Predicting Patient Admission From the Emergency Department Using Administrative and Diagnostic Data

2020 ◽  
Vol 2 (2) ◽  
pp. 1-11
Author(s):  
David W. Savage ◽  
Douglas G. Woolford ◽  
Mackenzie Simpson ◽  
David Wood ◽  
Robert Ohle
Keyword(s):  
Emergency Department ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Patient Flow ◽  
Patient Admission ◽  
Patient Acuity ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Ed Overcrowding ◽  
One Year

Emergency department (ED) overcrowding is a growing problem in Canada. Many interventions have been proposed to increase patient flow. The objective of this study was to predict patient admission early in the visit with the goal of reducing waiting time in ED for admitted patients. ED data for a one-year period from Thunder Bay, Canada was obtained. Initial logistic regression models were developed using age, sex, mode of arrival, and patient acuity as explanatory variables and admission yes or no as the outcome. A second stage prediction was made using the diagnostic tests ordered to further refine the predictive models. Predictive accuracy of the logistic regression model was adequate. The AUC was approximately 81%. By summing the probabilities of patients in the ED, the hourly prediction improved. This study has shown that the number of hospital beds required on an hourly basis can be predicted using triage administrative data.

scholarly journals P112: Predicting patient admission from the emergency department using triage administrative data

CJEM ◽  
2017 ◽  
Vol 19 (S1) ◽  
pp. S116
Author(s):  
D.W. Savage ◽  
B. Weaver ◽  
D. Wood
Keyword(s):  
Emergency Department ◽  
Logistic Regression ◽  
Acute Care ◽  
Administrative Data ◽  
Statistical Models ◽  
Regression Models ◽  
Patient Flow ◽  
Area Under The Curve ◽  
Patient Admission ◽  
Logistic Regression Models

Introduction: Emergency department (ED) over-crowding and increased wait times are a growing problem. Many interventions have been proposed to decrease patient length of stay and increase patient flow. Early disposition planning is one method to accomplish this goal. In this study we developed statistical models to predict patient admission based on ED administrative data. The objective of this study was to predict patient admission early in the visit with goal of preparation of the acute care bed and other resources. Methods: Retrospective administrative ED data from the Thunder Bay Regional Health Sciences Centre was obtained for the period May 2014 to April 2015. Data were divided into training and testing groups with 80% of data used to train the statistical models. Logistic regression models were developed using administrative variables (i.e., age, sex, mode of arrival, and triage level). Model accuracy was evaluated using sensitivity, specificity, and area under the curve measures. To predict hourly bed requirements, the probability of admission was summed to calculate a pooled bed requirement estimate. The estimated hourly bed requirement was then compared to the historical hourly demand. Results: The logistic regression models had a sensitivity of 23%, specificity of 97%, and an area under the curve of 0.78. Although, admission prediction for a particular individual was satisfactory, the hourly pooled probabilities showed better results. The predicted hourly bed requirements were close to historical demand for beds when compared. Conclusion: I have shown that the number of acute care beds required on an hourly basis can be predicted using triage administrative data. Early admission bed planning would allow resources to be managed more effectively. In addition, during periods of hospital over capacity, managers would be able to prioritize transfers and discharges based on early estimates of ED demand for beds.

scholarly journals OP0034 DOES THE RISK OF VENOUS THROMBOEMBOLISM VARY WITH DISEASE ACTIVITY IN RHEUMATOID ARTHRITIS?

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 23.2-24
Author(s):  
V. Molander ◽  
H. Bower ◽  
J. Askling
Keyword(s):  
Rheumatoid Arthritis ◽  
Logistic Regression ◽  
Venous Thromboembolism ◽  
Disease Activity ◽  
Das28 Score ◽  
Logistic Regression Models ◽  
Highly Active ◽  
Increased Risk ◽  
Das28 Remission ◽  
One Year

Background:Patients with rheumatoid arthritis (RA) are at increased risk for venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE) (1). Several established risk factors of VTE, such as age, immobilization and comorbid conditions, occur more often patients with RA (2). In addition, inflammation may in itself also increase VTE risk by upregulating procoagolatory factors and causing endothelial damage (3). Recent reports indicate an increased risk of VTE in RA patients treated with JAK-inhibitors (4), pointing to the need to better understand how inflammation measured as clinical RA disease activity influences VTE risk.Objectives:To investigate the relationship between clinical RA disease activity and incidence of VTE.Methods:Patients with RA were identified from the Swedish Rheumatology Quality Register (SRQ) between July 1st2006 and December 31st2017. Clinical rheumatology data for these patients were obtained from the visits recorded in SRQ, and linked to national registers capturing data on VTE events and comorbid conditions. For each such rheumatologist visit, we defined a one-year period after the visit and determined whether a VTE event had occurred within this period or not. A visit followed by a VTE event was categorized as a case, all other visits were used as controls. Each patient could contribute to several visits. The DAS28 score registered at the visit was stratified into remission (0-2.5) vs. low (2.6-3.1), moderate (3.2-5.1) and high (>5.1) disease activity. Logistic regression with robust cluster standard errors was used to estimate the association between the DAS28 score and VTE.Results:We identified 46,311 patients with RA who contributed data from 320,094 visits. Among these, 2,257 visits (0.7% of all visits) in 1345 unique individuals were followed by a VTE within the one-year window. Of these, 1391 were DVT events and 866 were PE events. Figure 1 displays the absolute probabilities of a VTE in this one-year window, and odds ratios for VTE by each DAS28 category, using DAS28 remission as reference. The one-year risk of a VTE increased from 0.5% in patients in DAS28 remission, to 1.1% in patients with DAS28 high disease activity (DAS28 above 5.1). The age- and sex-adjusted odds ratio for a VTE event in highly active RA compared to RA in remission was 2.12 (95% CI 1.80-2.47). A different analysis, in which each patient could only contribute to one visit, yielded similar results.Figure 1.Odds ratios (OR) comparing the odds of VTE for DAS28 activity categories versus remission. Grey estimates are from unadjusted logistic regression models, black estimates are from logistic regression models adjusted for age and sex. Absolute one-year risk of VTE are estimated from unadjusted models.Conclusion:This study demonstrates a strong association between clinical RA inflammatory activity as measured through DAS28 and risk of VTE. Among patients with high disease activity one in a hundred will develop a VTE within the coming year. These findings highlight the need for proper VTE risk assessment in patients with active RA, and confirm that patients with highly active RA, such as those recruited to trials for treatment with new drugs, are already at particularly elevated risk of VTE.References:[1]Holmqvist et al. Risk of venous thromboembolism in patients with rheumatoid arthritis and association with disease duration and hospitalization. JAMA. 2012;308(13):1350-6.[2]Cushman M. Epidemiology and risk factors for venous thrombosis. Semin Hematol. 2007;44(2):62-9.[3]Xu J et al. Inflammation, innate immunity and blood coagulation. Hamostaseologie. 2010;30(1):5-6, 8-9.[4]FDA. Safety trial finds risk of blood clots in the lungs and death with higher dose of tofacitinib (Xeljanz, Xeljanz XR) in rheumatoid arthritis patients; FDA to investigate. 2019.Acknowledgments:Many thanks to all patients and rheumatologists persistently filling out the SRQ.Disclosure of Interests:Viktor Molander: None declared, Hannah Bower: None declared, Johan Askling Grant/research support from: JA acts or has acted as PI for agreements between Karolinska Institutet and the following entities, mainly in the context of the ARTIS national safety monitoring programme of immunomodulators in rheumatology: Abbvie, BMS, Eli Lilly, Merck, MSD, Pfizer, Roche, Samsung Bioepis, Sanofi, and UCB Pharma

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks

2017 ◽  
Vol 56 (05) ◽  
pp. 377-389 ◽  
Author(s):  
Xingyu Zhang ◽  
Joyce Kim ◽  
Rachel E. Patzer ◽  
Stephen R. Pitts ◽  
Aaron Patzer ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emergency Department ◽  
Logistic Regression ◽  
Natural Language Processing ◽  
Natural Language ◽  
Hospital Admission ◽  
Language Processing ◽  
Predictive Accuracy ◽  
Free Text

SummaryObjective: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements.Methods: Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient’s reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model.Results: Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.7310.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN.Conclusions: The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient’s reason for visit regardless of modeling approach. Natural language processing and neural networks that incorporate patient-reported outcome free text may increase predictive accuracy for hospital admission.

scholarly journals An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 200
Author(s):  
Youssef Zizi ◽  
Amine Jamali-Alaoui ◽  
Badreddine El Goumi ◽  
Mohamed Oudgou ◽  
Abdeslam El Moudden
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Financial Distress ◽  
Prediction Models ◽  
Type I ◽  
Return On Assets ◽  
Logistic Regression Models ◽  
The Face ◽  
One Year ◽  
Type Ii Errors

In the face of rising defaults and limited studies on the prediction of financial distress in Morocco, this article aims to determine the most relevant predictors of financial distress and identify its optimal prediction models in a normal Moroccan economic context over two years. To achieve these objectives, logistic regression and neural networks are used based on financial ratios selected by lasso and stepwise techniques. Our empirical results highlight the significant role of predictors, namely interest to sales and return on assets in predicting financial distress. The results show that logistic regression models obtained by stepwise selection outperform the other models with an overall accuracy of 93.33% two years before financial distress and 95.00% one year prior to financial distress. Results also show that our models classify distressed SMEs better than healthy SMEs with type I errors lower than type II errors.

Visualizing Emergency Department Admission through Data Mining

2019 ◽  
Vol 1 (2) ◽  
pp. 19-31
Author(s):  
Kalaivani S ◽  
Shalini Dhiman ◽  
Rajagopal T.K.P.
Keyword(s):  
Emergency Department ◽  
Logistic Regression ◽  
Prediction Models ◽  
Personal Data ◽  
Mobility Data ◽  
Novel Approach ◽  
Key Factor ◽  
Ed Overcrowding ◽  
Historic Data ◽  
Short Time

Emergency Department (ED) boarding –the inability to transfer emergency patients to inpatient beds- is a key factor contributing to ED overcrowding. This paper presents a novel approach to improving hospital operational efficiency and, therefore, to decreasing ED boarding. Using the historic data of 15,000 patients, admission results and patient information are correlated in order to identify important admission predictor factors. For example, the type of radiology exams prescribed by the ED physician is identified as among the most important predictors of admission. Based on these  factors, a  real-time prediction  model is  developed which  is able  to correctly predict  the  admission  result  of  four  out  of  every  five  ED  patients.  The  proposed admission  model  can  be  used  by inpatient  units  to  estimate  the  likelihood  of ED patients’ admission, and consequently, the number of incoming patients from ED in the near future. Using  similar prediction models,  hospitals can evaluate their short-time needs for inpatient care more accurately Emergency Department (ED) boarding – the inability to transfer emergency patients to inpatient beds- is a key factor contributing to ED overcrowding. This paper presents a novel approach to improving hospital operational efficiency and, therefore, to decreasing ED boarding. Using the historic data of 15,000 patients, admission results and patient information are correlated in order to identify important admission predictor factors. For example, the type of radiology exams prescribed by the ED physician is identified as among the most important predictors of admission. The proposed admission model can be used by inpatient units to estimate the likelihood of ED patients’ admission, and consequently, the number of incoming patients from ED in the near future. Using similar prediction models, hospitals can evaluate their short-time needs for inpatient care more accurately. We use three algorithms to build the predictive models: (1) logistic regression, (2) decision trees, and Analytic tools (accuracy=80.31%, AUC-ROC=0.859) than the decision tree accuracy=80.06%, AUC-ROC=0.824) and the logistic regression model (accuracy=79.94%, AUC-ROC=0.849). Drawing on logistic regression, we identify several factors related to hospital admissions including hospital site, age, arrival mode, triage category, care group, previous admission in the past month, and previous admission in the past year. From a different perspective, the research focuses on mobility data instead of personal data in general using Structural Equation Modelling analysis method. Based on this research finding, we identified an unexplored factor that can be used to predict the intention to disclose mobility data, and the result also confirmed that context aspects such as demographics and different personal data categories.

scholarly journals The Sydney triage to admission risk tool (START) to improve patient flow in an emergency department: a model of care implementation pilot study

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Anja Ebker-White ◽  
Kendall J. Bein ◽  
Saartje Berendsen Russell ◽  
Michael M. Dinh
Keyword(s):  
Emergency Department ◽  
Length Of Stay ◽  
Patient Flow ◽  
Intervention Group ◽  
Control Sample ◽  
Patient Admission ◽  
Implementation Study ◽  
Model Of Care ◽  
Early Assessment ◽  
Pilot Implementation

Abstract Background The Sydney Triage to Admission Risk Tool (START) is a validated clinical analytics tool designed to estimate the probability of in-patient admission based on Emergency Department triage characteristics. Methods This was a single centre pilot implementation study using a matched case control sample of patients assessed at ED triage. Patients in the intervention group were identified at triage by the START tool as likely requiring in-patient admission and briefly assessed by an ED Consultant. Bed management were notified of these patients and their likely admitting team based on senior early assessment. Matched controls were identified on the same day of presentation if they were admitted to the same in-patient teams as patients in the intervention group and same START score category. Outcomes were ED length of stay and proportion of patients correctly classified as an in-patient admission by the START tool. Results One hundred and thirteen patients were assessed using the START-based model of care. When compared with matched control patients, this intervention model of care was associated with a significant reduction in ED length of stay [301 min (IQR 225–397) versus 423 min (IQR 297–587) p < 0.001] and proportion of patients meeting 4 h length of stay thresholds increased from 24 to 45% (p < 0.001). Conclusion In this small pilot implementation study, the START tool, when used in conjunction with senior early assessment was associated with a reduction in ED length of stay. Further controlled studies are now underway to further examine its utility across other ED settings.

Bottleneck detection for improvement of Emergency Department efficiency

2015 ◽  
Vol 21 (3) ◽  
pp. 564-585 ◽  
Author(s):  
Yuancheng Zhao ◽  
Qingjin Peng ◽  
Trevor Strome ◽  
Erin Weldon ◽  
Michael Zhang ◽  
...  
Keyword(s):  
Emergency Department ◽  
Simulation Modeling ◽  
Treatment Time ◽  
Patient Flow ◽  
Current Model ◽  
Public Healthcare ◽  
Time Saving ◽  
Content Type ◽  
Bottleneck Detection ◽  
Ed Overcrowding

Purpose – The purpose of this paper is to introduce a method of the bottleneck detection for Emergency Department (ED) improvement using benchmarking and design of experiments (DOE) in simulation model. Design/methodology/approach – Four procedures of treatments are used to represent ED activities of the patient flow. Simulation modeling is applied as a cost-effective tool to analyze the ED operation. Benchmarking provides the achievable goal for the improvement. DOE speeds up the process of bottleneck search. Findings – It is identified that the long waiting time is accumulated by previous arrival patients waiting for treatment in the ED. Comparing the processing time of each treatment procedure with the benchmark reveals that increasing the treatment time mainly happens in treatment in progress and emergency room holding (ERH) procedures. It also indicates that the to be admitted time caused by the transfer delay is a common case. Research limitations/implications – The current research is conducted in the ED only. Activities in the ERH require a close cooperation of several medical teams to complete patients’ condition evaluations. The current model may be extended to the related medical units to improve the model detail. Practical implications – ED overcrowding is an increasingly significant public healthcare problem. Bottlenecks that affect ED overcrowding have to be detected to improve the patient flow. Originality/value – Integration of benchmarking and DOE in simulation modeling proposed in this research shows the promise in time-saving for bottleneck detection of ED operations.

scholarly journals Forecasting Daily Emergency Department Arrivals Using High-Dimensional Multivariate Data: A Feature Selection Approach

2021 ◽  
Author(s):  
Jalmari Tuominen ◽  
Francesco Lomio ◽  
Niku Oksala ◽  
Ari Palomäki ◽  
Jaakko Peltonen ◽  
...  
Keyword(s):  
Emergency Department ◽  
Feature Selection ◽  
Predictive Accuracy ◽  
Recursive Least Squares ◽  
High Dimensional ◽  
Explanatory Variables ◽  
Public Events ◽  
Selection Processes ◽  
Selection Approach ◽  
Feature Selection Approach

Abstract Background and Objective Emergency Department (ED) overcrowding is a chronic international issue that is associated with adverse treatment outcomes. Accurate forecasts of future service demand would enable intelligent resource allocation that could alleviate the problem. There has been continued academic interest in ED forecasting but the number of used explanatory variables has been low, limited mainly to calendar and weather variables. In this study we investigate whether predictive accuracy of next day arrivals could be enhanced using high number of potentially relevant explanatory variables and document two feature selection processes that aim to identify which subset of variables is associated with number of next day arrivals.Methods We extracted numbers of total daily arrivals from Tampere University Hospital ED between the time period of June 1, 2015 and June 19, 2019. 158 potential explanatory variables were collected from multiple data sources consisting not only of weather and calendar variables but also an extensive list of local public events, numbers of website visits to two hospital domains, numbers of available hospital beds in 33 local hospitals or health centres and Google trends searches for the ED. We used two feature selection processes: Simulated Annealing (SA) and Floating Search (FS) with Recursive Least Squares (RLS) and Least Mean Squares (LMS). Performance of these approaches was compared against autoregressive integrated moving average (ARIMA), regression with ARIMA errors (ARIMAX) and Random Forest (RF). Mean Absolute Percentage Error (MAPE) was used as the main error metric.Results Calendar variables, load of secondary care facilities and local public events were dominant in the identified predictive features. RLS-SA and RLS-FA provided slightly better accuracy compared ARIMA. ARIMAX was the most accurate model but the difference between RLS-SA and RLS-FA was not statistically significant.Conclusions Our study provides new insight into potential underlying factors associated with number of next day presentations. It also suggests that predictive accuracy of next day arrivals can be increased using high-dimensional feature selection approach when compared to both univariate and nonfiltered high-dimensional approach. However, outperforming ARIMAX remains a challenge when working with daily data. Future work should focus on enhancing the feature selection mechanism, investigating its applicability to other domains and in identifying other potentially relevant explanatory variables.

Comparison of logistic regression and linear discriminant analysis

2004 ◽  
Vol 1 (1) ◽  
pp. 143-161
Author(s):  
Maja Pohar ◽  
Mateja Blas ◽  
Sandra Turk
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Predictive Accuracy ◽  
Classification Models ◽  
Linear Classification ◽  
Robust Method ◽  
Linear Discriminant ◽  
Explanatory Variables ◽  
The Impact

Two of the most widely used statistical methods for analyzing categorical outcome variables are linear discriminant analysis and logistic regression. While both are appropriate for the development of linear classification models, linear discriminant analysis makes more assumptions about the underlying data. Hence, it is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions. In this paper we consider the problem of choosing between the two methods, and set some guidelines for proper choice. The comparison between the methods is based on several measures of predictive accuracy. The performance of the methods is studied by simulations. We start with an example where all the assumptions of the linear discriminant analysis are satisfied and observe the impact of changes regarding the sample size, covariance matrix, Mahalanobis distance and direction of distance between group means. Next, we compare the robustness of the methods towards categorisation and non-normality of explanatory variables in a closely controlled way. We show that the results of LDA and LR are close whenever the normality assumptions are not too badly violated, and set some guidelines for recognizing these situations. We discuss the inappropriateness of LDA in all other cases.

An investigation on fatality of drivers in vehicle–fixed object accidents on expressways in China: Using multinomial logistic regression model

2018 ◽  
Vol 232 (7) ◽  
pp. 643-654
Author(s):  
Yong Peng ◽  
Shuangling Peng ◽  
Xinghua Wang ◽  
Shiyang Tan
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Multinomial Logistic Regression ◽  
Hunan Province ◽  
Driving Experience ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Modeling Process ◽  
Best Fitting ◽  
Bad Weather

This study aims to identify the effects of characteristics of vehicle, roadway, driver, and environment on fatality of drivers in vehicle-fixed object accidents on expressways in Changsha–Zhuzhou–Xiangtan district of Hunan province in China by developing multinomial logistic regression models. For this purpose, 121 vehicle–fixed object accidents from 2011-2017 are included in the modeling process. First, descriptive statistical analysis is made to understand the main characteristics of the vehicle–fixed object crashes. Then, 19 explanatory variables are selected, and correlation analysis of each two variables is conducted to choose the variables to be concluded. Finally, five multinomial logistic regression models including different independent variables are compared, and the model with best fitting and prediction capability is chosen as the final model. The results showed that the turning direction in avoiding fixed objects raised the possibility that drivers would die. About 64% of drivers died in the accident were found being ejected out of the car, of which 50% did not use a seatbelt before the fatal accidents. Drivers are likely to die when they encounter bad weather on the expressway. Drivers with less than 10 years of driving experience are more likely to die in these accidents. Fatigue or distracted driving is also a significant factor in fatality of drivers. Findings from this research provide an insight into reducing fatality of drivers in vehicle–fixed object accidents.

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