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.

scholarly journals COMPARING SCHEDULING APPOINTMENT RULES PERFORMANCE IN HEALTH CARE UNITS: A DISCRETE EVENT SIMULATION APPROACH

2021 ◽  
Vol 16 (1) ◽  
pp. 28-41
Author(s):  
Thiago Nunes Klojda ◽  
Antônio Pedro de Britto Pereira Fortuna ◽  
Bianca Menezes Araujo ◽  
Daniel Bouzon Nagem Assad ◽  
Thaís Spiegel
Keyword(s):  
Health Care ◽  
Discrete Event Simulation ◽  
Health Care Systems ◽  
Waiting Times ◽  
Discrete Event ◽  
Idle Time ◽  
Event Simulation ◽  
Care Systems ◽  
Demand Scenario ◽  
Patient Waiting

Health care systems are affected by sudden increases in demand that can be generated by factors such as natural disasters, terrorist attacks, epidemics, among others. Patient demand can be divided between scheduled and walk-in and, in pandemic scenarios, both of them must be managed in order to avoid higher patient waiting times or number in queue. A discrete event simulation model is proposed in order to evaluate critical indicators like: patient waiting times, number in queue, resource utilization (doctors), using four different patient schedule appointment rules. In this study it was also considered patients impunctuality, walk-in patients and no-show in different scenarios. The best schedule appointment rules for each demand scenario were evaluated. After comparing six performance indicators, four schedule appointment rules in nine different scenarios it was found that the most known scheduling rule had the lowest queue sizes at scenarios with low or no walk-in patients, whereas, as the unpredictability of the scenarios rose, other rules outperformed it. It was also presented to exist an inverse relation between queue size and the physician idle time. Keywords: discrete event simulation, idle-time, queue management, appointment scheduling, health care.

Improving patient waiting time of centralized front office service in a regional hub hospital using the discrete event simulation model

2020 ◽  
Vol 28 (5) ◽  
pp. 487-494
Author(s):  
D. Cocchi ◽  
E. Ciagli ◽  
A. Ancora ◽  
P. Tortoli ◽  
C. Carpini ◽  
...  
Keyword(s):  
Simulation Model ◽  
Discrete Event Simulation ◽  
Waiting Time ◽  
Waiting Times ◽  
Discrete Event ◽  
University Hospital ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
Front Office ◽  
Patient Waiting

BACKGROUND: Today, hospital rankings are based not only on basic clinical indicators, but even on quality service indicators such as patient waiting times. Improving these indicators is a very important issue for hospital management, so finding a solution to achieve it in a simple and effective way is one of the greatest goals. OBJECTIVES: The aim of this article is to evaluate the use of a discrete event simulation model to improve healthcare processes and reduce waiting time of patients and hospital costs. METHODS: The case study proposed in this paper is the reorganization of non-clinical front office operation for the patients (i.e. booking of exams, delivering medical reports, etc.) of the Careggi University Hospital of Florence, to optimize the utilization of the human resources and to improve performances of the process. RESULTS: The development and validation of the model was made according to an analysis of real processes and data, pre and post implementation of model outcomes. The new organization shows a decrease of waiting times from an average value of 10 minutes and 37 seconds to 5 minutes and 57 seconds (-44%). CONCLUSIONS: This paper shows that discrete event simulation could be a precise, cost-limited tool to optimize hospital processes and performance.

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.

Reducing Consultation Waiting Time and Overtime in Outpatient Clinic

2011 ◽  
pp. 229-245
Author(s):  
Zhu Zhecheng ◽  
Heng Bee Hoon ◽  
Teow Kiok Liang
Keyword(s):  
Patient Safety ◽  
Design Of Experiments ◽  
Discrete Event Simulation ◽  
Waiting Time ◽  
Outpatient Clinic ◽  
Performance Indicators ◽  
Discrete Event ◽  
Outpatient Clinics ◽  
Event Simulation ◽  
Growing Population

Outpatient clinics face increasing pressure to handle more appointment requests due to aging and growing population. The increase in workload impacts two critical performance indicators: consultation waiting time and clinic overtime. Consultation waiting time is the physical waiting time a patient spends in the waiting area of the clinic, and clinic overtime is the amount of time the clinic is open beyond its normal opening hours. Long consultation waiting time negatively affects patient safety and satisfaction, while long clinic overtime negatively affects the morale of clinic staff. This chapter analyzes the complexity of an outpatient clinic in a Singapore public hospital, and factors causing long consultation waiting time and clinic overtime. Discrete event simulation and design of experiments are applied to quantify the effects of the factors on consultation waiting time/clinic overtime. Implementation results show significant improvement once those factors are well addressed.

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.

scholarly journals Improving workflow control in radiotherapy using discrete-event simulation

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Bruno Vieira ◽  
Derya Demirtas ◽  
Jeroen B. van de Kamer ◽  
Erwin W. Hans ◽  
Wim van Harten
Keyword(s):  
Discrete Event Simulation ◽  
Waiting Time ◽  
Waiting Times ◽  
Discrete Event ◽  
Hybrid Strategy ◽  
Event Simulation ◽  
Baseline Case ◽  
Pull Strategy ◽  
Pre Treatment ◽  
The Impact

Abstract Background In radiotherapy, minimizing the time between referral and start of treatment (waiting time) is important to possibly mitigate tumor growth and avoid psychological distress in cancer patients. Radiotherapy pre-treatment workflow is driven by the scheduling of the first irradiation session, which is usually set right after consultation (pull strategy) or can alternatively be set after the pre-treatment workflow has been completed (push strategy). The objective of this study is to assess the impact of using pull and push strategies and explore alternative interventions for improving timeliness in radiotherapy. Methods Discrete-event simulation is used to model the patient flow of a large radiotherapy department of a Dutch hospital. A staff survey, interviews with managers, and historical data from 2017 are used to generate model inputs, in which fluctuations in patient inflow and resource availability are considered. Results A hybrid (40% pull / 60% push) strategy representing the current practice (baseline case) leads to 12% lower average waiting times and 48% fewer first appointment rebooks when compared to a full pull strategy, which in turn leads to 41% fewer patients breaching the waiting time targets. An additional scenario analysis performed on the baseline case showed that spreading consultation slots evenly throughout the week can provide a 21% reduction in waiting times. Conclusions A 100% pull strategy allows for more patients starting treatment within the waiting time targets than a hybrid strategy, in spite of slightly longer waiting times and more first appointment rebooks. Our algorithm can be used by radiotherapy policy makers to identify the optimal balance between push and pull strategies to ensure timely treatments while providing patient-centered care adapted to their specific conditions.

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?

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