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Liao HC, Wang YH. A Robust ORMS Framework for Taiwanese Healthcare: Taguchi's Dynamic Method in Action. Healthcare (Basel) 2025; 13:1024. [PMID: 40361802 PMCID: PMC12071905 DOI: 10.3390/healthcare13091024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2025] [Revised: 04/13/2025] [Accepted: 04/26/2025] [Indexed: 05/15/2025] Open
Abstract
The study focused on the design of an ORMS in a medical center in central Taiwan, which also functions as a teaching hospital. Background/Objectives: The research objectives were to design an ORMS simulation system based on the status quo of the operating room planning and scheduling in the medical center, obtain the optimal parameter setting in the ORMS, and find improvement strategies according to the sensitivity analysis based on the optimal parameter setting for total performance. Methods: Taguchi's dynamic method was adopted to design the ORMS under human and material resource constraints. The scope of the study was internal medicine patients of the ORMS. A neural network was used to construct a relationship between parameters and performances. A genetic algorithm was used to obtain the optimal parameter setting for optimal performance. Results: This study successfully established a robust operating room management system (ORMS) to help hospital manager to plan and schedule operating rooms and take the ORMS into account to meet patient needs. Decision-makers can use the insights from the sensitivity analysis to refine their strategies effectively. The sensitivity analysis showed that the impact power (the percentage change in d) of the "number of circulating nurses (-0.15 to -1.25; -0.25 to -1.85)" factor was less than (<) that of the "number of holding nurses (-0.85 to -2.04; -0.91 to -2.07)" factor < that of the "number of preoperative beds (-2.57 to -4.53; -2.23 to -4.10)" factor < that of the "number of anesthetists (-3.13 to -7.50)" factor. Conclusions: In the optimal parameter setting obtained, the number of holding nurses was 18, the number of circulating nurses was 20, the number of anesthetists was 15, and the number of preoperative beds was 12. The optimal performance was 0.91.
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Affiliation(s)
- Hung-Chang Liao
- Department of Health Policy and Management, Chung Shan Medical University, Taichung 40201, Taiwan;
- Department of Medical Management, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
| | - Ya-Huei Wang
- Department of Applied Foreign Languages, Chung Shan Medical University, Taichung 40201, Taiwan
- Department of Medical Education, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
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Chen HLR, Lee PP, Zhao Y, Ng WHC, Zhao J, Tan YEC, Loh BJS, Chow KHP, Tan HK, Tan KWE. The Impact of COVID-19 Pandemic on the Diagnosis, Treatment, and Outcomes of Colorectal Cancer in Singapore. MEDICINA (KAUNAS, LITHUANIA) 2025; 61:138. [PMID: 39859120 PMCID: PMC11766542 DOI: 10.3390/medicina61010138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2024] [Revised: 12/25/2024] [Accepted: 01/09/2025] [Indexed: 01/27/2025]
Abstract
Background and Objectives: During the COVID-19 pandemic, many countries implemented lockdowns and social distancing measures, which may delay the early diagnosis of colorectal cancer (CRC). This study aims to review the impact of the pandemic on the diagnosis and treatment outcomes of CRC. Materials and Methods: Patients who underwent colonoscopy or surgery for CRC were included. The study was divided into the pre-COVID-19 (January 2019-January 2020), early COVID-19 (February-May 2020), recovery (June-December 2020), and heightened alert (January-December 2021) periods. Cox regression was used to model the waiting time to colonoscopy. Multivariable logistic regression identified associations between time periods and incidence of CRC diagnosed. The characteristics and outcomes of the surgical procedures that were performed were compared across the time periods. Results: A total of 18,662 colonoscopies and 1462 surgical procedures were performed in the study period. Compared to the pre-COVID-19 period, there was a longer time to colonoscopy during the recovery (HR: 0.91; 95% CI: 0.87, 0.94) and heightened alert periods (HR: 0.88; 95% CI 0.85, 0.91). The early COVID-19 (OR: 1.36; 95% CI: 1.04, 1.77) and recovery (OR: 1.20; 95% CI: 1.01, 1.43) periods were associated with higher odds of diagnosing CRC. Compared to the pre-COVID-19 period, there was a higher proportion of ASA 4 patients (4.3% vs. 1.3%; p < 0.001) and stage 4 CRC patients (22.2% vs. 16.9%; p = 0.001) that required surgery during the heightened alert period. Similarly, there was a higher proportion of emergency surgeries (22% vs. 13.3%; p = 0.002); diverting stomas (13.5% vs. 10.5%; p = 0.005), and Hartmann's procedures (4.4% vs. 0.4%; p = 0.001) performed during the heightened alert period. Conclusions: The pandemic was associated with a higher proportion of metastatic CRC patients requiring surgery. Healthcare policies should facilitate early cancer screening, diagnosis, and treatment to reduce cancer-related morbidity for future pandemics.
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Affiliation(s)
- Hui Lionel Raphael Chen
- Department of Colorectal Surgery, Singapore General Hospital, Singapore 169608, Singapore
- Duke-NUS Medical School, Singapore 169857, Singapore
| | - Piea Peng Lee
- Division of Surgery & Surgical Oncology, National Cancer Centre Singapore, Singapore 168583, Singapore
| | - Yun Zhao
- Department of Colorectal Surgery, Singapore General Hospital, Singapore 169608, Singapore
- Group Finance Analytics, Singapore Health Services, Singapore 168753, Singapore
| | - Wei Hao Caleb Ng
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Jiashen Zhao
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Yu En Christopher Tan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Bo Jie Sean Loh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Kah-Hoe Pierce Chow
- Duke-NUS Medical School, Singapore 169857, Singapore
- Division of Surgery & Surgical Oncology, National Cancer Centre Singapore, Singapore 168583, Singapore
- SingHealth Duke-NUS Global Health Institute, Singapore 168582, Singapore
| | - Hiang Khoon Tan
- Duke-NUS Medical School, Singapore 169857, Singapore
- Division of Surgery & Surgical Oncology, National Cancer Centre Singapore, Singapore 168583, Singapore
- SingHealth Duke-NUS Global Health Institute, Singapore 168582, Singapore
| | - Kwong-Wei Emile Tan
- Department of Colorectal Surgery, Singapore General Hospital, Singapore 169608, Singapore
- Duke-NUS Medical School, Singapore 169857, Singapore
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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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Duggal A, Scheraga R, Sacha GL, Wang X, Huang S, Krishnan S, Siuba MT, Torbic H, Dugar S, Mucha S, Veith J, Mireles-Cabodevila E, Bauer SR, Kethireddy S, Vachharajani V, Dalton JE. Forecasting disease trajectories in critical illness: comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit. BMJ Open 2024; 14:e079243. [PMID: 38320842 PMCID: PMC10860023 DOI: 10.1136/bmjopen-2023-079243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 01/22/2024] [Indexed: 02/15/2024] Open
Abstract
OBJECTIVE Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness. DESIGN We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm. SETTING AND PARTICIPANTS We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022. RESULTS 5241 patients were included in the analysis. For ICU days 2-7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4-7 for all states. CONCLUSION We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a <10% misclassification rate over the first week of critical illness.
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Affiliation(s)
- Abhijit Duggal
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | - Rachel Scheraga
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | | | - Xiaofeng Wang
- Department of Qualitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA
| | - Shuaqui Huang
- Department of Qualitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA
| | - Sudhir Krishnan
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | - Matthew T Siuba
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | - Heather Torbic
- Department of Pharmacy, Cleveland Clinic, Cleveland, Ohio, USA
| | - Siddharth Dugar
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | - Simon Mucha
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | - Joshua Veith
- Department of Critical Care, Cleveland Clinic, Cleveland, Ohio, USA
| | | | - Seth R Bauer
- Department of Pharmacy, Cleveland Clinic, Cleveland, Ohio, USA
| | | | | | - Jarrod E Dalton
- Department of Qualitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA
- Cleveland Clinic, Cleveland, Ohio, USA
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Mazingi D, Shinondo P, Ihediwa G, Ford K, Ademuyiwa A, Lakhoo K. The impact of the COVID-19 pandemic on paediatric surgical volumes in Africa: A retrospective observational study. J Pediatr Surg 2023; 58:275-281. [PMID: 36404186 PMCID: PMC9618459 DOI: 10.1016/j.jpedsurg.2022.10.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 10/11/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND The aim of this study is to investigate the impact that COVID-19 had on the pattern and trend of surgical volumes, urgency and reason for surgery during the first 6 months of the pandemic in sub-Saharan Africa. METHODS This retrospective facility-based study involved collection of paediatric operation data from operating theatre records across 5 hospitals from 3 countries: Zimbabwe, Zambia and Nigeria over the first half of 2019 and 2020 for comparison. Data concerning diagnosis, procedure, anaesthesia, grade, speciality, NCEPOD classification and indication was collected. The respective dates of enactment of cancellation policies in each country were used to compare changes in weekly median surgical case volume before cancellation using the Wilcoxon Sign-Rank Test. RESULTS A total of 1821 procedures were recorded over the study period. Surgical volumes experienced a precipitous drop overall from a median of 100 cases/week to 50 cases/week coinciding with cancellation of surgical electives. Median accumulated weekly procedures before COVID-related cancellation were significantly different from those after cancellation (p = 0.027). Emergency surgery fell by 23.3% while electives fell by 78,9% (P = 0.042). The most common primary indication for surgery was injury which experienced a 30.5% drop in number of procedures, only exceeded by congenital surgery which dropped 34.7%. CONCLUSIONS The effects of surgical cancellations during the covid-19 pandemic are particularly devastating in African countries where the unmet need and surgical caseload are high. Continued cancellations that have since occurred will cause similar drops in surgical case volume that these health systems may not have the resilience to recover from. LEVEL OF EVIDENCE Level II.
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Affiliation(s)
- Dennis Mazingi
- Department of Surgical Sciences, College of Health Sciences, University of Zimbabwe, Harare, Zimbabwe; Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
| | | | - George Ihediwa
- Paediatric Surgery Unit, Department of Surgery, Lagos University Teaching Hospital, Lagos State, Nigeria
| | - Kathryn Ford
- Department of Specialist Neonatal and Paediatric Surgery, Great Ormond Street Hospital, London, UK
| | - Adesoji Ademuyiwa
- Department of Surgery, College of Medicine, University of Lagos, Lagos, Nigeria
| | - Kokila Lakhoo
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK,Department of Paediatric Surgery, Muhimbili National Hospital, Tanzania
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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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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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