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Bienzeisler J, Becker G, Erdmann B, Kombeiz A, Majeed RW, Röhrig R, Greiner F, Otto R, Otto-Sobotka F. The Effects of Displaying the Time Targets of the Manchester Triage System to Emergency Department Personnel: Prospective Crossover Study. J Med Internet Res 2024; 26:e45593. [PMID: 38743464 PMCID: PMC11134237 DOI: 10.2196/45593] [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: 01/09/2023] [Revised: 02/02/2024] [Accepted: 03/31/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND The use of triage systems such as the Manchester Triage System (MTS) is a standard procedure to determine the sequence of treatment in emergency departments (EDs). When using the MTS, time targets for treatment are determined. These are commonly displayed in the ED information system (EDIS) to ED staff. Using measurements as targets has been associated with a decline in meeting those targets. OBJECTIVE This study investigated the impact of displaying time targets for treatment to physicians on processing times in the ED. METHODS We analyzed the effects of displaying time targets to ED staff on waiting times in a prospective crossover study, during the introduction of a new EDIS in a large regional hospital in Germany. The old information system version used a module that showed the time target determined by the MTS, while the new system version used a priority list instead. Evaluation was based on 35,167 routinely collected electronic health records from the preintervention period and 10,655 records from the postintervention period. Electronic health records were extracted from the EDIS, and data were analyzed using descriptive statistics and generalized additive models. We evaluated the effects of the intervention on waiting times and the odds of achieving timely treatment according to the time targets set by the MTS. RESULTS The average ED length of stay and waiting times increased when the EDIS that did not display time targets was used (average time from admission to treatment: preintervention phase=median 15, IQR 6-39 min; postintervention phase=median 11, IQR 5-23 min). However, severe cases with high acuity (as indicated by the triage score) benefited from lower waiting times (0.15 times as high as in the preintervention period for MTS1, only 0.49 as high for MTS2). Furthermore, these patients were less likely to receive delayed treatment, and we observed reduced odds of late treatment when crowding occurred. CONCLUSIONS Our results suggest that it is beneficial to use a priority list instead of displaying time targets to ED personnel. These time targets may lead to false incentives. Our work highlights that working better is not the same as working faster.
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Affiliation(s)
- Jonas Bienzeisler
- Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | | | | | - Alexander Kombeiz
- Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Raphael W Majeed
- Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
- Department of Internal Medicine, Universities of Giessen and Marburg Lung Center (UGMLC), German Center for Lung Research (DZL), Giessen, Germany
| | - Rainer Röhrig
- Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Felix Greiner
- Institute for Occupational and Maritime Medicine (ZfAM), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Department of Trauma Surgery, Otto von Guericke University, Magdeburg, Germany
| | - Ronny Otto
- Department of Trauma Surgery, Otto von Guericke University, Magdeburg, Germany
| | - Fabian Otto-Sobotka
- Division of Epidemiology and Biometry, Faculty of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
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Guo LL, Guo LY, Li J, Gu YW, Wang JY, Cui Y, Qian Q, Chen T, Jiang R, Zheng S. Characteristics and Admission Preferences of Pediatric Emergency Patients and Their Waiting Time Prediction Using Electronic Medical Record Data: Retrospective Comparative Analysis. J Med Internet Res 2023; 25:e49605. [PMID: 37910168 PMCID: PMC10652198 DOI: 10.2196/49605] [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: 06/03/2023] [Revised: 07/04/2023] [Accepted: 09/20/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND The growing number of patients visiting pediatric emergency departments could have a detrimental impact on the care provided to children who are triaged as needing urgent attention. Therefore, it has become essential to continuously monitor and analyze the admissions and waiting times of pediatric emergency patients. Despite the significant challenge posed by the shortage of pediatric medical resources in China's health care system, there have been few large-scale studies conducted to analyze visits to the pediatric emergency room. OBJECTIVE This study seeks to examine the characteristics and admission patterns of patients in the pediatric emergency department using electronic medical record (EMR) data. Additionally, it aims to develop and assess machine learning models for predicting waiting times for pediatric emergency department visits. METHODS This retrospective analysis involved patients who were admitted to the emergency department of Children's Hospital Capital Institute of Pediatrics from January 1, 2021, to December 31, 2021. Clinical data from these admissions were extracted from the electronic medical records, encompassing various variables of interest such as patient demographics, clinical diagnoses, and time stamps of clinical visits. These indicators were collected and compared. Furthermore, we developed and evaluated several computational models for predicting waiting times. RESULTS In total, 183,024 eligible admissions from 127,368 pediatric patients were included. During the 12-month study period, pediatric emergency department visits were most frequent among children aged less than 5 years, accounting for 71.26% (130,423/183,024) of the total visits. Additionally, there was a higher proportion of male patients (104,147/183,024, 56.90%) compared with female patients (78,877/183,024, 43.10%). Fever (50,715/183,024, 27.71%), respiratory infection (43,269/183,024, 23.64%), celialgia (9560/183,024, 5.22%), and emesis (6898/183,024, 3.77%) were the leading causes of pediatric emergency room visits. The average daily number of admissions was 501.44, and 18.76% (34,339/183,204) of pediatric emergency department visits resulted in discharge without a prescription or further tests. The median waiting time from registration to seeing a doctor was 27.53 minutes. Prolonged waiting times were observed from April to July, coinciding with an increased number of arrivals, primarily for respiratory diseases. In terms of waiting time prediction, machine learning models, specifically random forest, LightGBM, and XGBoost, outperformed regression methods. On average, these models reduced the root-mean-square error by approximately 17.73% (8.951/50.481) and increased the R2 by approximately 29.33% (0.154/0.525). The SHAP method analysis highlighted that the features "wait.green" and "department" had the most significant influence on waiting times. CONCLUSIONS This study offers a contemporary exploration of pediatric emergency room visits, revealing significant variations in admission rates across different periods and uncovering certain admission patterns. The machine learning models, particularly ensemble methods, delivered more dependable waiting time predictions. Patient volume awaiting consultation or treatment and the triage status emerged as crucial factors contributing to prolonged waiting times. Therefore, strategies such as patient diversion to alleviate congestion in emergency departments and optimizing triage systems to reduce average waiting times remain effective approaches to enhance the quality of pediatric health care services in China.
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Affiliation(s)
- Lin Lin Guo
- Children's Hospital Capital Institute of Pediatrics, Beijing, China
| | - Lin Ying Guo
- Children's Hospital Capital Institute of Pediatrics, Beijing, China
| | - Jiao Li
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yao Wen Gu
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jia Yang Wang
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ying Cui
- Children's Hospital Capital Institute of Pediatrics, Beijing, China
| | - Qing Qian
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ting Chen
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Rui Jiang
- Department of Automation, Tsinghua University, Beijing, China
| | - Si Zheng
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
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Abensur Vuillaume L, Gennai S, Casalino E, Tazarourte K, Bilbault P. An emergency department organizational assessment questionnaire: a Delphi study to create standardized comparators for emergency department directors. Eur J Emerg Med 2023; 30:209-210. [PMID: 37103900 PMCID: PMC10128897 DOI: 10.1097/mej.0000000000001003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/20/2022] [Indexed: 04/28/2023]
Affiliation(s)
| | - Stéphane Gennai
- Université de Reims Champagne-Ardenne, CHU Reims, INSERM, P3Cell, U 1250
- Emergency Department, CHU Reims, Reims
| | - Enrique Casalino
- Service des Urgences Hôpital Bichat, Assistance Publique-Hôpitaux de Paris
- IAME, UMR 1137, Université de Paris Cité, Paris
| | - Karim Tazarourte
- Université Lyon 1, INSERM U1290 RESHAPE
- Service des Urgences-SAMU 69, Hôpital Edouard Herriot, Hospices civils de Lyon, Lyon
| | - Pascal Bilbault
- Emergency Department, Hôpitaux Universitaires de Strasbourg
- INSERM (French National Institute of Health and Medical Research), UMR 1260, Regenerative NanoMedicine, Fédération de Médecine Translationnelle (FMTS), University of Strasbourg, Strasbourg, France
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Gazder U, Almalki Y, Shah Alam M, Arifuzzaman M. The effect of different mobile uses on crash frequency among young drivers: application of statistical models and clustering analysis. Int J Inj Contr Saf Promot 2023; 30:4-14. [PMID: 35763707 DOI: 10.1080/17457300.2022.2092872] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
This study focuses on investigating the use of mobile phones among young drivers by employing an online questionnaire survey data. Ordinal logistic regression model was used for modelling the probabilities of crashes due to different uses of mobile phone while driving. Moreover, binary logistic regression models were used for predicting the probabilities of different uses of mobile phone. Logistic regression models revealed that texting and internet use have the same likelihood of causing crashes. Drivers having prior experience of being fined for mobile phone use, also showed a higher tendency to be involved in 2 crashes. Moreover, these drivers had a higher likelihood of being involved in texting, as compared to other uses of mobile phones. Drivers with more education had a higher tendency for internet use during driving. Drivers who use mobile phone for long periods during driving have a lesser tendency to get involved in texting, internet use or GPS navigation. Moreover, drivers with a previous crash record have less likelihood of being involved in texting. The models of this study can be useful in developing effective road safety measures. Clustering was also applied in this study which reinforced the findings of the statistical analysis and models.
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Affiliation(s)
- Uneb Gazder
- Department of Civil Engineering, University of Bahrain, Isa Town, Bahrain
| | - Yusuf Almalki
- Department of Civil Engineering, University of Bahrain, Isa Town, Bahrain
| | - Md Shah Alam
- Department of Civil Engineering, University of Bahrain, Isa Town, Bahrain
| | - Md Arifuzzaman
- Department of Civil and Environmental Engineering, King Faisal University, Al-Ahsa, Saudi Arabia
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Maninchedda M, Proia AS, Bianco L, Aromatario M, Orsi GB, Napoli C. Main Features and Control Strategies to Reduce Overcrowding in Emergency Departments: A Systematic Review of the Literature. Risk Manag Healthc Policy 2023; 16:255-266. [PMID: 36852330 PMCID: PMC9961148 DOI: 10.2147/rmhp.s399045] [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: 12/01/2022] [Accepted: 02/14/2023] [Indexed: 02/25/2023] Open
Abstract
Purpose Overcrowding is a problem that affects emergency departments (ED) all over the world; it occurs due to a disproportion between user demand and the physical, human and structural resources available. Essential prerequisites to assessing and managing the phenomenon are its accurate measurement and an understanding of its impact. The objective of this systematic review is to identify the characteristics of the problem, analyzing the proposed strategies aimed at improving patient flow, delay in services provided and overcrowding of emergency departments. Methods To achieve our objectives, a manual computerized search was performed in the bibliographic databases using as keywords "Emergency Department", "Overcrowding", "Emergency Room", "Emergency Service", "Emergency Unit"",Emergency Ward", "Emergency Outpatient Unit", "Emergency Hospital", "Crowding", "Mass Gathering", "Management" and "Comprehensive Health Care". Two independent reviewers analyzed abstracts, titles and full text articles for admissibility, according to the selected inclusion and exclusion criteria. Results The process lead to include 19 articles. It was possible to group the solutions proposed in five categories: work organization, investment in primary care, creation of new dedicated professional figures, work and structural modifications and implementation of predictive simulation models using mathematical algorithms. Conclusion The most effective measures to guarantee an improvement in the flow of patients are represented by both improving the efficiency of human resources and by developing predictive mathematical models, regardless of the type of hospital and its location. Considering the complexity of EDs and the multiple characteristics of overcrowding and that the causes of crowding are different and site-specific, a careful examination of the specifics of each ED is necessary to identify improving fields.
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Affiliation(s)
- Mario Maninchedda
- Department of Public Health and Infectious Diseases, “Sapienza” University of Rome, Rome, Italy
| | - Anna Silvia Proia
- Department of Public Health and Infectious Diseases, “Sapienza” University of Rome, Rome, Italy
| | - Lavinia Bianco
- Department of Public Health and Infectious Diseases, “Sapienza” University of Rome, Rome, Italy
| | | | - Giovanni Battista Orsi
- Department of Public Health and Infectious Diseases, “Sapienza” University of Rome, Rome, Italy,Sant’ Andrea University Hospital, Rome, Italy
| | - Christian Napoli
- Sant’ Andrea University Hospital, Rome, Italy,Department of Medical Surgical Sciences and Translational Medicine, “Sapienza” University of Rome, Rome, Italy,Correspondence: Christian Napoli, Email
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von Wagner M, Queck A, Beekers P, Tolhuizen L, Synnatschke A, Boesing J, Chatterjea S. Towards accurate and automatic emergency department workflow characterization using a real-time locating system. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2023. [DOI: 10.1080/20479700.2023.2172829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Affiliation(s)
| | | | - Pim Beekers
- Philips Electronics Nederland B.V., Eindhoven, The Netherlands
| | - Ludo Tolhuizen
- Philips Electronics Nederland B.V., Eindhoven, The Netherlands
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Research on Industry Data Analytics on Processing Procedure of Named 3-4-8-2 Components Combination for the Application Identification in New Chain Convenience Store. Processes (Basel) 2023. [DOI: 10.3390/pr11010180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
With the rapid economic boom of Asian countries, the president of Country-A has made great efforts to reform in recent years. The prospect of economic development is promising, and business opportunities are emerging gradually, depicting a prosperous scene; accordingly, people’s livelihood consumption also has changed significantly. The original main point of consumption for urban and rural people was the old and traditional grocery store with poor sanitation, but due to the economic improvement, the quality of consumption has also improved, and convenience stores are gradually replacing grocery store. However, convenience store management involves performance, logistic, competition, and personnel costs. Both whether the store can create a net profit and evaluate and select a new store will be important keys that significantly influence business performance. Therefore, this study attempts to use the industry data analysis method for highlighting a concept of processing an experience procedure of named 3-4-8-2 components combination in two stages. First, in the data preprocessing stage, this research considers 22 condition attributes and two types of decision factors, that include net profit and new store selection, and use both techniques of attribute selection and data discretization through the analysis and prediction of data mining tools. Next, in the experiment execution stage, three well-known classifiers (Bayes net, logistic regression, and J48 decision tree) with past good performance and four models (without preprocessing, with attribute selection, with data discretization, and with attribute selection and data discretization) are used for eight different experiments through two data verification methods (percentage split and cross-validation). Conclusively, three key results are identified from empirical analysis: (1) It is found that the prediction accuracy of the J48 decision tree classifier is relatively high and stable among the three classifiers in this study; at the same time, the J48 decision tree can yield comprehensible knowledge-based rules to instruct interested parties. (2) The results of this study show that the important attributes for the net profit decision attribute include the store type, POS number, and cashier number, while the important attributes for the new store selection include the store type and cashier number. (3) There is a difference in the selection of important attributes. Furthermore, four key valuable contributions are addressed from the empirical results, including academic contributions, enterprise contributions, application contributions, and management contributions. It is expected that the direction of store layout expansion can be found and identified through this study, but there are still many risks hidden behind the considerable business opportunities that need to be carefully managed.
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Ataman MG, Sariyer G, Saglam C, Karagoz A, Unluer EE. Factors Relating to Decision Delay in the Emergency Department: Effects of Diagnostic Tests and Consultations. Open Access Emerg Med 2023; 15:119-131. [PMID: 37143526 PMCID: PMC10153439 DOI: 10.2147/oaem.s384774] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/26/2023] [Indexed: 05/06/2023] Open
Abstract
Purpose The purpose of this study is to investigate the factors increasing waiting time (WT) and length of stay (LOS) in patients, which may cause delays in decision-making in the emergency departments (ED). Patients and Methods Patients who arrived at a training hospital in the central region of Izmir City, Turkey, during the first quarter of 2020 were retrospectively analyzed. WT and LOS were the outcome variables of the study, and gender, age, arrival type, triage level determined based on the clinical acuity, diagnosis encoded based on International Classification of Diseases-10 (ICD-10), the existence of diagnostic tests or consultation status were the identified factors. The significance of the differences in WT and LOS values based on each level of these factors was analyzed using independent sample t-tests and ANOVA. Results While patients for which no diagnostic testing or consultation was requested had a significantly higher WT in EDs, their LOS values were substantially lower than those for which at least one diagnostic test or consultation was ordered (p≤0.001). Besides, elderly and red zone patients and those who arrived by ambulance had significantly lower WT and higher LOS values than other levels for all groups of patients for which laboratory-type or imaging-type diagnostic test or consultation was requested (p≤0.001 for each comparison). Conclusion Besides ordering diagnostic tests or consultation in EDs, different factors may extend patients' WT and LOS values and cause significant decision-making delays. Understanding the patient characteristics associated with longer waiting times and LOS values and, thus, delayed decisions will enable practitioners to improve operations management in EDs.
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Affiliation(s)
- Mustafa Gokalp Ataman
- Department of Emergency Medicine, Bakırçay University Çiğli Training and Research Hospital, İzmir, Turkey
- Correspondence: Mustafa Gokalp Ataman, Department of Emergency Medicine, Bakırçay University Çiğli Training and Research Hospital, 8780/1 Street No: 18 Yeni Mahalle Ata Sanayi / Çiğli, İzmir, Turkey, Tel +90 232 398 37 00, Fax +90 444 35 30, Email
| | - Gorkem Sariyer
- Department of Business Administration, Yaşar University, İzmir, Turkey
| | - Caner Saglam
- Department of Emergency Medicine, University of Health Sciences Bozyaka Training and Research Hospital, İzmir, Turkey
| | - Arif Karagoz
- Department of Emergency Medicine, Bakırçay University Çiğli Training and Research Hospital, İzmir, Turkey
| | - Erden Erol Unluer
- Department of Emergency Medicine, University of Health Sciences Bozyaka Training and Research Hospital, İzmir, Turkey
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Developing a machine learning model to predict patient need for computed tomography imaging in the emergency department. PLoS One 2022; 17:e0278229. [PMID: 36520785 PMCID: PMC9754219 DOI: 10.1371/journal.pone.0278229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 11/13/2022] [Indexed: 12/23/2022] Open
Abstract
Overcrowding is a well-known problem in hospitals and emergency departments (ED) that can negatively impact patients and staff. This study aims to present a machine learning model to detect a patient's need for a Computed Tomography (CT) exam in the emergency department at the earliest possible time. The data for this work was collected from ED at Thunder Bay Regional Health Sciences Centre over one year (05/2016-05/2017) and contained administrative triage information. The target outcome was whether or not a patient required a CT exam. Multiple combinations of text embedding methods, machine learning algorithms, and data resampling methods were experimented with to find the optimal model for this task. The final model was trained with 81, 118 visits and tested on a hold-out test set with a size of 9, 013 visits. The best model achieved a ROC AUC score of 0.86 and had a sensitivity of 87.3% and specificity of 70.9%. The most important factors that led to a CT scan order were found to be chief complaint, treatment area, and triage acuity. The proposed model was able to successfully identify patients needing a CT using administrative triage data that is available at the initial stage of a patient's arrival. By determining that a CT scan is needed early in the patient's visit, the ED can allocate resources to ensure these investigations are completed quickly and patient flow is maintained to reduce overcrowding.
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Sariyer G, Ataman MG, Mangla SK, Kazancoglu Y, Dora M. Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations. ANNALS OF OPERATIONS RESEARCH 2022; 328:1-31. [PMID: 36124052 PMCID: PMC9476441 DOI: 10.1007/s10479-022-04955-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/29/2022] [Indexed: 05/03/2023]
Abstract
Grounded in dynamic capabilities, this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs, it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients, the average daily length of stay (LOS), and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19, and includes data from 238,152 patients. Comparing statistics on daily patient volumes, average LOS, and resource usage, both before and during the COVID-19 pandemic, we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period, these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model, we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158,347, it decreased to 79,805 during-COVID-19. On the other hand, while the average daily LOS was 117.53 min before-COVID-19, this value was calculated to be 165,03 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies, it empirically investigates the impact of different policies on ED operations.
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Affiliation(s)
- Görkem Sariyer
- Yasar University, Department of Business Administration, İzmir, Turkey
| | - Mustafa Gokalp Ataman
- Bakırçay University Çiğli Region Training and Research Hospital, Department of Emergency Medicine, İzmir, Turkey
| | - Sachin Kumar Mangla
- Digital Circular Economy for Sustainbale Development Goals (DCE-SDG), Jindal Global Business School, O P Jindal Global University, Haryana, India
| | - Yigit Kazancoglu
- Yasar University, Department of Logistics Management, İzmir, Turkey
| | - Manoj Dora
- Sustainable Production and Consumption School of Management Anglia Ruskin University, Cambridge, UK
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Sariyer G, Ataman MG. How machine learning facilitates decision making in emergency departments: Modelling diagnostic test orders. Int J Clin Pract 2021; 75:e14980. [PMID: 34637191 DOI: 10.1111/ijcp.14980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/10/2021] [Indexed: 11/28/2022] Open
Abstract
OBJECTIVES Since emergency departments (EDs) are responsible for providing initial care for patients who may need urgent medical care, they are highly sensitive to increased patient delays. A key factor that increases patient delays is ordering diagnostic tests. Therefore, understanding the factors increasing diagnostic test orders and proposing efficient models may facilitate decision making in EDs. METHODS Month and week of the year, day of the week, and daily numbers of patients encoded based on 21 different ICD-10 codes were used as input variables. Daily test frequencies of patients requiring tests from laboratory and imaging services were modelled separately by linear regression models. Although significance of the input variables was identified based on these models, obtained forecasts and residuals were further processed by machine learning techniques to obtain hybrid models. RESULTS Day of the week, and number of patients with ICD-10 codes of 'A00-B99', 'I00-I99', 'J00-J99', 'M00-M99' and 'R00-R99' were significant in both test types. In addition to these, although daily patient frequencies with 'H60-H95', 'N00-N99' and 'O00-O9A' were significant for laboratory services, 'L00-L99', 'S00-T88' and 'Z00-Z99' were significant for imaging services. Although prediction accuracies of regression models were, respectively, as 93.658% and 95.028% for laboratory and imaging services modelling, they increased to 99.997% and 99.995% with the machine learning-integrated hybrid model. CONCLUSION The significant factors identified here can predict increases in use of laboratory and imaging services. This could enable these services to be prepared in advance to reduce ED patient delays, thereby reducing ED overcrowding. The proposed model may also be efficiently used for decision making.
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Affiliation(s)
| | - Mustafa Gökalp Ataman
- Department of Emergency Medicine, Çiğli Training and Research Hospital, Bakırçay University, Izmir, Turkey
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A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19. MATHEMATICS 2021. [DOI: 10.3390/math9212808] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
In the context of the long-term coexistence between COVID-19 and human society, the implementation of personnel health monitoring in construction sites has become one of the urgent needs of current construction management. The installation of infrared temperature sensors on the helmets required to be worn by construction personnel to track and monitor their body temperature has become a relatively inexpensive and reliable means of epidemic prevention and control, but the accuracy of measuring body temperature has always been a problem. This study developed a smart helmet equipped with an infrared temperature sensor and conducted a simulated construction experiment to collect data of temperature and its influencing factors in indoor and outdoor construction operation environments. Then, a Partial Least Square–Back Propagation Neural Network (PLS-BPNN) temperature error compensation model was established to correct the temperature measurement results of the smart helmet. The temperature compensation effects of different models were also compared, including PLS-BPNN with Least Square Regression (LSR), Partial Least Square Regression (PLSR), and single Back Propagation Neural Network (BPNN) models. The results showed that the PLS-BPNN model had higher accuracy and reliability, and the determination coefficient of the model was 0.99377. After using PLS-BPNN model for compensation, the relative average error of infrared body temperature was reduced by 2.745 °C and RMSE was reduced by 0.9849. The relative error range of infrared body temperature detection was only 0.005~0.143 °C.
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Li J, Zhu G, Luo L, Shen W. Big Data-Enabled Analysis of Factors Affecting Patient Waiting Time in the Nephrology Department of a Large Tertiary Hospital. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:5555029. [PMID: 34136109 PMCID: PMC8178001 DOI: 10.1155/2021/5555029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/20/2021] [Indexed: 02/05/2023]
Abstract
The length of waiting time has become an important indicator of the efficiency of medical services and the quality of medical care. Lengthy waiting times for patients will inevitably affect their mood and reduce satisfaction. For patients who are in urgent need of hospitalization, delayed admission often leads to exacerbation of the patient's condition and may threaten the patient's life. We gathered patients' information about outpatient visits and hospital admissions in the Nephrology Department of a large tertiary hospital in western China from January 1st, 2014, to December 31st, 2016, and we used big data-enabled analysis methods, including univariate analysis and multivariate linear regression models, to explore the factors affecting waiting time. We found that gender (P=0.048), the day of issuing the admission card (Saturday, P=0.028), the applied period for admission (P < 0.001), and the registration interval (P < 0.001) were positive influencing factors of patients' waiting time. Disease type (after kidney transplantation, P < 0.001), number of diagnoses (P=0.037), and the day of issuing the admission card (Sunday, P=0.001) were negative factors. A linear regression model built using these data performed well in the identification of factors affecting the waiting time of patients in the Nephrology Department. These results can be extended to other departments and could be valuable for improving patient satisfaction and hospital service quality by identifying the factors affecting waiting time.
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Affiliation(s)
- Jialing Li
- School of Management, Hunan University of Technology and Business, Changsha 410205, China
| | - Guiju Zhu
- School of Management, Hunan University of Technology and Business, Changsha 410205, China
| | - Li Luo
- Business School of Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, China
| | - Wenwu Shen
- Outpatient Department, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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