scholarly journals Predicting Patient Waiting Time in the Queue System Using Deep Learning Algorithms in the Emergency Room

2021 ◽  
Vol 03 (01) ◽  
pp. 33-45
Author(s):  
Hassan Hijry ◽  
Richard Olawoyin
Keyword(s):  
Deep Learning ◽  
Emergency Room ◽  
Waiting Time ◽  
Waiting Times ◽  
Real Life ◽  
Healthcare Sector ◽  
Medical Attention ◽  
Queue System ◽  
Patient Waiting Time ◽  
Patient Waiting

Many hospitals consider the length of time waiting in queue to be a measure of emergency room (ER) overcrowding. Long waiting times plague many ER departments, hindering the ability to effectively provide medical attention to those in need and increasing overall costs. Advanced techniques such as machine learning and deep learning (DL) have played a central role in queuing system applications. This study aims to apply DL algorithms for historical queueing variables to predict patient waiting time in a system alongside, or in place of, queueing theory (QT). We applied four optimization algorithms, including SGD, Adam, RMSprop, and AdaGrad. The algorithms were compared to find the best model with the lowest mean absolute error (MAE). A traditional mathematical simulation was used for additional comparisons. The results showed that the DL model is applicable using the SGD algorithm by activating a lowest MAE of 10.80 minutes (24% error reduction) to predict patients' waiting times. This work presents a theoretical contribution of predicting patients’ waiting time with alternative techniques by achieving the highest performing model to better prioritize patients waiting in the queue. Also, this study offers a practical contribution by using real-life data from ERs. Furthermore, we proposed models to predict patients' waiting time with more accurate results than a traditional mathematical method. Our approach can be easily implemented for the queue system in the healthcare sector using electronic health records (EHR) data.

Announcing Waiting Time in Emergency Departments

2019 ◽  
pp. 1-6
Author(s):  
Martin Lariviere ◽  
Sarang Deo
Keyword(s):  
Waiting Time ◽  
Emergency Departments ◽  
Waiting Times ◽  
Large Network ◽  
Regional Networks ◽  
Marketing Campaign ◽  
National Healthcare ◽  
Time Estimates ◽  
Patient Waiting Time ◽  
Patient Waiting

First National Healthcare (FNH) runs a large network of hospitals and has worked to systematically reduce waiting times in its emergency departments. One of FNH's regional networks has run a successful marketing campaign promoting its low ED waiting times that other regions want to emulate. The corporate quality manager must now determine whether to allow these campaigns to be rolled out and, if so, which waiting time estimates to use. Are the numbers currently being reported accurate? Is there a more accurate way of estimating patient waiting time that can be easily understood by consumers?

scholarly journals An Edge Computing Based Smart Healthcare Framework for Resource Management

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4307 ◽  
Author(s):  
Soraia Oueida ◽  
Yehia Kotb ◽  
Moayad Aloqaily ◽  
Yaser Jararweh ◽  
Thar Baker
Keyword(s):  
Resource Utilization ◽  
Waiting Time ◽  
Smart Cities ◽  
Real Life ◽  
Edge Computing ◽  
Utilization Rate ◽  
Resource Assignment ◽  
Smart Healthcare ◽  
Patient Waiting Time ◽  
Patient Waiting

The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time.

scholarly journals Factors Associated With Patient Waiting Time at a Medical Outpatient Clinic: A Case Study of University of Nairobi Health Services

2021 ◽  
Vol 6 (12) ◽  
pp. 915-918
Author(s):  
Rebecca Bisanju Wafula (BSCN, MSCHSM) ◽  
Dr. Richard Ayah (MBCHB, MSC, PHD)
Keyword(s):  
Health Services ◽  
Health Care Providers ◽  
Waiting Time ◽  
Outpatient Clinic ◽  
Health Workers ◽  
Waiting Times ◽  
Care Providers ◽  
Cross Sectional ◽  
Patient Waiting Time ◽  
Patient Waiting

Background: Long waiting time in outpatient clinics is a constant challenge for patients and the health care providers. Prolonged waiting times are associated with poor adherence to treatment, missed appointment and failure or delay in initiation of treatment and is a major factor towards the perception of the patient towards the care received. Objective: To determine the waiting time and associated factors among out patients attending staff clinic at University of Nairobi health services. Method: A cross-sectional study design was used and data collected from 384 ambulatory patients over a period of four weeks using an interviewer administered pretested structured exit questionnaire with a time-tracking section. Simple random sampling was used to select respondents in a walk- in outpatient clinic set up. Data was cleaned and analysed using Statistical Package for Social Sciences (SPSS) 20. Analysis of variance (ANOVA), and cross tabulation was used to establish associations between the independent variable and dependent variables. Results: In total 384 patients were tracked and interviewed. The average patient waiting time was 55.3mins.Most respondents (52%) suggested that improving availability of staff at their stations would help to reduce patient waiting time. In this study, gender (P=0.005) and availability of doctors (p=0.000) were found to affect patient waiting time with women waiting longer than the male patients. Conclusion: Majority of the patients spent about an hour at the facility to be served. Inadequate number of health workers was the main cause of long waiting time.

Predicting Patient Waiting Time in Phlebotomy Units Using a Deep Learning Method

2019 ◽  
Author(s):  
Hamed Javadifard ◽  
Suleyman Sevinc ◽  
Oktay Yildirim ◽  
Dilek Orbatu ◽  
Eminullah Yasar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Waiting Time ◽  
Learning Method ◽  
Patient Waiting Time ◽  
Patient Waiting

scholarly journals Integral patient scheduling in outpatient clinics under demand uncertainty to minimize patient waiting times

2019 ◽  
Vol 26 (1) ◽  
pp. 435-448 ◽  
Author(s):  
Jyoti R Munavalli ◽  
Shyam Vasudeva Rao ◽  
Aravind Srinivasan ◽  
GG van Merode
Keyword(s):  
Real Time ◽  
Waiting Time ◽  
Outpatient Clinic ◽  
Demand Uncertainty ◽  
Waiting Times ◽  
Optimal Path ◽  
Outpatient Clinics ◽  
Patient Scheduling ◽  
Patient Waiting Time ◽  
Patient Waiting

This study addressed the problem of scheduling walk-in patients in real time. Outpatient clinics encounter uncertainty in patient demand. In addition, the disparate departments are locally (department-centric) organized, leading to prolonged waiting times for patients. The proposed integral patient scheduling model incorporates the status and information of all departments in the outpatient clinic along with all possible pathways to direct patients, on their arrival, to the optimal path. The developed hybrid ant agent algorithm identifies the optimal path to reduce the patient waiting time and cycle time (time from registration to exit). An outpatient clinic in Aravind Eye Hospital, Madurai, has a huge volume of walk-in patients and was selected for this study. The simulation study was performed for diverse scenarios followed by implementation study. The results indicate that integral patient scheduling reduced waiting time significantly. The path optimization in real time makes scheduling effective and efficient as it captures the changes in the outpatient clinic instantly.

A combined digital and organizational approach dramatically improves access to outpatient care in cardiology

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
F Jamal ◽  
F Boiron ◽  
C Tettoni ◽  
I Couvert ◽  
C Ielissof ◽  
...  
Keyword(s):  
Emergency Room ◽  
Access To Care ◽  
Waiting Time ◽  
Waiting Times ◽  
Real Life ◽  
Digital Tools ◽  
Risk Level ◽  
Funding Source ◽  
Private Company ◽  
The Impact

Abstract   We have previously shown that digital preconsultation saves medical time and may improve outcome. But waiting times for a cardiology consultation are still growing mainly due to chronic diseases, aging and a growing demand for prevention. Purpose We aimed to evaluate the impact of a combined approach using digital tools and a novel outpatient team organization on access to care, in real life Methods We implemented a full digital solution and a dedicated team in a new outpatient cardiology center (Figure). The organization was schematically divided in five parts: (1) digital PRECONSULTATION completed by the patient with an assistant and nurse support if necessary; (2) digital AI-NALYSIS of this data with a trained nurse validation to define the risk level; (3) Medical CONSULTATION either physical or using teleconsultation; (4) RESULTS: directly accessible to the patient and his GP; (5) FOLLOW-UP if relevant mainly based on digital tools and trained nurses. Activity was monitored for 9 months. The following parameters were measured: waiting time (from demand to consultation); medical efficiency (number of consultations/hour of work); patient overall satisfaction Results 2867 consultations were performed between April and December 2019, with a waiting time of 4.3±1.6 days (compared to a national average of 61 days). Efficiency averaged 2.3 patients/hour. Patient satisfaction averaged 4.84 over 5. In addition, 160 possible consultations in the emergency room were avoided (mainly mild palpitations and non-cardiac chest pain) Conclusion A specific digital platform and a dedicated medical team and organization improved the access to care and the medical efficiency. We believe this is a promising way to decrease the demand burden in the emergency room, to decrease the professional burnout risk and to improve prevention. Figure 1. A Phygital care path Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Cardioparc, Izycardio

Using lean techniques and discrete-event simulation for performance improvement in an outpatient clinic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kudret Demirli ◽  
Abdulqader Al Kaf ◽  
Mecit Can Emre Simsekler ◽  
Raja Jayaraman ◽  
Mumtaz Jamshed Khan ◽  
...  
Keyword(s):  
Health Care ◽  
Discrete Event Simulation ◽  
Waiting Time ◽  
Outpatient Clinic ◽  
Waiting Times ◽  
Discrete Event ◽  
Content Type ◽  
Event Simulation ◽  
Patient Waiting Time ◽  
Patient Waiting

Purpose Increased demand and the pressure to reduce health-care costs have led to longer waiting time for patients to make appointments and during the day of hospital visits. The purpose of this study is to identify opportunities to reduce waiting time using lean techniques and discrete-event simulation (DES). Design/methodology/approach A five-step procedure is proposed to facilitate the effective utilization of lean and DES to improve the performance of the Otolaryngology Head and Neck Surgery Outpatient Clinic at Cleveland Clinic Abu Dhabi. While lean techniques were applied to reduce the potential sources of waste by aligning processes, a DES model was developed to validate the proposed solutions and plan patient arrivals under dynamic conditions and different scenarios. Findings Aligning processes resulted in an efficient patient flow reducing both waiting times. DES played a complementary role in verifying lean solutions under dynamic conditions, helping to plan the patient arrivals and striking a balance between the waiting times. The proposed solutions offered flexibility to improve the clinic capacity from the current 176 patients up to 479 (without violating the 30 min waiting time policy) or to reduce the patient waiting time during the visit from the current 33 min to 4.5 min (without violating the capacity goal of 333 patients). Research limitations/implications Proposing and validating lean solutions require reliable data to be collected from the clinic and such a process could be laborious as data collection require patient and resource tracing without interfering with the regular functions of the clinic. Practical implications The work enables health-care managers to conveniently conduct a trade-off analysis and choose a suitable inter-arrival time – for every physician – that would satisfy their objectives between resource utilization (clinic capacity) and average patient waiting time. Social implications Successful implementation of lean requires a supportive and cooperative culture from all stakeholders involved. Originality/value This study presents an original and detailed application of lean techniques with DES to reduce patient waiting times. The adopted approach in this study could be generalized to other health-care settings with similar objectives.

Study of Patient Waiting Time in the Emergence Department: An Example of a Medical Center in New Taipei County, Taiwan

2014 ◽  
Vol 519-520 ◽  
pp. 1581-1584
Author(s):  
Chen Shie Ho ◽  
Min Li Yeh ◽  
Yu Sheng Liao
Keyword(s):  
Emergency Department ◽  
Waiting Time ◽  
Queuing Theory ◽  
Flow Model ◽  
Medical Center ◽  
Waiting Times ◽  
Literature Survey ◽  
Theory Analysis ◽  
Patient Waiting Time ◽  
Patient Waiting

Patients who receive care in an emergency department (ED) are usually unattended while waiting in queues. This study attempted to determine whether the application of queuing theory analysis might shorten the waiting times of patients admitted to emergency wards. After the literature survey phase, the flow model to evaluate the patient waiting time in the emergence department corresponding to the target hospital is presented, then the waiting time under some circumstance are simulated. By allocating the human and space resource dynamically, the waiting time can be reduced then patient satisfaction is improved.

scholarly journals The Use of Information Technology to Short the Waiting Time for Examination of Road Care Patients at RSU Islam Klaten

2021 ◽  
Vol 04 (01) ◽  
Author(s):  
Owin Bambang Wijanarko ◽  
Keyword(s):  
Information Technology ◽  
Patient Satisfaction ◽  
Health Services ◽  
Waiting Time ◽  
Waiting Times ◽  
Value Added ◽  
Outpatient Services ◽  
Cross Sectional ◽  
Patient Waiting Time ◽  
Patient Waiting

Background: Outpatient services are a reflection of hospital services.As a form of health service facility that organizes health efforts, hospitals often experience difficulties in managing information for both internal and external needs. One form of application is through service systems by utilizing information technology through the use of computer-based on information systems.The Lean Hospital concept, which has been successfully implemented in several hospitals, is expected to eliminate waste and add value added activity which will ultimately increase patient satisfaction. Purpose: The purpose of this study was to calculate patient waiting time with the application of information technology in the outpatient polyclinic of RSU Islam Klaten. Research methods: This type of research uses a descriptive analytic method. This research meth-od used a cross sectional approach. The sample in this study amounted to 81 respondents with the sampling technique using purposive sampling. The analysis in research using the t-test. The re-search instruments used included literature studies, interviews and direct observation of medical record officers, nurses of polyclinic nurses, registration departments, and patients at RSU Islam Klaten. Result: There is a significant relationship between waiting time and patient satisfaction p = 0.001. Patients with long waiting times were more dissatisfied (60.0%), while fast waiting times were more very satisfied (73.9%). Conclusion: The success of health services is seen from the patient's waiting time and patient satisfaction. Waiting time is the time used by patients to get health services from the registration point to getting in the doctor's examination room. Overall information technology shortens patient waiting time in parts of registration-polyclinic and Pharmacy.

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