Factors affecting the overcrowding in outpatient healthcare

Bahadori, Mohammadkarim; Teymourzadeh, Ehsan; Ravangard, Ramin; Raadabadi, Mehdi

doi:10.4103/2277-9531.204742

Cited by 28 publications

(22 citation statements)

References 27 publications

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“…Hospital adverse events increase with crowding, and have further effects on patient satisfaction, quality of nursing, treatment, wait time, and length of stay [ 1 – 4 ]. A vast literature about overcrowding focus on the outpatient wards [ 1 , 5 ] and emergence departments [ 4 , 6 ]. Overcrowding appearing in the inpatient wards should also be paid attention to.…”

Section: Introductionmentioning

confidence: 99%

Time series model for forecasting the number of new admission inpatients

Zhou

Zhao

et al. 2018

BMC Med Inform Decis Mak

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BackgroundHospital crowding is a rising problem, effective predicting and detecting managment can helpful to reduce crowding. Our team has successfully proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in the schistosomiasis and hand, foot, and mouth disease forecasting study. In this paper, our aim is to explore the application of the hybrid ARIMA-NARNN model to track the trends of the new admission inpatients, which provides a methodological basis for reducing crowding.MethodsWe used the single seasonal ARIMA (SARIMA), NARNN and the hybrid SARIMA-NARNN model to fit and forecast the monthly and daily number of new admission inpatients. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the forecasting performance among the three models. The modeling time range of monthly data included was from January 2010 to June 2016, July to October 2016 as the corresponding testing data set. The daily modeling data set was from January 4 to September 4, 2016, while the testing time range included was from September 5 to October 2, 2016.ResultsFor the monthly data, the modeling RMSE and the testing RMSE, MAE and MAPE of SARIMA-NARNN model were less than those obtained from the single SARIMA or NARNN model, but the MAE and MAPE of modeling performance of SARIMA-NARNN model did not improve. For the daily data, all RMSE, MAE and MAPE of NARNN model were the lowest both in modeling stage and testing stage.ConclusionsHybrid model does not necessarily outperform its constituents’ performances. It is worth attempting to explore the reliable model to forecast the number of new admission inpatients from different data.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0616-8) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Time series model for forecasting the number of new admission inpatients

Zhou

Zhao

et al. 2018

BMC Med Inform Decis Mak

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“…Wait times for outpatients and the management thereof is a major challenge to providing outpatient services, especially in large centers and hospitals. 26 Studying patient flow in the system will help identify the current status of the system and its bottlenecks and will play an important role in managing the delivery process and, ultimately, reducing wait times for patients. 1 Patient flow assessment results from the studied hospital indicated that more than 91% of a patient's total stay in the system was related to wait time, and more than 82% of total wait time was for physical examinations.…”

Section: Discussionmentioning

confidence: 99%

Patient Flow Analysis in General Hospitals: How Clinical Disciplines Affect Outpatient Wait Times

et al. 2019

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Background: Overcrowding is an important problem for outpatient services in healthcare facilities. Patient flow analysis (PFA) is a useful method for identifying inefficiencies in and facilitating patient flow. Objective: PFA was used to estimate patient wait time and determine how different clinical disciplines impact wait times in the studied hospital. Methods: This cross-sectional study investigated a study population comprised of outpatients who referred to clinics at a general hospital in Tehran, Iran. A total of 3836 samples were selected from different stations. Nonrandomized quota sampling was used, and data was gathered using workflow checklists, the content validity of which was proven by experts and hospital authorities. SPSS statistical software was used for data analysis. Results: Total patient stay in the outpatient setting was an estimated 77 minutes (without considering para-clinic units and pharmacy). More than 90% of this time was spent waiting. The wait time of patients at the clinic was greater than that at other stations, and it was less at the cash station than at other stations. Wait times varied at different clinics (P value < 0.001) and were correlated with physician delay (P value < 0.001). Conclusion: The most important result of using PFA in the outpatient setting was managing wait times. This study indicated that a considerable amount of a patient’s total stay in the system was related to waiting for physical examinations. This suggests that the first priority in improving the system should be managing patient wait times for physical examinations.

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“…The patient flow data is basically time series data. In [48], a time series is defined as a vector X = {x (1) , x (2)…”

Section: Related Workmentioning

confidence: 99%

“…Overcrowding of hospital waiting rooms by patients characterizes the outpatient departments (OPDs) of healthcare centers of developing countries [1,2]. It is true that most of the patients that visit health centers have varying health challenges and some may require prompt medical attention.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Patient Load Prediction and IoT Integrated Intelligent Patient Transfer Systems

et al. 2019

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A mismatch between staffing ratios and service demand leads to overcrowding of patients in waiting rooms of health centers. Overcrowding consequently leads to excessive patient waiting times, incomplete preventive service delivery and disgruntled medical staff. Worse, due to the limited patient load that a health center can handle, patients may leave the clinic before the medical examination is complete. It is true that as one health center may be struggling with an excessive patient load, another facility in the vicinity may have a low patient turn out. A centralized hospital management system, where hospitals are able to timely exchange patient load information would allow excess patient load from an overcrowded health center to be re-assigned in a timely way to the nearest health centers. In this paper, a machine learning-based patient load prediction model for forecasting future patient loads is proposed. Given current and historical patient load data as inputs, the model outputs future predicted patient loads. Furthermore, we propose re-assigning excess patient loads to nearby facilities that have minimal load as a way to control overcrowding and reduce the number of patients that leave health facilities without receiving medical care as a result of overcrowding. The re-assigning of patients will imply a need for transportation for the patient to move from one facility to another. To avoid putting a further strain on the already fragmented ambulatory services, we assume the existence of a scheduled bus system and propose an Internet of Things (IoT) integrated smart bus system. The developed IoT system can be tagged on buses and can be queried by patients through representation state transfer application program interfaces (APIs) to provide them with the position of the buses through web app or SMS relative to their origin and destination stop. The back end of the proposed system is based on message queue telemetry transport, which is lightweight, data efficient and scalable, unlike the traditionally used hypertext transfer protocol.

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Factors affecting the overcrowding in outpatient healthcare

Cited by 28 publications

References 27 publications

Time series model for forecasting the number of new admission inpatients

Time series model for forecasting the number of new admission inpatients

Patient Flow Analysis in General Hospitals: How Clinical Disciplines Affect Outpatient Wait Times

Machine Learning-Based Patient Load Prediction and IoT Integrated Intelligent Patient Transfer Systems

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