hemodialysis clinic, located within a major public hospital in Istanbul, which serves both infected and uninfected patients during the COVID-19 pandemic with limited resources (i.e., dialysis machines). The clinic currently applies a 3-unit cohorting strategy to treat different types of patients (i.e., uninfected, infected, suspected) in separate units and at different times to mitigate the risk of infection spread risk. Accordingly, at the beginning of each week, the clinic needs to allocate the available dialysis machines to each unit that serves different patient cohorts. However, given the uncertainties in the number of different types of patients that will need dialysis each day, it is a challenge to determine which capacity configuration would minimize the overlapping treatment sessions of different cohorts over a week. We represent the uncertainties in the number of patients by a set of scenarios and present a stochastic programming approach to support capacity allocation decisions of the clinic. We present a case study based on the real-world patient data obtained from the hemodialysis clinic to illustrate the effectiveness of the proposed model. We also compare the performance of different cohorting strategies with three and two patient cohorts.
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Affiliation(s)
- Cem D C Bozkir
- Industrial Engineering Department, Ozyegin University, Istanbul, Turkey
| | - Cagri Ozmemis
- Industrial Engineering Department, Ozyegin University, Istanbul, Turkey
| | | | - Burcu Balcik
- Industrial Engineering Department, Ozyegin University, Istanbul, Turkey
| | - Evrim D Gunes
- Business Administration, College of Administrative Sciences and Economics, Koc University, Sariyer, Istanbul, Turkey
| | - Serhan Tuglular
- Medical Faculty, Department of Internal Medicine, Marmara University, Istanbul, Turkey
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21
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Hosseini-Motlagh SM, Samani MRG, Homaei S. Design of control strategies to help prevent the spread of COVID-19 pandemic. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023; 304:219-238. [PMID: 34803212 PMCID: PMC8592648 DOI: 10.1016/j.ejor.2021.11.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 11/09/2021] [Indexed: 05/10/2023]
Abstract
This paper proposes control strategies to allocate COVID-19 patients to screening facilities, health facilities, and quarantine facilities for minimizing the spread of the virus by these patients. To calculate the transmission rate, we propose a function that accounts for contact rate, duration of the contact, age structure of the population, susceptibility to infection, and the number of transmission events per contact. Moreover, the COVID-19 cases are divided into different groups according to the severity of their disease and are allocated to appropriate health facilities that provide care tailored to their needs. The multi-stage fuzzy stochastic programming approach is applied to cope with uncertainty, in which the probability associated with nodes of the scenario tree is treated as fuzzy variables. To handle the probabilistic model, we use a more flexible measure, M e measure, which allows decision-makers to adopt varying attitudes by assigning the optimistic-pessimistic parameter. This measure does not force decision-makers to hold extreme views and obtain the interval solution that provides further information in the fuzzy environment. We apply the proposed model to the case of Tehran, Iran. The results of this study indicate that assigning patients to appropriate medical centers improves the performance of the healthcare system. The result analysis highlights the impact of the demographic differences on virus transmission, and the older population has a greater influence on virus transmission than other age groups. Besides, the results indicate that behavioral changes in the population and their vaccination play a key role in curbing COVID-19 transmission.
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Affiliation(s)
- Seyyed-Mahdi Hosseini-Motlagh
- School of Industrial Engineering, Iran University of Science and Technology, University Ave, Narmak, Tehran 16846, Iran
| | - Mohammad Reza Ghatreh Samani
- School of Industrial Engineering, Iran University of Science and Technology, University Ave, Narmak, Tehran 16846, Iran
| | - Shamim Homaei
- School of Industrial Engineering, Iran University of Science and Technology, University Ave, Narmak, Tehran 16846, Iran
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22
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Self SCW, Huang R, Amin S, Ewing J, Rudisill C, McLain AC. A Bayesian susceptible-infectious-hospitalized-ventilated-recovered model to predict demand for COVID-19 inpatient care in a large healthcare system. PLoS One 2022; 17:e0260595. [PMID: 36520809 PMCID: PMC9754233 DOI: 10.1371/journal.pone.0260595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 11/12/2021] [Indexed: 12/23/2022] Open
Abstract
The COVID-19 pandemic has strained healthcare systems in many parts of the United States. During the early months of the pandemic, there was substantial uncertainty about whether the large number of COVID-19 patients requiring hospitalization would exceed healthcare system capacity. This uncertainty created an urgent need to accurately predict the number of COVID-19 patients that would require inpatient and ventilator care at the local level. As the pandemic progressed, many healthcare systems relied on such predictions to prepare for COVID-19 surges and to make decisions regarding staffing, the discontinuation of elective procedures, and the amount of personal protective equipment (PPE) to purchase. In this work, we develop a Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered (SIHVR) model to predict the burden of COVID-19 at the healthcare system level. The Bayesian SIHVR model provides daily estimates of the number of new COVID-19 patients admitted to inpatient care, the total number of non-ventilated COVID-19 inpatients, and the total number of ventilated COVID-19 patients at the healthcare system level. The model also incorporates county-level data on the number of reported COVID-19 cases, and county-level social distancing metrics, making it locally customizable. The uncertainty in model predictions is quantified with 95% credible intervals. The Bayesian SIHVR model is validated with an extensive simulation study, and then applied to data from two regional healthcare systems in South Carolina. This model can be adapted for other healthcare systems to estimate local resource needs.
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Affiliation(s)
- Stella Coker Watson Self
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States of America
| | - Rongjie Huang
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States of America
| | - Shrujan Amin
- Care Coordination Institute, Prisma Health, Greenville, South Carolina, United States of America
| | - Joseph Ewing
- Care Coordination Institute, Prisma Health, Greenville, South Carolina, United States of America
| | - Caroline Rudisill
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States of America
| | - Alexander C. McLain
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States of America
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23
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Ning T, Han Y, Wang J. Highway traffic flow prediction model with multi-component spatial-temporal graph convolution networks. Sci Rep 2022; 12:21617. [PMID: 36517499 PMCID: PMC9749628 DOI: 10.1038/s41598-022-18027-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 08/03/2022] [Indexed: 12/15/2022] Open
Abstract
In order to effectively solve the problems of redundant medical material allocation, unbalanced material allocation, high distribution cost and lack of symmetry caused by unreasonable prediction in the case of sudden epidemic disasters, the prospect theory is introduced to establish a two-stage robust allocation model of medical materials, and the HQDRO based on the two-stage decision model is proposed. Aiming at minimizing the emergency response time and the total number of allocated materials, and taking the dynamic change of medical material demand in the epidemic sealed control area as the constraint condition, a two-stage robust planning model of medical materials based on scenario is established to realize the symmetrical allocation of medical materials under the sudden epidemic situation. Then, the perception model based on demand prediction, symmetry optimization, targeted distribution and psychological expectation of medical materials are constructed. Through the comparative analysis with the fitness of three commonly used algorithms in this field, the effectiveness of the robust configuration model and HQDRO proposed in this paper is verified.
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Affiliation(s)
- Tao Ning
- grid.440687.90000 0000 9927 2735Institute of Computer Science and Engineering, Dalian Minzu University, Dalian, 116000 China ,Big Data Application Technology Key Laboratory of State Ethnic Affairs Commission, Dalian, 116000 China
| | - Yumeng Han
- grid.440687.90000 0000 9927 2735Institute of Computer Science and Engineering, Dalian Minzu University, Dalian, 116000 China ,Big Data Application Technology Key Laboratory of State Ethnic Affairs Commission, Dalian, 116000 China
| | - Jiayu Wang
- grid.440686.80000 0001 0543 8253Institute of Computer and Communication Engineering, Dalian Maritime University, Dalian, 116028 China
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24
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Li N, Zeller MP, Shih AW, Heddle NM, St John M, Bégin P, Callum J, Arnold DM, Akbari-Moghaddam M, Down DG, Jamula E, Devine DV, Tinmouth A. A data-informed system to manage scarce blood product allocation in a randomized controlled trial of convalescent plasma. Transfusion 2022; 62:2525-2538. [PMID: 36285763 DOI: 10.1111/trf.17151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/19/2022] [Accepted: 09/26/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND Equitable allocation of scarce blood products needed for a randomized controlled trial (RCT) is a complex decision-making process within the blood supply chain. Strategies to improve resource allocation in this setting are lacking. METHODS We designed a custom-made, computerized system to manage the inventory and allocation of COVID-19 convalescent plasma (CCP) in a multi-site RCT, CONCOR-1. A hub-and-spoke distribution model enabled real-time inventory monitoring and assignment for randomization. A live CCP inventory system using REDCap was programmed for spoke sites to reserve, assign, and order CCP from hospital hubs. A data-driven mixed-integer programming model with supply and demand forecasting was developed to guide the equitable allocation of CCP at hubs across Canada (excluding Québec). RESULTS 18/38 hospital study sites were hubs with a median of 2 spoke sites per hub. A total of 394.5 500-ml doses of CCP were distributed; 349.5 (88.6%) doses were transfused; 9.5 (2.4%) were wasted due to mechanical damage sustained to the blood bags; 35.5 (9.0%) were unused at the end of the trial. Due to supply shortages, 53/394.5 (13.4%) doses were imported from Héma-Québec to Canadian Blood Services (CBS), and 125 (31.7%) were transferred between CBS regional distribution centers to meet demand. 137/349.5 (39.2%) and 212.5 (60.8%) doses were transfused at hubs and spoke sites, respectively. The mean percentages of total unmet demand were similar across the hubs, indicating equitable allocation, using our model. CONCLUSION Computerized tools can provide efficient and immediate solutions for equitable allocation decisions of scarce blood products in RCTs.
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Affiliation(s)
- Na Li
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.,McMaster Centre for Transfusion Research, Department of Medicine, McMaster University, Hamilton, Ontario, Canada.,Department of Computing and Software, McMaster University, Hamilton, Ontario, Canada
| | - Michelle P Zeller
- McMaster Centre for Transfusion Research, Department of Medicine, McMaster University, Hamilton, Ontario, Canada.,Department of Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada.,Canadian Blood Services, Ottawa, Ontario, Canada
| | - Andrew W Shih
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Pathology and Laboratory Medicine, Vancouver Coastal Health Authority, Vancouver, British Columbia, Canada.,Centre for Blood Research, University of British Columbia, Vancouver, British Columbia, Canada
| | - Nancy M Heddle
- McMaster Centre for Transfusion Research, Department of Medicine, McMaster University, Hamilton, Ontario, Canada.,Canadian Blood Services, Ottawa, Ontario, Canada
| | - Melanie St John
- McMaster Centre for Transfusion Research, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Philippe Bégin
- Section of Allergy, Immunology and Rheumatology, Department of Pediatrics, CHU Sainte-Justine, Montréal, Québec, Canada.,Department of Medicine, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
| | - Jeannie Callum
- Department of Pathology and Molecular Medicine, Kingston Health Sciences Centre and Queen's University, Kingston, Ontario, Canada.,Department of Laboratory Medicine and Molecular Diagnostics, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Donald M Arnold
- McMaster Centre for Transfusion Research, Department of Medicine, McMaster University, Hamilton, Ontario, Canada.,Department of Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada.,Canadian Blood Services, Ottawa, Ontario, Canada
| | - Maryam Akbari-Moghaddam
- McMaster Centre for Transfusion Research, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Douglas G Down
- Department of Computing and Software, McMaster University, Hamilton, Ontario, Canada
| | - Erin Jamula
- McMaster Centre for Transfusion Research, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Dana V Devine
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.,Canadian Blood Services, Vancouver, British Columbia, Canada
| | - Alan Tinmouth
- Canadian Blood Services, Ottawa, Ontario, Canada.,Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
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25
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Impact of ICU strain on outcomes. Curr Opin Crit Care 2022; 28:667-673. [PMID: 36226707 DOI: 10.1097/mcc.0000000000000993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
PURPOSE OF REVIEW Acute surge events result in health capacity strain, which can result in deviations from normal care, activation of contingencies and decisions related to resource allocation. This review discusses the impact of health capacity strain on patient centered outcomes. RECENT FINDINGS This manuscript discusses the lack of validated metrics for ICU strain capacity and a need for understanding the complex interrelationships of strain with patient outcomes. Recent work through the coronavirus disease 2019 pandemic has shown that acute surge events are associated with significant increase in hospital mortality. Though causal data on the differential impact of surge actions and resource availability on patient outcomes remains limited the overall signal consistently highlights the link between ICU strain and critical care outcomes in both normal and surge conditions. SUMMARY An understanding of ICU strain is fundamental to the appropriate clinical care for critically ill patients. Accounting for stain on outcomes in critically ill patients allows for minimization of variation in care and an ability of a given healthcare system to provide equitable, and quality care even in surge scenarios.
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26
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Hosseinzadeh S, Ketabi S, Atighehchian A, Nazari R. Hospital bed capacity management during the COVID-19 outbreak using system dynamics: A case study in Amol public hospitals, Iran. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2022. [DOI: 10.1080/20479700.2022.2149083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
| | - Saeedeh Ketabi
- Department of Management, University of Isfahan, Isfahan, Iran
| | - Arezoo Atighehchian
- Department of Industrial Engineering and Futures Studies, Faculty of Engineering, University of Isfahan, Isfahan, Iran
| | - Roghieh Nazari
- Department of nursing, Amol Faculty of Nursing and Midwifery, Mazandaran University of Medical Sciences, Sari, Iran
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27
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Jin Z(J, Wang Z. Operational and Financial Impacts of Digital Health Technology: A Study on Canadian Healthcare System during the COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15025. [PMID: 36429743 PMCID: PMC9690613 DOI: 10.3390/ijerph192215025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
During COVID-19, hospital capacity was significantly reduced to limit the spread of the pandemic. The limitations affected the efficiency of service delivery. We examined the effects of pandemic-related challenges on patient experience and hypothesize that digital health implementation increased patient satisfaction. We surveyed nationally aggregated data in hospital occupancy, hospital funding and patient experience, and plotted their correlation. We found digital health to contribute to patient experience and service-delivery effectiveness. We evaluate the benefits of digital health in context of hospital service delivery. Post-COVID-19, we recommend a continued implementation of digital health and offer suggestions to further improve its efficiency and cost-effectiveness.
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Affiliation(s)
- Zixin (Jessie) Jin
- Faculty of Arts and Science, Havergal College, Toronto, ON M5N 2H9, Canada
| | - Zongjie Wang
- Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
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28
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Is there an association between hospital staffing levels and inpatient-COVID-19 mortality rates? PLoS One 2022; 17:e0275500. [PMID: 36260606 PMCID: PMC9581383 DOI: 10.1371/journal.pone.0275500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 09/19/2022] [Indexed: 11/05/2022] Open
Abstract
Objective This study aims to investigate the relationship between RNs and hospital-based medical specialties staffing levels with inpatient COVID-19 mortality rates. Methods We relied on data from AHA Annual Survey Database, Area Health Resource File, and UnitedHealth Group Clinical Discovery Database. In phase 1 of the analysis, we estimated the risk-standardized event rates (RSERs) based on 95,915 patients in the UnitedHealth Group Database 1,398 hospitals. We then used beta regression to analyze the association between hospital- and county- level factors with risk-standardized inpatient COVID-19 mortality rates from March 1, 2020, through December 31, 2020. Results Higher staffing levels of RNs and emergency medicine physicians were associated with lower COVID-19 mortality rates. Moreover, larger teaching hospitals located in urban settings had higher COVID-19 mortality rates. Finally, counties with greater social vulnerability, specifically in terms of housing type and transportation, and those with high infection rates had the worst patient mortality rates. Conclusion Higher staffing levels are associated with lower inpatient mortality rates for COVID-19 patients. More research is needed to determine appropriate staffing levels and how staffing levels interact with other factors such as teams, leadership, and culture to impact patient care during pandemics.
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29
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Vecherin S, Chang D, Wells E, Trump B, Meyer A, Desmond J, Dunn K, Kitsak M, Linkov I. Assessment of the COVID-19 infection risk at a workplace through stochastic microexposure modeling. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:712-719. [PMID: 35095095 PMCID: PMC8801387 DOI: 10.1038/s41370-022-00411-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 01/06/2022] [Accepted: 01/11/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND The COVID-19 pandemic has a significant impact on economy. Decisions regarding the reopening of businesses should account for infection risks. OBJECTIVE This paper describes a novel model for COVID-19 infection risks and policy evaluations. METHODS The model combines the best principles of the agent-based, microexposure, and probabilistic modeling approaches. It takes into account specifics of a workplace, mask efficiency, and daily routines of employees, but does not require specific inter-agent rules for simulations. Likewise, it does not require knowledge of microscopic disease related parameters. Instead, the risk of infection is aggregated into the probability of infection, which depends on the duration and distance of every contact. The probability of infection at the end of a workday is found using rigorous probabilistic rules. Unlike previous models, this approach requires only a few reference data points for calibration, which are more easily collected via empirical studies. RESULTS The application of the model is demonstrated for a typical office environment and for a real-world case. CONCLUSION The proposed model allows for effective risk assessment and policy evaluation when there are large uncertainties about the disease, making it particularly suitable for COVID-19 risk assessments.
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Affiliation(s)
- Sergey Vecherin
- Engineer Research and Development Center, Vicksburg, MS, USA.
| | - Derek Chang
- Engineer Research and Development Center, Vicksburg, MS, USA
| | - Emily Wells
- Engineer Research and Development Center, Vicksburg, MS, USA
- Carnegie Mellon University, Pittsburgh, PA, USA
| | - Benjamin Trump
- Engineer Research and Development Center, Vicksburg, MS, USA
| | - Aaron Meyer
- Engineer Research and Development Center, Vicksburg, MS, USA
| | - Jacob Desmond
- Engineer Research and Development Center, Vicksburg, MS, USA
| | - Kyle Dunn
- Engineer Research and Development Center, Vicksburg, MS, USA
| | - Maxim Kitsak
- Delft University of Technology, Delft, Netherlands
| | - Igor Linkov
- Engineer Research and Development Center, Vicksburg, MS, USA.
- Carnegie Mellon University, Pittsburgh, PA, USA.
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30
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Pinson JA, Diep ML, Krishnan V, Aird C, Cooper C, Leong C, Chen J, Ardley N, Paul E, Badawy MK. Imaging volumes during COVID-19: A Victorian health service experience. World J Radiol 2022; 14:293-310. [PMID: 36160832 PMCID: PMC9453320 DOI: 10.4329/wjr.v14.i8.293] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 07/07/2022] [Accepted: 07/22/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND The World Health Organisation declared the coronavirus disease 2019 (COVID-19) a pandemic on March 11, 2020. While globally, the relative caseload has been high, Australia’s has been relatively low. During the pandemic, radiology services have seen significant changes in workflow across modalities and a reduction in imaging volumes.
AIM To investigate differences in modality imaging volumes during the COVID-19 pandemic across a large Victorian public health network.
METHODS A retrospective analysis from January 2019 to December 2020 compared imaging volumes across two periods corresponding to the pandemic’s first and second waves. Weekly volumes across patient class, modality and mobile imaging were summed for periods: wave 1 (weeks 11 to 16 for 2019; weeks 63 to 68 for 2020) and wave 2 (weeks 28 to 43 for 2019; weeks 80 to 95 for 2020). Microsoft Power Business Intelligence linked to the radiology information system was used to mine all completed examinations.
RESULTS Summed weekly data during the pandemic’s first wave showed the greatest decrease of 29.8% in adult outpatient imaging volumes and 46.3% in paediatric emergency department imaging volumes. Adult nuclear medicine demonstrated the greatest decrease of 37.1% for the same period. Paediatric nuclear medicine showed the greatest decrease of 47.8%, with angiography increasing by 50%. The pandemic’s second wave demonstrated the greatest decrease of 23.5% in adult outpatient imaging volumes, with an increase of 18.2% in inpatient imaging volumes. The greatest decrease was 28.5% in paediatric emergency department imaging volumes. Nuclear medicine showed the greatest decrease of 37.1% for the same period. Paediatric nuclear medicine showed the greatest decrease of 36.7%. Mobile imaging utilisation increased between 57.8% and 135.1% during the first and second waves. A strong correlation was observed between mobile and non-mobile imaging in the emergency setting (Spearman’s correlation coefficient = -0.743, P = 0.000). No correlation was observed in the inpatient setting (Spearman’s correlation coefficient = -0.059, P = 0.554).
CONCLUSION Nuclear medicine was most impacted, while computed tomography and angiography were the least affected by the pandemic. The impact was less during the pandemic’s second wave. Mobile imaging shows continuous growth during both waves.
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Affiliation(s)
- Jo-Anne Pinson
- Monash Health Imaging, Monash Health, Clayton, Victoria 3168, Australia
- Department of Medical Imaging, Peninsula Health, Melbourne, Victoria 3099, Australia
- Department of Medical Imaging and Radiation Sciences, School of Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria 3800, Australia
| | - My Linh Diep
- Monash Health Imaging, Monash Health, Clayton, Victoria 3168, Australia
| | - Vinay Krishnan
- Monash Health Imaging, Monash Health, Clayton, Victoria 3168, Australia
| | - Caroline Aird
- Monash Health Imaging, Monash Health, Clayton, Victoria 3168, Australia
| | - Cassie Cooper
- Monash Health Imaging, Monash Health, Clayton, Victoria 3168, Australia
| | - Christopher Leong
- Monash Health Imaging, Monash Health, Clayton, Victoria 3168, Australia
| | - Jeff Chen
- Monash Health Imaging, Monash Health, Clayton, Victoria 3168, Australia
| | - Nicholas Ardley
- Monash Health Imaging, Monash Health, Clayton, Victoria 3168, Australia
| | - Eldho Paul
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3800, Australia
| | - Mohamed Khaldoun Badawy
- Monash Health Imaging, Monash Health, Clayton, Victoria 3168, Australia
- Department of Medical Imaging and Radiation Sciences, School of Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria 3800, Australia
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31
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Kang E, Yun J, Hwang SH, Lee H, Lee JY. The impact of the COVID-19 pandemic in the healthcare utilization in Korea: Analysis of a nationwide survey. J Infect Public Health 2022; 15:915-921. [PMID: 35872432 PMCID: PMC9265238 DOI: 10.1016/j.jiph.2022.07.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/29/2022] [Accepted: 07/04/2022] [Indexed: 10/25/2022] Open
Abstract
BACKGROUND COVID-19 has brought changes in daily life and increased the medical burden. This study aims to evaluate the delays in healthcare services and related factors in the general population during the COVID-19 pandemic. METHODS We took a nationally representative sample and conducted a mobile phone-based survey. The study was conducted anonymously. Of the 3377 subjects who consented to participate, a total of 2097 finished the survey. The primary outcome was respondents' experiences with delayed (1) health screenings, (2) non-urgent medical visits, (3) medical visits for chronic disease, and (4) emergency visits during the COVID-19 pandemic. RESULTS Of 2097 respondents, females, residents of the Seoul metropolitan area, those with private insurance, those without chronic diseases, smokers, and drinkers had higher risk of delays in health screening and non-urgent medical visits after adjustment. Among chronic disease patients, those who were over 60 years old (adjusted odds ratio 0.36, 95% CI 0.14-0.92) showed lower risk of delayed medical visit. Residents of the Seoul metropolitan area, those with private insurance, smokers, and drinkers were all associated with experiencing delayed health screening and non-urgent medical visits had higher risk of delays in chronic disease visits and emergent medical visits. CONCLUSIONS Delayed access to healthcare services is associated with poor outcomes and may cause different complications. Efforts are needed to prevent delays in medical use due to infectious diseases such as COVID-19. Considering the possibility of the emergence of infectious diseases, various countermeasures are needed to prevent delays in medical visit.
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Affiliation(s)
- EunKyo Kang
- National Cancer Control Institute, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si Gyeonggi-do 10408, Republic of Korea; Department of Family Medicine, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si Gyeonggi-do 10408, Republic of Korea
| | - Jieun Yun
- Department of Pharmaceutical Engineering, Cheongju University, 298, Daeseong-ro, Cheongwon-gu, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Soo-Hee Hwang
- HIRA Research Institute, Health Insurance Review & Assessment Service, 60 Hyeoksin-ro, Wonju-si, Gangwon-do, 26465, Republic of Korea
| | - Hyejin Lee
- Department of Family Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeomggi-do 13620, Republic of Korea; Department of Family Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea.
| | - Jin Yong Lee
- HIRA Research Institute, Health Insurance Review & Assessment Service, 60 Hyeoksin-ro, Wonju-si, Gangwon-do, 26465, Republic of Korea; Public Healthcare Center, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Health Policy and Management, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea.
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32
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Li Y, Hou S, Zhang Y, Liu J, Fan H, Cao C. Effect of Travel Restrictions of Wuhan City Against COVID-19: A Modified SEIR Model Analysis. Disaster Med Public Health Prep 2022; 16:1431-1437. [PMID: 33413723 PMCID: PMC8027550 DOI: 10.1017/dmp.2021.5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 12/16/2020] [Accepted: 12/21/2020] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Since December 2019, a new coronavirus viral was initially detected in Wuhan, China. Population migration increases the risk of epidemic transmission. Here, the objective of study is to estimate the output risk quantitatively and evaluate the effectiveness of travel restrictions of Wuhan city. METHODS We proposed a modified susceptible-exposed-infectious-recovered (SEIR) dynamics model to predict the number of coronavirus disease 2019 (COVID-19) symptomatic and asymptomatic infections in Wuhan. And, subsequently, we estimated the export risk of COVID-19 epidemic from Wuhan to other provinces in China. Finally, we estimated the effectiveness of travel restrictions of Wuhan city quantitatively by the export risk on the assumption that the measure was postponed. RESULTS The export risks of COVID-19 varied from Wuhan to other provinces of China. The peak of export risk was January 21-23, 2020. With the travel restrictions of Wuhan delayed by 3, 5, and 7 d, the export risk indexes will increase by 38.50%, 55.89%, and 65.63%, respectively. CONCLUSIONS The results indicate that the travel restrictions of Wuhan reduced the export risk and delayed the overall epidemic progression of the COVID-19 epidemic in China. The travel restrictions of Wuhan city may provide a reference for the control of the COVID-19 epidemic all over the world.
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Affiliation(s)
- Yue Li
- Institute of Disaster Medicine, Tianjin University, Tianjin, P.R. China
| | - Shike Hou
- Institute of Disaster Medicine, Tianjin University, Tianjin, P.R. China
| | - Yongzhong Zhang
- Institute of Disaster Medicine, Tianjin University, Tianjin, P.R. China
| | - Junfeng Liu
- Department of Mathematics, Renai College, Tianjin University, Tianjin, P.R. China
| | - Haojun Fan
- Institute of Disaster Medicine, Tianjin University, Tianjin, P.R. China
| | - Chunxia Cao
- Institute of Disaster Medicine, Tianjin University, Tianjin, P.R. China
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Fluck D, Fry CH, Rankin S, Lewis A, Robin J, Rees J, Finch J, Jones Y, Jones G, Tudose J, Taylor L, Han TS. Does the length of stay in hospital affect healthcare outcomes of patients without COVID-19 who were admitted during the pandemic? A retrospective monocentric study. Intern Emerg Med 2022; 17:1385-1393. [PMID: 35211848 PMCID: PMC8869351 DOI: 10.1007/s11739-022-02945-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 02/02/2022] [Indexed: 11/05/2022]
Abstract
Uncertainties remain if changes to hospital care during the coronavirus disease (COVID-19) pandemic had an adverse impact on the care-quality of non-COVID-19 patients. We examined the association of hospital length of stay (LOS) with healthcare quality indicators in patients admitted with general medical conditions (non-COVID-19). In this retrospective monocentric study at a National Health Service hospital (Surrey), data were collected from 1st April 2019 to 31st March 2021, including the pandemic from 1st March 2020. Primary admissions, in-hospital mortality, post-discharge readmission and mortality were compared between the pre-pandemic (reference group) and pandemic period, according to LOS categories. There were 10,173 (47.7% men) from the pre-pandemic and 11,019 (47.5% men) from the pandemic period; mean (SD) age 68.3 year (20.0) and 68.3 year (19.6), respectively. During the pandemic, primary admission rates for acute cardiac conditions, pulmonary embolism, cerebrovascular accident and malignancy were higher, whilst admission rates for respiratory diseases and common age-related infections, and in-hospital mortality rates were lower. Amongst 19,721 survivors, sex distribution and underlying health status did not significantly differ between admissions before the pandemic and during wave-1 and wave-2 of the pandemic. Readmission rates did not differ between pre-pandemic and pandemic groups within the LOS categories of < 7 and 7-14 days, but were lower for the pandemic group who stayed > 14 days. For patients who died within seven days of admission, in-hospital mortality rates were lower in patients admitted during the pandemic. Mortality rates within 30 days of discharge did not differ between pre-pandemic and pandemic groups, irrespective of the initial hospital LOS. Despite higher rates of admission for serious conditions during the pandemic, in-hospital mortality was lower. Discharge time was similar to that for patients admitted before the pandemic, except it was earlier during the pandemic for those who stayed > 14 days, There were no group differences in quality-care outcomes.
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Affiliation(s)
- David Fluck
- Department of Cardiology, Ashford and St Peter's Hospitals NHS Foundation Trust, Guildford Road, Chertsey, KT16 0PZ, Surrey, UK
| | - Christopher Henry Fry
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, BS8 1TD, UK
| | - Suzanne Rankin
- Department of Medicine, Ashford and St Peter's Hospitals NHS Foundation Trust, Guildford Road, Chertsey, KT16 0PZ, Surrey, UK
| | - Andrea Lewis
- Department of Medicine, Ashford and St Peter's Hospitals NHS Foundation Trust, Guildford Road, Chertsey, KT16 0PZ, Surrey, UK
| | - Jonathan Robin
- Department of Medicine, Ashford and St Peter's Hospitals NHS Foundation Trust, Guildford Road, Chertsey, KT16 0PZ, Surrey, UK
| | - Jacqui Rees
- Department of Quality, Ashford and St Peter's Hospitals NHS Foundation Trust, Guildford Road, Chertsey, KT16 0PZ, Surrey, UK
| | - Jo Finch
- Department of Quality, Ashford and St Peter's Hospitals NHS Foundation Trust, Guildford Road, Chertsey, KT16 0PZ, Surrey, UK
| | - Yvonne Jones
- Department of Quality, Ashford and St Peter's Hospitals NHS Foundation Trust, Guildford Road, Chertsey, KT16 0PZ, Surrey, UK
| | - Gareth Jones
- Department of Quality, Ashford and St Peter's Hospitals NHS Foundation Trust, Guildford Road, Chertsey, KT16 0PZ, Surrey, UK
| | - Julia Tudose
- Department of Quality, Ashford and St Peter's Hospitals NHS Foundation Trust, Guildford Road, Chertsey, KT16 0PZ, Surrey, UK
| | - Liz Taylor
- Department of Quality, Ashford and St Peter's Hospitals NHS Foundation Trust, Guildford Road, Chertsey, KT16 0PZ, Surrey, UK
| | - Thang Sieu Han
- Department of Endocrinology, Ashford and St Peter's Hospitals NHS Foundation Trust, Guildford Road, Chertsey, KT16 0PZ, Surrey, UK.
- Institute of Cardiovascular Research, Royal Holloway, University of London, Egham, TW20 0EX, Surrey, UK.
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Gerami Seresht N. Enhancing resilience in construction against infectious diseases using stochastic multi-agent approach. AUTOMATION IN CONSTRUCTION 2022; 140:104315. [PMID: 35573273 PMCID: PMC9091540 DOI: 10.1016/j.autcon.2022.104315] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 04/18/2022] [Accepted: 04/29/2022] [Indexed: 06/15/2023]
Abstract
To recover from the adverse impacts of COVID-19 on construction and to avoid further losses to the industry in future pandemics, the resilience of construction industry needs to be enhanced against infectious diseases. Currently, there is a gap for modelling frameworks to simulate the spread of infectious diseases in construction projects at micro-level and to test interventions' effectiveness for data-informed decision-making. Here, this gap is addressed by developing a simulation framework using stochastic agent-based modelling, which enables construction researchers and practitioners to simulate and limit the spread of infectious diseases in construction projects. This is specifically important, since the results of a building project case-study reveals that, in comparison to the general population, infectious diseases may spread faster among construction workers and fatalities can be significantly higher. The proposed framework motivates future research on micro-level modelling of infectious diseases and efforts for intervening the spread of diseases in construction projects.
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Affiliation(s)
- Nima Gerami Seresht
- Department of Mechanical and Construction Engineering, Northumbria University, Newcastle Upon Tyne NE1 8ST, United Kingdom
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35
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Feasibility and Safety of Ambulatory Transoral Endoscopic Thyroidectomy via Vestibular Approach (TOETVA). World J Surg 2022; 46:2678-2686. [PMID: 35854011 PMCID: PMC9295883 DOI: 10.1007/s00268-022-06666-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/04/2022] [Indexed: 11/08/2022]
Abstract
Background In search of an ideal cosmesis, transoral endoscopic thyroidectomy via vestibular approach (TOETVA) has recently been introduced to avoid a visible scar. Although ambulatory thyroid surgery is considered safe in carefully selected patients, this remains unclear for TOETVA. Methods All consecutive adult patients who underwent ambulatory TOETVA or open thyroid surgery at a French university hospital were prospectively enrolled from 12/2020 until 11/2021. The primary outcome was postoperative morbidity (recurrent laryngeal nerve (RLN) palsy, re-intervention for bleeding, wound morbidity, or hospital readmission). The secondary outcome was quality of life (QoL), measured by a survey including a validated questionnaire (SF-12) and a modified thyroid surgery questionnaire six weeks after surgery. Results Throughout the study period, 374 patients underwent a unilateral lobectomy or isthmectomy in ambulatory setting, of which 34 (9%) as TOETVA (including 21 (62%) for a possible malignancy). In the TOETVA group, younger age (median 40 (IQR 35–50) vs. 51 (40–60) years, P < 0.001) and lower BMI (median 23.1 (20.9–25.4) vs. 24.9 (22.1–28.9) kg/m2, P = 0.001) were noted. No cases were converted to open cervicotomy. TOETVA was at least as good as open cervicotomy with nil versus four (1%) re-interventions for bleeding, one temporary (5%) versus 13 (4%) (temporary) RLN palsies, and one (<1%) wound infection (open cervicotomy group). No hospital readmissions occurred in all ambulatory surgery patients. No differences were found in physical (P = 0.280) and mental (P = 0.569) QoL between TOETVA and open surgery. Conclusions In carefully selected patients, the feasibility and safety of ambulatory TOETVA are comparable to open surgery. Supplementary Information The online version contains supplementary material available at 10.1007/s00268-022-06666-y.
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Ndayishimiye C, Sowada C, Dyjach P, Stasiak A, Middleton J, Lopes H, Dubas-Jakóbczyk K. Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning-A Scoping Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:8195. [PMID: 35805855 PMCID: PMC9266736 DOI: 10.3390/ijerph19138195] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/28/2022] [Accepted: 06/29/2022] [Indexed: 12/17/2022]
Abstract
The SARS-CoV-2 pandemic has put unprecedented pressure on the hospital sector around the world. It has shown the importance of preparing and planning in the future for an outbreak that overwhelms every aspect of a hospital on a rapidly expanding scale. We conducted a scoping review to identify, map, and systemize existing knowledge about the relationships between COVID-19 and hospital infrastructure adaptation and capacity planning worldwide. We searched the Web of Science, Scopus, and PubMed and hand-searched gray papers published in English between December 2019 and December 2021. A total of 106 papers were included: 102 empirical studies and four technical reports. Empirical studies entailed five reviews, 40 studies focusing on hospital infrastructure adaptation and planning during the pandemics, and 57 studies on modeling the hospital capacity needed, measured mostly by the number of beds. The majority of studies were conducted in high-income countries and published within the first year of the pandemic. The strategies adopted by hospitals can be classified into short-term (repurposing medical and non-medical buildings, remote adjustments, and establishment of de novo structures) and long-term (architectural and engineering modifications, hospital networks, and digital approaches). More research is needed, focusing on specific strategies and the quality assessment of the evidence.
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Affiliation(s)
- Costase Ndayishimiye
- Europubhealth, Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, 31-008 Krakow, Poland
| | - Christoph Sowada
- Health Economics and Social Security Department, Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, 31-008 Krakow, Poland; (C.S.); (K.D.-J.)
| | - Patrycja Dyjach
- Health Care Management, Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, 31-008 Krakow, Poland; (P.D.); (A.S.)
| | - Agnieszka Stasiak
- Health Care Management, Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, 31-008 Krakow, Poland; (P.D.); (A.S.)
| | - John Middleton
- Association of Schools of Public Health in the European Region (ASPHER), 1150 Brussels, Belgium; (J.M.); (H.L.)
| | - Henrique Lopes
- Association of Schools of Public Health in the European Region (ASPHER), 1150 Brussels, Belgium; (J.M.); (H.L.)
- Comité Mondial Pour Les Apprentissages tout au Long de la vie (CMAtlv), Partenaire Officiel de l’UNESCO, 75004 Paris, France
| | - Katarzyna Dubas-Jakóbczyk
- Health Economics and Social Security Department, Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, 31-008 Krakow, Poland; (C.S.); (K.D.-J.)
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Berec L, Smyčka J, Levínský R, Hromádková E, Šoltés M, Šlerka J, Tuček V, Trnka J, Šmíd M, Zajíček M, Diviák T, Neruda R, Vidnerová P. Delays, Masks, the Elderly, and Schools: First Covid-19 Wave in the Czech Republic. Bull Math Biol 2022; 84:75. [PMID: 35726074 PMCID: PMC9208712 DOI: 10.1007/s11538-022-01031-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 05/16/2022] [Indexed: 11/29/2022]
Abstract
Running across the globe for nearly 2 years, the Covid-19 pandemic keeps demonstrating its strength. Despite a lot of understanding, uncertainty regarding the efficiency of interventions still persists. We developed an age-structured epidemic model parameterized with epidemiological and sociological data for the first Covid-19 wave in the Czech Republic and found that (1) starting the spring 2020 lockdown 4 days earlier might prevent half of the confirmed cases by the end of lockdown period, (2) personal protective measures such as face masks appear more effective than just a realized reduction in social contacts, (3) the strategy of sheltering just the elderly is not at all effective, and (4) leaving schools open is a risky strategy. Despite vaccination programs, evidence-based choice and timing of non-pharmaceutical interventions remains an effective weapon against the Covid-19 pandemic.
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Affiliation(s)
- Luděk Berec
- Department of Mathematics, Centre for Mathematical Biology, Faculty of Science, University of South Bohemia, Branišovská 1760, 37005, České Budějovice, Czech Republic. .,Czech Academy of Sciences, Biology Centre, Institute of Entomology, Branišovská 31, 37005, České Budějovice, Czech Republic. .,Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.
| | - Jan Smyčka
- Center for Theoretical Studies, Husova 4, 11000, Prague 1, Czech Republic
| | - René Levínský
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,CERGE-EI, Politických vězňů 7, 11121, Prague 1, Czech Republic
| | - Eva Hromádková
- CERGE-EI, Politických vězňů 7, 11121, Prague 1, Czech Republic
| | - Michal Šoltés
- CERGE-EI, Politických vězňů 7, 11121, Prague 1, Czech Republic
| | - Josef Šlerka
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,New Media Studies, Faculty of Arts, Charles University, Na Příkopě 29, 11000, Prague 1, Czech Republic
| | - Vít Tuček
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Department of Mathematics, University of Zagreb, Bijenička 30, 10000, Zagreb, Croatia
| | - Jan Trnka
- Department of Biochemistry, Cell and Molecular Biology, Third Faculty of Medicine, Charles University, Ruská 87, 100 00, Prague 10, Czech Republic
| | - Martin Šmíd
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou věží 4, 18200, Prague 8, Czech Republic
| | - Milan Zajíček
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou věží 4, 18200, Prague 8, Czech Republic
| | - Tomáš Diviák
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Department of Criminology, School of Social Sciences, University of Manchester, Oxford Rd, Manchester, UK
| | - Roman Neruda
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200, Prague 8, Czech Republic
| | - Petra Vidnerová
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200, Prague 8, Czech Republic
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38
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Alafif T, Alotaibi R, Albassam A, Almudhayyani A. On the prediction of isolation, release, and decease states for COVID-19 patients: A case study in South Korea. ISA TRANSACTIONS 2022; 124:191-196. [PMID: 33451801 PMCID: PMC7785285 DOI: 10.1016/j.isatra.2020.12.053] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 12/26/2020] [Accepted: 12/28/2020] [Indexed: 06/12/2023]
Abstract
A respiratory syndrome COVID-19 pandemic has become a serious public health issue nowadays. The COVID-19 virus has been affecting tens of millions people worldwide. Some of them have recovered and have been released. Others have been isolated and few others have been unfortunately deceased. In this paper, we apply and compare different machine learning approaches such as decision tree models, random forest, and multinomial logistic regression to predict isolation, release, and decease states for COVID-19 patients in South Korea. The prediction can help health providers and decision makers to distinguish the states of infected patients based on their features in early intervention to take an action either by releasing or isolating the patient after the infection. The proposed approaches are evaluated using Data Science for COVID-19 (DS4C) dataset. An analysis of DS4C dataset is also provided. Experimental results and evaluation show that multinomial logistic regression outperforms other approaches with 95% in a state prediction accuracy and a weighted average F1-score of 95%.
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Affiliation(s)
- Tarik Alafif
- Computer Science Department, Jamoum University College, Umm Al-Qura University, Jamoum, Saudi Arabia.
| | - Reem Alotaibi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Ayman Albassam
- Computer Science Department, Jamoum University College, Umm Al-Qura University, Jamoum, Saudi Arabia.
| | - Abdulelah Almudhayyani
- Computer Science Department, Jamoum University College, Umm Al-Qura University, Jamoum, Saudi Arabia.
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Humphreys P, Spratt B, Tariverdi M, Burdett RL, Cook D, Yarlagadda PKDV, Corry P. An Overview of Hospital Capacity Planning and Optimisation. Healthcare (Basel) 2022; 10:826. [PMID: 35627963 PMCID: PMC9140785 DOI: 10.3390/healthcare10050826] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/18/2022] [Accepted: 04/27/2022] [Indexed: 02/04/2023] Open
Abstract
Health care is uncertain, dynamic, and fast growing. With digital technologies set to revolutionise the industry, hospital capacity optimisation and planning have never been more relevant. The purposes of this article are threefold. The first is to identify the current state of the art, to summarise/analyse the key achievements, and to identify gaps in the body of research. The second is to synthesise and evaluate that literature to create a holistic framework for understanding hospital capacity planning and optimisation, in terms of physical elements, process, and governance. Third, avenues for future research are sought to inform researchers and practitioners where they should best concentrate their efforts. In conclusion, we find that prior research has typically focussed on individual parts, but the hospital is one body that is made up of many interdependent parts. It is also evident that past attempts considering entire hospitals fail to incorporate all the detail that is necessary to provide solutions that can be implemented in the real world, across strategic, tactical and operational planning horizons. A holistic approach is needed that includes ancillary services, equipment medicines, utilities, instrument trays, supply chain and inventory considerations.
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Affiliation(s)
- Peter Humphreys
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia; (B.S.); (R.L.B.); (P.K.D.V.Y.); (P.C.)
| | - Belinda Spratt
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia; (B.S.); (R.L.B.); (P.K.D.V.Y.); (P.C.)
| | | | - Robert L. Burdett
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia; (B.S.); (R.L.B.); (P.K.D.V.Y.); (P.C.)
| | - David Cook
- Princess Alexandra Hospital, Brisbane, QLD 4000, Australia;
| | - Prasad K. D. V. Yarlagadda
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia; (B.S.); (R.L.B.); (P.K.D.V.Y.); (P.C.)
| | - Paul Corry
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia; (B.S.); (R.L.B.); (P.K.D.V.Y.); (P.C.)
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40
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Booton RD, Powell AL, Turner KME, Wood RM. Modelling the Effect of COVID-19 Mass Vaccination on Acute Hospital Admissions. Int J Qual Health Care 2022; 34:6572765. [PMID: 35459950 DOI: 10.1093/intqhc/mzac031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 03/14/2022] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Managing high levels of acute COVID-19 bed occupancy can affect the quality of care provided to both affected patients and those requiring other hospital services. Mass vaccination has offered a route to reduce societal restrictions while protecting hospitals from being overwhelmed. Yet, early in the mass vaccination effort, the possible impact on future bed pressures remained subject to considerable uncertainty. The aim of this study was to model the effect of vaccination on projections of acute and intensive care bed demand within a one million resident healthcare system located in South West England. METHODS An age-structured epidemiological model of the Susceptible-Exposed-Infectious-Recovered (SEIR) type was fitted to local data up to the time of the study, in early March 2021. Model parameters and vaccination scenarios were calibrated through a system-wide multi-disciplinary working group, comprising public health intelligence specialists, healthcare planners, epidemiologists, and academics. Scenarios assumed incremental relaxations to societal restrictions according to the envisaged UK Government timeline, with all restrictions to be removed by 21 June 2021. RESULTS Achieving 95% vaccine uptake in adults by 31 July 2021 would not avert a third wave in autumn 2021 but would produce a median peak bed requirement approximately 6% (IQR: 1% to 24%) of that experienced during the second wave (January 2021). A two-month delay in vaccine rollout would lead to significantly higher peak bed occupancy, at 66% (11% to 146%) of that of the second wave. If only 75% uptake was achieved (the amount typically associated with vaccination campaigns) then the second wave peak for acute and intensive care beds would be exceeded by 4% and 19% respectively, an amount which would seriously pressure hospital capacity. CONCLUSION Modelling influenced decision making among senior managers in setting COVID-19 bed capacity levels, as well as highlighting the importance of public health in promoting high vaccine uptake among the population. Forecast accuracy has since been supported by actual data collected following the analysis, with observed peak bed occupancy falling comfortably within the inter-quartile range of modelled projections.
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Affiliation(s)
| | - Anna L Powell
- Modelling and Analytics, UK National Health Service (BNSSG CCG), UK
| | - Katy M E Turner
- Bristol Medical School, University of Bristol, UK.,Health Data Research UK South West Better Care Partnership, UK
| | - Richard M Wood
- Modelling and Analytics, UK National Health Service (BNSSG CCG), UK.,Health Data Research UK South West Better Care Partnership, UK.,School of Management, University of Bath, UK
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41
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Finn Z, Carter P, Rogers D, Burnett A. Prehospital COVID19-related Encounters Predict Future Hospital Utilization. PREHOSP EMERG CARE 2022; 27:297-302. [PMID: 35412382 DOI: 10.1080/10903127.2022.2064946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Objective: Identify if prehospital patient encounters can predict SARS-CoV-2 (COVID19) related hospital utilization. Methods: EMS data from COVID19-related prehospital encounters was pulled from NEMSIS systems in Minnesota. This data was plotted against hospital general medical-surgical bed and ICU bed usage during the initial COVID19 surge and again during a second surge. A validation dataset from 2019 was also utilized. Results: There was a total of 6,460 influenza-like-illness calls, and 2,161 COVID19-specific calls during the studied timeframe. A total of 24,806 medical-surgical bed-days and 20,208 ICU bed-days were analyzed. During initial COVID surge (April-July 2020), EMS encounters best correlated with medical-surgical bed utilization 10 days in the future (r2 = 0.85, N = 113, p = <0.001), with each encounter correlating with a utilization of 7.1 beds. ICU bed utilization was best predicted 16 days in the future (r2 = 0.86, N = 107, p = <0.001) with each encounter correlating with the use of 4.5 ICU beds. Similarly strong and clinically significant correlations were found during the second surged during July and August. There was no significant correlation when comparing to a similar dataset using 2019 ILI calls. Conclusion: Minnesota prehospital COVID19-related prehospital encounters are shown to accurately predict hospital bed utilization 1-2 weeks in advance. This was reproducible across two COVID19 surges. Trends in EMS patient encounters could serve as a valuable data point in predicting COVID19 surges and their effects on hospital utilization.
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Affiliation(s)
- Zachary Finn
- Regions Hospital Emergency Medical Services, Saint Paul, Minnesota
| | | | - David Rogers
- Minnesota Emergency Medical Services Regulatory Board, Minneapolis, Minnesota
| | - Aaron Burnett
- Regions Hospital Emergency Medical Services, Saint Paul, Minnesota
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42
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Hadley E, Rhea S, Jones K, Li L, Stoner M, Bobashev G. Enhancing the prediction of hospitalization from a COVID-19 agent-based model: A Bayesian method for model parameter estimation. PLoS One 2022; 17:e0264704. [PMID: 35231066 PMCID: PMC8887758 DOI: 10.1371/journal.pone.0264704] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/15/2022] [Indexed: 12/18/2022] Open
Abstract
Agent-based models (ABMs) have become a common tool for estimating demand for hospital beds during the COVID-19 pandemic. A key parameter in these ABMs is the probability of hospitalization for agents with COVID-19. Many published COVID-19 ABMs use either single point or age-specific estimates of the probability of hospitalization for agents with COVID-19, omitting key factors: comorbidities and testing status (i.e., received vs. did not receive COVID-19 test). These omissions can inhibit interpretability, particularly by stakeholders seeking to use an ABM for transparent decision-making. We introduce a straightforward yet novel application of Bayes' theorem with inputs from aggregated hospital data to better incorporate these factors in an ABM. We update input parameters for a North Carolina COVID-19 ABM using this approach, demonstrate sensitivity to input data selections, and highlight the enhanced interpretability and accuracy of the method and the predictions. We propose that even in tumultuous scenarios with limited information like the early months of the COVID-19 pandemic, straightforward approaches like this one with discrete, attainable inputs can improve ABMs to better support stakeholders.
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Affiliation(s)
- Emily Hadley
- RTI International, Durham, NC, United States of America
| | - Sarah Rhea
- RTI International, Durham, NC, United States of America
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, United States of America
| | - Kasey Jones
- RTI International, Durham, NC, United States of America
| | - Lei Li
- RTI International, Durham, NC, United States of America
| | - Marie Stoner
- RTI International, Durham, NC, United States of America
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43
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Feld Y, Hartmann AK. Large deviations of a susceptible-infected-recovered model around the epidemic threshold. Phys Rev E 2022; 105:034313. [PMID: 35428162 DOI: 10.1103/physreve.105.034313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 03/04/2022] [Indexed: 06/14/2023]
Abstract
We numerically study the dynamics of the SIR disease model on small-world networks by using a large-deviation approach. This allows us to obtain the probability density function of the total fraction of infected nodes and of the maximum fraction of simultaneously infected nodes down to very small probability densities like 10^{-2500}. We analyze the structure of the disease dynamics and observed three regimes in all probability density functions, which correspond to quick mild, quick extremely severe, and sustained severe dynamical evolutions, respectively. Furthermore, the mathematical rate functions of the densities are investigated. The results indicate that the so-called large-deviation property holds for the SIR model. Finally, we measured correlations with other quantities like the duration of an outbreak or the peak position of the fraction of infections, also in the rare regions which are not accessible by standard simulation techniques.
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Affiliation(s)
- Yannick Feld
- Institut für Physik, Carl von Ossietzky Universität Oldenburg, 26111 Oldenburg, Germany
| | - Alexander K Hartmann
- Institut für Physik, Carl von Ossietzky Universität Oldenburg, 26111 Oldenburg, Germany
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Preiss A, Hadley E, Jones K, Stoner MC, Kery C, Baumgartner P, Bobashev G, Tenenbaum J, Carter C, Clement K, Rhea S. Incorporation of near-real-time hospital occupancy data to improve hospitalization forecast accuracy during the COVID-19 pandemic. Infect Dis Model 2022; 7:277-285. [PMID: 35136849 PMCID: PMC8813201 DOI: 10.1016/j.idm.2022.01.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 01/28/2022] [Accepted: 01/29/2022] [Indexed: 10/28/2022] Open
Abstract
Public health decision makers rely on hospitalization forecasts to inform COVID-19 pandemic planning and resource allocation. Hospitalization forecasts are most relevant when they are accurate, made available quickly, and updated frequently. We rapidly adapted an agent-based model (ABM) to provide weekly 30-day hospitalization forecasts (i.e., demand for intensive care unit [ICU] beds and non-ICU beds) by state and region in North Carolina for public health decision makers. The ABM was based on a synthetic population of North Carolina residents and included movement of agents (i.e., patients) among North Carolina hospitals, nursing homes, and the community. We assigned SARS-CoV-2 infection to agents using county-level compartmental models and determined agents' COVID-19 severity and probability of hospitalization using synthetic population characteristics (e.g., age, comorbidities). We generated weekly 30-day hospitalization forecasts during May-December 2020 and evaluated the impact of major model updates on statewide forecast accuracy under a SARS-CoV-2 effective reproduction number range of 1.0-1.2. Of the 21 forecasts included in the assessment, the average mean absolute percentage error (MAPE) was 7.8% for non-ICU beds and 23.6% for ICU beds. Among the major model updates, integration of near-real-time hospital occupancy data into the model had the largest impact on improving forecast accuracy, reducing the average MAPE for non-ICU beds from 6.6% to 3.9% and for ICU beds from 33.4% to 6.5%. Our results suggest that future pandemic hospitalization forecasting efforts should prioritize early inclusion of hospital occupancy data to maximize accuracy.
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Affiliation(s)
| | - Emily Hadley
- RTI International, Research Triangle Park, NC, USA
| | - Kasey Jones
- RTI International, Research Triangle Park, NC, USA
| | | | | | | | | | - Jessica Tenenbaum
- North Carolina Department of Health and Human Services, Raleigh, NC, USA
| | - Charles Carter
- North Carolina Department of Health and Human Services, Raleigh, NC, USA
| | - Kimberly Clement
- North Carolina Department of Health and Human Services, Raleigh, NC, USA
| | - Sarah Rhea
- North Carolina State University, Department of Population Health and Pathobiology, Raleigh, NC, USA
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Campos AT, Dos Santos CH, Gabriel GT, Montevechi JAB. Safety assessment for temporary hospitals during the COVID-19 pandemic: A simulation approach. SAFETY SCIENCE 2022; 147:105642. [PMID: 34955606 PMCID: PMC8692075 DOI: 10.1016/j.ssci.2021.105642] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 06/18/2021] [Accepted: 12/16/2021] [Indexed: 05/15/2023]
Abstract
Amid the devastating effects caused by the pandemic of the new Coronavirus (COVID-19), health leaders around the world are adding efforts to search efficient and effective responses in the fight against the disease. Conventional health centers, such as hospitals and emergency departments have been registering an increase in demand and atypical patterns due to the high transmissibility of the virus. In this context, the adoption of Temporary Hospitals (THs) is effective in trying to relieve conventional hospitals and direct efforts in the treatment of suspected and positive patients for COVID-19. However, some requirements should be considered regarding the processes performed by THs to maintain the health and safety of patients and staff. Based on the literature, we evaluated aspects related to patient safety in THs, especially linked to biosafety of medical facilities, and patient transport and visit. We highlight the analysis of flows and layouts, hospital cleaning and patient care. We described two case studies to demonstrate the proposed approach. As result, simulation tests improved safety metrics, such as waiting time for procedures, movement intensity in each area, length of stay and TH capacity. We conclude that the approach allows us to provide better THs that prevent cross-contamination, provide suitable care, and meet the demand.
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Affiliation(s)
- Afonso Teberga Campos
- Industrial Engineering and Management Institute, Federal University of Itajubá, Av. BPS, 1303 Itajubá, Minas Gerais, Brazil
| | - Carlos Henrique Dos Santos
- Industrial Engineering and Management Institute, Federal University of Itajubá, Av. BPS, 1303 Itajubá, Minas Gerais, Brazil
| | - Gustavo Teodoro Gabriel
- Industrial Engineering and Management Institute, Federal University of Itajubá, Av. BPS, 1303 Itajubá, Minas Gerais, Brazil
| | - José Arnaldo Barra Montevechi
- Industrial Engineering and Management Institute, Federal University of Itajubá, Av. BPS, 1303 Itajubá, Minas Gerais, Brazil
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Weiner JA, Swiatek PR, Johnson DJ, Louie PK, Harada GK, McCarthy MH, Germscheid N, Cheung JPY, Neva MH, El-Sharkawi M, Valacco M, Sciubba DM, Chutkan NB, An HS, Samartzis D. Spine Surgery and COVID-19: The Influence of Practice Type on Preparedness, Response, and Economic Impact. Global Spine J 2022; 12:249-262. [PMID: 32762354 PMCID: PMC8902318 DOI: 10.1177/2192568220949183] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
STUDY DESIGN Cross-sectional observational cohort study. OBJECTIVE To investigate preparation, response, and economic impact of COVID-19 on private, public, academic, and privademic spine surgeons. METHODS AO Spine COVID-19 and Spine Surgeon Global Impact Survey includes domains on surgeon demographics, location of practice, type of practice, COVID-19 perceptions, institutional preparedness and response, personal and practice impact, and future perceptions. The survey was distributed by AO Spine via email to members (n = 3805). Univariate and multivariate analyses were performed to identify differences between practice settings. RESULTS A total of 902 surgeons completed the survey. In all, 45.4% of respondents worked in an academic setting, 22.9% in privademics, 16.1% in private practice, and 15.6% in public hospitals. Academic practice setting was independently associated with performing elective and emergent spine surgeries at the time of survey distribution. A majority of surgeons reported a >75% decrease in case volume. Private practice and privademic surgeons reported losing income at a higher rate compared with academic or public surgeons. Practice setting was associated with personal protective equipment availability and economic issues as a source of stress. CONCLUSIONS The current study indicates that practice setting affected both preparedness and response to COVID-19. Surgeons in private and privademic practices reported increased worry about the economic implications of the current crisis compared with surgeons in academic and public hospitals. COVID-19 decreased overall clinical productivity, revenue, and income. Government response to the current pandemic and preparation for future pandemics needs to be adaptable to surgeons in all practice settings.
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Affiliation(s)
- Joseph A. Weiner
- Northwestern University, Chicago,
IL, USA,Joseph A. Weiner, Department of Orthopaedic
Surgery, Northwestern University, 676 North Saint Clair Street, Suite 1350,
Chicago, IL, 60611, USA.
| | | | | | | | - Garrett K. Harada
- Rush University Medical Center,
Chicago, IL, USA,The International Spine Research and
Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | | | | | | | | | | | | | | | | | - Howard S. An
- Rush University Medical Center,
Chicago, IL, USA,The International Spine Research and
Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Dino Samartzis
- Rush University Medical Center,
Chicago, IL, USA,The International Spine Research and
Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
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Mu E, Jabbour S, Dalca AV, Guttag J, Wiens J, Sjoding MW. Augmenting existing deterioration indices with chest radiographs to predict clinical deterioration. PLoS One 2022; 17:e0263922. [PMID: 35167608 PMCID: PMC8846502 DOI: 10.1371/journal.pone.0263922] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/29/2022] [Indexed: 12/02/2022] Open
Abstract
IMPORTANCE When hospitals are at capacity, accurate deterioration indices could help identify low-risk patients as potential candidates for home care programs and alleviate hospital strain. To date, many existing deterioration indices are based entirely on structured data from the electronic health record (EHR) and ignore potentially useful information from other sources. OBJECTIVE To improve the accuracy of existing deterioration indices by incorporating unstructured imaging data from chest radiographs. DESIGN, SETTING, AND PARTICIPANTS Machine learning models were trained to predict deterioration of patients hospitalized with acute dyspnea using existing deterioration index scores and chest radiographs. Models were trained on hospitalized patients without coronavirus disease 2019 (COVID-19) and then subsequently tested on patients with COVID-19 between January 2020 and December 2020 at a single tertiary care center who had at least one radiograph taken within 48 hours of hospital admission. MAIN OUTCOMES AND MEASURES Patient deterioration was defined as the need for invasive or non-invasive mechanical ventilation, heated high flow nasal cannula, IV vasopressor administration or in-hospital mortality at any time following admission. The EPIC deterioration index was augmented with unstructured data from chest radiographs to predict risk of deterioration. We compared discriminative performance of the models with and without incorporating chest radiographs using area under the receiver operating curve (AUROC), focusing on comparing the fraction and total patients identified as low risk at different negative predictive values (NPV). RESULTS Data from 6278 hospitalizations were analyzed, including 5562 hospitalizations without COVID-19 (training cohort) and 716 with COVID-19 (216 in validation, 500 in held-out test cohort). At a NPV of 0.95, the best-performing image-augmented deterioration index identified 49 more (9.8%) individuals as low-risk compared to the deterioration index based on clinical data alone in the first 48 hours of admission. At a NPV of 0.9, the EPIC image-augmented deterioration index identified 26 more individuals (5.2%) as low-risk compared to the deterioration index based on clinical data alone in the first 48 hours of admission. CONCLUSION AND RELEVANCE Augmenting existing deterioration indices with chest radiographs results in better identification of low-risk patients. The model augmentation strategy could be used in the future to incorporate other forms of unstructured data into existing disease models.
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Affiliation(s)
- Emily Mu
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Sarah Jabbour
- Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI, United States of America
| | - Adrian V. Dalca
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Martinos Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - John Guttag
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Jenna Wiens
- Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI, United States of America
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, United States of America
| | - Michael W. Sjoding
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, United States of America
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States of America
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Aruta JJBR, Almazan JU, Alamri MS, Adolfo CS, Gonzales F. Measuring mental well-being among frontline nurses during the COVID-19 crisis: Evidence from Saudi Arabia. CURRENT PSYCHOLOGY 2022; 42:1-11. [PMID: 35153454 PMCID: PMC8815723 DOI: 10.1007/s12144-022-02828-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/27/2022] [Indexed: 12/22/2022]
Abstract
In the days of the COVID-19 pandemic, frontline nurses providing care to different communities face are particularly vulnerable to the mental health threats of the crisis. The objective of this study was to examine the structural validity, convergent validity, and reliability of the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) in professional nurses amidst the COVID-19 crisis in Saudi Arabia. Data were collected from 413 nurses in Saudi Arabia using a cross-sectional online survey. Consistent with the original version, results of the confirmatory factor analysis revealed a unidimensional structure of the WEMWBS. Support for convergent validity was found as the WEMWBS significantly correlated with measures of burnout and compassion satisfaction. In terms of reliability, all WEMWBS items yielded high internal consistencies suggesting that the 14 items were robust indicators of mental well-being. In response to the challenges of the COVID-19 crisis, the current study offers a psychometrically sound instrument that can be utilized in screening the mental well-being of nurses in the days of a public health crisis. Preserving the positive aspect of mental health among frontline healthcare workers and promoting quality of care for communities requires a contextualized measurement tool that efficiently assesses mental well-being.
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Affiliation(s)
| | - Joseph U. Almazan
- School of Medicine, Nazarbayev University, Nursultan, 010000 Kazakhstan
| | - Majed Sulaiman Alamri
- Department of Nursing, College of Applied Medical Sciences, University of Hafr Albatin, Hafr Albatin, Saudi Arabia
| | - Cris S. Adolfo
- Department of Nursing, College of Applied Medical Sciences, Majmaah University, Majmaah, 11952 Saudi Arabia
| | - Ferdinand Gonzales
- Medical Surgical Department, College of Nursing, University of Hail, Hail, Saudi Arabia
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Shen L, Levie A, Singh H, Murray K, Desai S. Harnessing Event Report Data to Identify Diagnostic Error During the COVID-19 Pandemic. Jt Comm J Qual Patient Saf 2022; 48:71-80. [PMID: 34844874 PMCID: PMC8553646 DOI: 10.1016/j.jcjq.2021.10.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/12/2021] [Accepted: 10/14/2021] [Indexed: 10/26/2022]
Abstract
INTRODUCTION COVID-19 exposed systemic gaps with increased potential for diagnostic error. This project implemented a new approach leveraging electronic safety reporting to identify and categorize diagnostic errors during the pandemic. METHODS All safety event reports from March 1, 2020, to February 28, 2021, at an academic medical center were evaluated using two complementary pathways (Pathway 1: all reports with explicit mention of COVID-19; Pathway 2: all reports without explicit mention of COVID-19 where natural language processing [NLP] plus logic-based stratification was applied to identify potential cases). Cases were evaluated by manual review to identify diagnostic error/delay and categorize error type using a recently proposed classification framework of eight categories of pandemic-related diagnostic errors. RESULTS A total of 14,230 reports were included, with 95 (0.7%) identified as cases of diagnostic error/delay. Pathway 1 (n = 1,780 eligible reports) yielded 45 reports with diagnostic error/delay (positive predictive value [PPV] = 2.5%), of which 35.6% (16/45) were attributed to pandemic-related strain. In Pathway 2, the NLP-based algorithm flagged 110 safety reports for manual review from 12,450 eligible reports. Of these, 50 reports had diagnostic error/delay (PPV = 45.5%); 94.0% (47/50) were related to strain. Errors from all eight categories of the taxonomy were found on analysis. CONCLUSION An event reporting-based strategy including use of simple-NLP-identified COVID-19-related diagnostic errors/delays uncovered several safety concerns related to COVID-19. An NLP-based approach can complement traditional reporting and be used as a just-in-time monitoring system to enable early detection of emerging risks from large volumes of safety reports.
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Evaluación de los planes de contingencia en la atención a pacientes en unidades de cuidados intensivos en la pandemia COVID-19. J Healthc Qual Res 2022; 37:291-298. [PMID: 35249860 PMCID: PMC8825313 DOI: 10.1016/j.jhqr.2021.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 12/16/2021] [Accepted: 12/17/2021] [Indexed: 11/23/2022]
Abstract
Introducción La expansión de las áreas de cuidados de intensivos ha sido una de las medidas más significativas en esa obligada adaptación a la evolución de las distintas fases de la pandemia por la COVID-19. El objetivo es evaluar el despliegue de los planes de contingencia en la atención a pacientes ingresados en Unidades de Cuidados Intensivos (UCI) durante el periodo del 19 de marzo al 20 de abril de 2020, en un hospital público, referencia para 300.000 habitantes, perteneciente al Servicio Galego de Saúde. Materiales y métodos Investigación cualitativa a partir de grupos focales, con muestreo sistemático. A partir de la adaptación al entorno sanitario de las 10 medidas recomendadas por Deloitte para afrontar una pandemia, se realizó una evaluación por la Unidad de Calidad del Área Sanitaria. Como indicadores de resultados, se evaluaron el número de pacientes con COVID-19, porcentaje de pacientes con ingreso hospitalario, % de pacientes con ingreso en UCI, así como el número de fallecidos por COVID-19, en siete áreas sanitarias en el periodo del 19 marzo al 20 de abril de 2020. Resultados La evaluación cualitativa identificó dos áreas de mejora (comunicación y evaluación de riesgos) de las 10 medidas recomendadas (80%). El área sanitaria presenta la menor tasa relativa (2,6%) y absoluta (16) de pacientes fallecidos, así como la menor tasa relativa (7,9%) y absoluta (24) de pacientes ingresados en servicios de intensivos por COVID-19. La tasa de infección en profesionales ha sido de 4,4%. Conclusiones La simplicidad e identificación de áreas de mejora sumado al escaso consumo de recursos son fortalezas de esta propuesta. Esta autoevaluación puede ser útil para detectar áreas de mejora.
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