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Abstract
Background: The Department of Obstetrics and Gynecology (OB/GYN) at the University of Arkansas for Medical
Sciences (UAMS) tested various, new system-restructuring ideas such as varying number of different types of nurses to reduce patient wait times for its outpatient clinic, often with little or no effect on waiting time. Witnessing litt...
Contexts in source publication
Context 1
... 56 patients are sched- uled on Monday, Tuesday, Thursday, and Friday, and 24 patients on Wednesday. The appointment scheduling template currently used for a provider is shown in Table 2. The appointment times denote the appointment times with the providers and not for the ultrasound (US) proce- dure. ...
Context 2
... observed that the double appointments caused more peo- ple to wait in the waiting rooms resulting in increased WT which in turn resulted in increased TWT and TST. Hence, we decided to modify the existing appointment template in Table 2 by splitting all double (two in one time slot) appointments into two single appointments in dif- ferent time slots by creating two new time slots in the AM session and three slots in the PM session as shown in Table 7. This type of changes to the appointment tem- plate was found to be effective as pointed out by Ho and Lau [23]. ...
Citations
... Najmuddin et al. developed an optimized scheduling method combined with a discrete event simulation model to reduce further waiting time and increase flow in obstetric outpatients and improved the optimization effect of this method through multiple simulations run [7]. Lenin et al. optimized the appointment model of obstetrics and gynecology clinics and distinguished three different patient types according to the possibilities of each type of patient attending appointments [8]. The best solution resulted in adding a medical assistant and modifying the appointment system to eliminate bottlenecks without sacrificing resource utilization by reducing patient waiting times. ...
Background
The size and cost of outpatient capacity directly affect the operational efficiency of a whole hospital. Many scholars have faced the study of outpatient capacity planning from an operations management perspective.
Objective
The outpatient service is refined, and the quantity allocation problem of each type of outpatient service is modeled as an integer linear programming problem. Thus, doctors’ work efficiency can be improved, patients’ waiting time can be effectively reduced, and patients can be provided with more satisfactory medical services.
Methods
Outpatient service is divided into examination and diagnosis service according to lean thinking. CPLEX is used to solve the integer linear programming problem of outpatient service allocation, and the maximum working time is minimized by constraint solution.
Results
A variety of values are taken for the relevant parameters of the outpatient service, using CPLEX to obtain the minimum and maximum working time corresponding to each situation. Compared with no refinement stratification, the work efficiency of senior doctors has increased by an average of 25%. In comparison, the patient flow of associate senior doctors has increased by an average of 50%.
Conclusion
In this paper, the method of outpatient capacity planning improves the work efficiency of senior doctors and provides outpatient services for more patients in need; At the same time, it indirectly reduces the waiting time of patients receiving outpatient services from senior doctors. And the patient flow of the associate senior doctors is improved, which helps to improve doctors’ technical level and solve the problem of shortage of medical resources.
... 10 Various studies around the globe have conducted DES in outpatient or the emergency department. 5,[11][12][13][14][15][16][17][18] Simulation modeling has been shown to be a valid, decision support tool for informing service planning but few studies are done in healthcare settings. [19][20][21][22] Some of the DES studies report results 23 while few report the implementation strategy. ...
... Besides removing all peak wait times and bottlenecks around noon and late in the afternoon, the best scenario yielded 39.84% (p < 0.001), 30.31% (p < 0.001), and 15.12% (p < 0.001) improvement in patients' average wait times for providers in the exam rooms, average total wait time at various locations and average total spent time in the clinic, respectively. This is achieved without any compromise in the utilization of the staff and in serving all patients by 5 pm.14 The model provides a tool for the clinic management to test new ideas to improve the performance of other UAMS OB/GYN clinics. ...
Background and Aims
A study was done to create and run a discrete event simulation in the outpatient department (OPD) of a tertiary care cancer hospital in North India to project and optimize resource deployment.
Methods
The OPD process & workflow as per the expected load at tertiary care cancer hospital were finalized with various stakeholders in a focused group discussion. The finalized OPD process & workflow along with the OPD Building plans were utilized to develop a discrete event simulation model for the OPD at a tertiary care cancer hospital using a discrete event simulator. The simulation model thus developed was tested with incremental patient loads in 5 different scenarios/“What if” situations (Scenario 1–5). The data regarding initial patient load and resources deployed was taken from on‐ground observations at the tertiary care cancer hospital.
Results
It was found that rooms and doctors were over‐utilized and support staff utilization remained low. This was implemented with a lesser waiting time for patients. No additional support staff was provided thus improving utilization of existing staff and saving on resources. The simulations enabled us to deploy resources just when it was required, which ensured optimal utilization and better efficiency. The peak census helped us to determine the capacity of the waiting area in different scenarios with incremental patient load and resource deployment.
Conclusion
The simulation software was very helpful, as “what if scenarios” could be created and the system tested, without disturbing the normal functioning of OPD. This enabled decision‐making before making on‐ground changes which saved a lot of time and money. Also, the processes of the old system were reengineered to fit the needs of changing times.
... Patients with atypical consultation times occupy consultation rooms for an extended period, rendering doctors and consultation rooms (resources) to appear constrained in the system during this period. A total of 30 independent replications were run to allow the sampling distribution to have an approximate normal distribution based on the Central Limit Theorem [42]. The length of each replication was five working days at the clinic. ...
Long wait times and crowding are major issues affecting outpatient service delivery, but it is unclear how these affect patients in dual practice settings. This study aims to evaluate the effects of changing consultation start time and patient arrival on wait times and crowding in an outpatient clinic with a dual practice system. A discrete event simulation (DES) model was developed based on real-world data from an Obstetrics and Gynaecology (O&G) clinic in a public hospital. Data on patient flow, resource availability, and time taken for registration and clinic processes for public and private patients were sourced from stakeholder discussion and time-motion study (TMS), while arrival times were sourced from the hospital’s information system database. Probability distributions were used to fit these input data in the model. Scenario analyses involved configurations on consultation start time/staggered patient arrival. The median registration and clinic turnaround times (TT) were significantly different between public and private patients (p < 0.01). Public patients have longer wait times than private patients in this study’s dual practice setting. Scenario analyses showed that early consultation start time that matches patient arrival time and staggered arrival could reduce the overall TT for public and private patients by 40% and 21%, respectively. Similarly, the number of patients waiting at the clinic per hour could be reduced by 10–21% during clinic peak hours. Matching consultation start time with staggered patient arrival can potentially reduce wait times and crowding, especially for public patients, without incurring additional resource needs and help narrow the wait time gap between public and private patients. Healthcare managers and policymakers can consider simulation approaches for the monitoring and improvement of healthcare operational efficiency to meet rising healthcare demand and costs.
... In order to further reduce the waiting time of obstetric outpatients and increase the flow, Najmuddin et al. developed an optimization scheduling method combined with the discrete-event simulation model [7] and improved the optimization effect of this method through multiple simulation runs. Lenin et al. optimized the appointment model of obstetrics and gynecology clinics [8]. According to the different possibility of each type of patient to participate in the appointment, three different types of patients were distinguished. ...
Background:
In recent years, there are many studies on scheduling methods of patient flow, nurse scheduling, bed allocation, operating room scheduling and other problems, but there is no report on the research methods of how to plan ward allocation from a more macroscopic perspective.
Methods:
The obstetric wards are divided into observation ward, cesarean section ward and natural delivery ward according to lean thinking. CPLEX is used to solve the mixed integer programming problem of ward allocation. In R software, multivariate GLM regression model is used to analyze the influence of each factor on patient flow.
Results:
The maximum patient flow of each case was obtained by CPLEX, which was 19-25% higher than which of patients without refinement, stratification and planning. GLM regression analysis was carried out on the above data, and the positive and negative correlation factors were obtained.
Conclusion:
According to lean thinking, obstetric wards are divided into three types of wards. Obstetricians and midwives work more efficiently and get more rest time. Pregnant women also enjoy more detailed medical services. By modeling the delivery ward allocation problem as a mixed integer programming problem, we can improve the capacity of the service in obstetric hospitals from a macro perspective. Through GLM regression model analysis, it is conducive to improve the obstetric hospital capacity from the perspective of positive and negative correlation factors.
... For instance, previous researchers (Famiglietti et al., 2017;Mustafee et al., 2010) used DES in an outpatient clinic to evaluate and analyze the factors that impact the clinic performance in terms of patient waiting and resource utilization, while another study (Berg et al., 2013) used DES to estimate the effects of noshows on an outpatient clinic in terms of cost. In contrast, implementing DES improvement strategies within outpatient settings is often challenging because of the tremendous amount of data extracted under the conflict of interest between the stakeholders (Eldabi et al., 2007;Matta and Patterson, 2007;Robinson et al., 2012); as a result, improvements with DES tend to be focused on testing suggested new changes by the management in terms of layout, patient flow and resource flow (Demir et al., 2018;Deo et al., 2012;Harper and Gamlin, 2003;Kopach et al., 2007;Vahdat et al., 2018) or it could be focused on performing sensitivity analysis to test the impact of one or a small number of factors on the clinic performance such IJLSS as the arrival rate, the number of resources hired and sequencing policies (Day et al., 2014;LaGanga and Lawrence, 2007;Lenin et al., 2015;Pan et al., 2015;Rau et al., 2013). In addition, some researchers applied DES to improve the clinical performance based on the effects of appointment scheduling rules and sequencing rules or other soft systems methodology on the clinical performance in terms of patient waiting and resource waiting times (Cayirli et al., 2019(Cayirli et al., , 2006Cayirli and Gunes, 2014;Yang and Cayirli, 2020). ...
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.
... 10,[12][13][14][22][23][24][25][26][27][28] shows sample definitional deficits. ...
Objectives:
Poorly defined measurement impairs interinstitutional comparison, interpretation of results, and process improvement in health care operations. We sought to develop a unifying framework that could be used by administrators, practitioners, and investigators to help define and document operational performance measures that are comparable and reproducible.
Study design:
Retrospective analysis.
Methods:
Health care operations and clinical investigators used an iterative process consisting of (1) literature review, (2) expert assessment and collaborative design, and (3) end-user feedback. We sampled the literature from the medical, health systems research, and health care operations (business and engineering) disciplines to assemble a representative sample of studies in which outpatient health care performance metrics were used to describe the primary or secondary outcome of the research.
Results:
We identified 2 primary deficiencies in outpatient performance metric definitions: incompletion and inconsistency. From our review of performance metrics, we propose the FASStR framework for the Focus, Activity, Statistic, Scale type, and Reference dimensions of a performance metric. The FASStR framework is a method by which performance metrics can be developed and examined from a multidimensional perspective to evaluate their comprehensiveness and clarity. The framework was tested and revised in an iterative process with both practitioners and investigators.
Conclusions:
The FASStR framework can guide the design, development, and implementation of operational metrics in outpatient health care settings. Further, this framework can assist investigators in the evaluation of the metrics that they are using. Overall, the FASStR framework can result in clearer, more consistent use and evaluation of outpatient performance metrics.
... The DES model developed to improve patient waiting time and flow in an OB/GYN outpatient clinic [43], could be improved by running more runs per scenario as DES to be more representative due to sampling from multiple distributions, and the number of "what-if" scenarios to evaluate could be expanded. Lenin et al. demonstrated through DES the optimised appointment templates for certain OB/GYN clinics [44]. The authors differentiated between three different patient types, each of which had different likelihoods of attending their appointments. ...
... In addition to the issues caused by unpunctuality, the no show rate or DNA rate has a substantial effect on clinic performance [53]. Many of the studies introduced in the inpatient and outpatient sections include scheduling but the most relevant study to our context is the use of a DES model to optimise appointment scheduling in US OB/GYN clinic [44]. In this study the authors determined the optimal duration between appointments for set numbers of patients split into two sessions. ...
Background: The demand for a large Norwegian hospital’s post-term pregnancy outpatient clinic has increased substantially over the last 10 years due to changes in the hospital’s catchment area and to clinical guidelines. Planning the clinic is further complicated due to the high did not attend rates as a result of women giving birth. The aim of this study is to determine the maximum number of women specified clinic configurations, combination of specified clinic resources, can feasibly serve within clinic opening times.
Methods: A hybrid agent based discrete event simulation model of the clinic was used to evaluate alternative configurations to gain insight into clinic planning and to support decision making. Clinic configurations consisted of six factors: X0: Arrivals. X1: Arrival pattern. X2: Order of midwife and doctor consultations. X3: Number of midwives. X4: Number of doctors. X5: Number of cardiotocography (CTGs) machines. A full factorial experimental design of the six factors generated 608 configurations.
Results: Each configuration was evaluated using the following measures: Y1: Arrivals. Y2: Time last woman checks out. Y3: Women’s length of stay (LoS). Y4: Clinic overrun time. Y5: Midwife waiting time (WT). Y6: Doctor WT. Y7: CTG connection WT. Optimisation was used to maximise X0 with respect to the 32 combinations of X1-X5. Configuration 0a, the base case Y1 = 7 women and Y3 = 102.97 [0.21] mins. Changing the arrival pattern (X1) and the order of the midwife and doctor consultations (X2) configuration 0d, where X3, X4, X5 = 0a, Y1 = 8 woman and Y3 86.06 [0.10] mins.
Conclusions: The simulation model identified the availability of CTG machines as a bottleneck in the clinic, indicated by the WT for CTG connection effect on LoS. One additional CTG machine improved clinic performance to the same degree as an extra midwife and an extra doctor. The simulation model demonstrated significant reductions to LoS can be achieved without additional resources, by changing the clinic pathway and scheduling of appointments. A more general finding is that a simulation model can be used to identify bottlenecks, and efficient ways of restructuring an outpatient clinic.
... We design an appointment scheduling model capturing multitype patient channeling to different provider levels in an OBGYN clinic specialty. Based on the research on OBGYN specialty clinics by [33] and [35], we divide patients into three categories, and consequently, seven patient types. ...
... Each day in the planning horizon is divided into 16 fifteen-minutes slots. Service time duration for each patient type is based on the literature on OBGYN clinic ( [33], [35]). The authors in [35] collected data from the West Little Rock (WLR) clinic operated under the University of Arkansas for Medical Sciences (UAM). ...
... Service time duration for each patient type is based on the literature on OBGYN clinic ( [33], [35]). The authors in [35] collected data from the West Little Rock (WLR) clinic operated under the University of Arkansas for Medical Sciences (UAM). The parameters a r and ρ r denote the target for new patient category and penalty for the deviation from the target for a provider r. ...
Clinics with a large volume of patients are often burdened with limited resources such as nurses and providers. Also, an efficient health system seeks short wait times for the patients to see the provider (indirect wait time) and within the clinic (direct wait time) during the day of the appointment. Additionally, the appointment duration, volume of patients, no-show behavior are uncertain. The direct and indirect wait times, stochastic parameters, rising treatment costs, and increased demand of patients motivate the need for efficient appointment schedules and clinic operations. In this paper, we develop a two-stage stochastic mixed-integer linear programming model (SMILP) integrated with a simulation model to generate a scheduling template for the providers to schedule individual patient appointments and resources. The model minimizes the expected wait times for the patients with a fair and equitable utilization of the resources. Computational experiments were conducted using a data-driven simulation model, and the results indicate that the proposed approach can significantly decrease patients' direct and indirect wait times when compared to a deterministic indexing policy used for scheduling appointments.
... The DES model developed to improve patient waiting time and ow in an OB/GYN outpatient clinic [43], could be improved by running more runs per scenario as DES to be more representative due to sampling from multiple distributions, and the number of "what-if" scenarios to evaluate could be expanded. Lenin et al demonstrated through DES the optimised appointment templates for certain OB/GYN clinics [44]. The authors differentiated between three different patient types, each of which had different likelihoods of attending their appointments. ...
... In addition to the issues caused by unpunctuality, the no show rate or DNA rate has a substantial effect on clinic performance [53]. Many of the studies introduced in the inpatient and outpatient sections include scheduling but the most relevant study to our context is the use of a DES model to optimise appointment scheduling in US OB/GYN clinic [44]. In this study the authors determined the optimal duration between appointments for set numbers of patients split into two sessions. ...
Background The demand for a large Norwegian hospital’s post-term pregnancy outpatient clinic has increased substantially over the last 10 years due to changes in the hospital’s catchment area and to clinical guidelines. Planning the clinic is further complicated due to the high did not attend rates as a result of women giving birth. The aim of this study was to develop a tool that supports clinic management to better understand and improve capacity and patient flow planning.
Methods A hybrid agent based discrete event simulation model of the clinic was used to evaluate alternative configurations to gain insight into clinic planning and to support decision making. Clinic configurations consisted of six factors: X0: Arrivals. X1: Arrival pattern. X2: Order of midwife and doctor consultations. X3: Number of midwives. X4: Number of doctors. X5: Number of cardiotocography (CTGs) machines. A full factorial experimental design of the six factors generated 608 configurations.
Results Each configuration was evaluated using the following measures: Y1: Arrivals. Y2: Time last woman checks out. Y3: Women’s length of stay (LoS). Y4: Clinic overrun time. Y5: Midwife waiting time (WT). Y6: Doctor WT. Y7: CTG connection WT. Optimisation was used to maximise X0 with respect to the 32 combinations of X1-X5. Configuration 0a, the base case Y1 = 7 women and Y3 = 102.97 [0.21] mins. Changing the arrival pattern (X1) and the order of the midwife and doctor consultations (X2) configuration 0d, where X3, X4, X5 = 0a, Y1 = 8 woman and Y3 86.06 [0.10] mins.
Conclusions From the clinic’s perspective, the changes in catchment area and clinical guidelines led to increased demand. The simulation model demonstrated flexible pathways in the order of midwife/doctor and appointment scheduling increases flow substantially, reducing LoS. Equipment appeared more of a bottleneck than personnel, as one additional CTG machine has the same effect as an extra midwife and an extra doctor, and the WT for CTG connection is a key contributor to LoS. A more general finding is that a simulation model can be used to identify bottlenecks, and efficient ways of restructuring an outpatient clinic.
