Use of Predictive and Simulation Models to Develop Strategies for Better Access Specialists Care

2020 ◽  
pp. 109-136
Author(s):  
Siang Li Chua ◽  
Wai Leng Chow
Keyword(s):  
Lead Time ◽  
Discrete Event ◽  
Simulation Models ◽  
Multiple Logistic Regression ◽  
Specialist Care ◽  
Event Simulation ◽  
Patients At Risk ◽  
The Impact ◽  
Specialist Outpatient Clinic ◽  
Des Model

No-shows are patients who miss scheduled Specialist Outpatient Clinic (SOC) appointments. No-shows can impact patients' access to care and appointment lead time. This chapter describes a data-driven strategy of improving access to specialist care through first developing a stratified predictive scoring model to identify patients at risk of no-shows; second, studying the impact of a dynamic overbooking strategy that incorporates the use of the no-show prediction model using discrete event simulation (DES) on lead time. Seventeen variables related to new SOC appointments for subsidized patients in 2016 were analyzed. Multiple logistic regression (MLR) found eight variables independently associated with no-shows with area under receiver operation curve (AUC) 70%. The model was tested and validated. DES model simulated the appointment overbooking strategy as applied to the top highest volume specialties and concluded that lead time of Specialty 1 and 2 can be shortened by 27.5 days (49% improvement) and 21.3 (33%) respectively.

scholarly journals Assessing asset monitoring levels for maintenance operations

2015 ◽  
Vol 26 (5) ◽  
pp. 632-659 ◽  
Author(s):  
Abdullah A Alabdulkarim ◽  
Peter Ball ◽  
Ashutosh Tiwari
Keyword(s):  
Asset Management ◽  
Business Models ◽  
Discrete Event ◽  
Dynamic Modelling ◽  
Simulation Models ◽  
Content Type ◽  
Event Simulation ◽  
Product Service ◽  
Asset Monitoring ◽  
The Impact

Purpose – Asset management has recently gained significance due to emerging business models such as Product Service Systems where the sale of asset use, rather than the sale of the asset itself, is applied. This leaves the responsibility of the maintenance tasks to fall on the shoulders of the manufacturer/supplier to provide high asset availability. The use of asset monitoring assists in providing high availability but the level of monitoring and maintenance needs to be assessed for cost effectiveness. There is a lack of available tools and understanding of their value in assessing monitoring levels. The paper aims to discuss these issues. Design/methodology/approach – This research aims to develop a dynamic modelling approach using Discrete Event Simulation (DES) to assess such maintenance systems in order to provide a better understanding of the behaviour of complex maintenance operations. Interviews were conducted and literature was analysed to gather modelling requirements. Generic models were created, followed by simulation models, to examine how maintenance operation systems behave regarding different levels of asset monitoring. Findings – This research indicates that DES discerns varying levels of complexity of maintenance operations but that more sophisticated asset monitoring levels will not necessarily result in a higher asset performance. The paper shows that it is possible to assess the impact of monitoring levels as well as make other changes to system operation that may be more or less effective. Practical implications – The proposed tool supports the maintenance operations decision makers to select the appropriate asset monitoring level that suits their operational needs. Originality/value – A novel DES approach was developed to assess asset monitoring levels for maintenance operations. In applying this quantitative approach, it was demonstrated that higher asset monitoring levels do not necessarily result in higher asset availability. The work provides a means of evaluating the constraints in the system that an asset is part of rather than focusing on the asset in isolation.

A Methodology to Evaluate Complex Manufacturing Systems through Discrete-Event Simulation Models

2012 ◽  
Vol 502 ◽  
pp. 7-12 ◽  
Author(s):  
L.P. Ferreira ◽  
E. Ares ◽  
G. Peláez ◽  
M. Marcos ◽  
M. Araújo
Keyword(s):  
Discrete Event Simulation ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Simulation Models ◽  
Network Configuration ◽  
Event Simulation ◽  
Automobile Assembly ◽  
Loop Network ◽  
Complex Manufacturing Systems ◽  
The Impact

This paper proposes a methodology to analyze complex manufacturing systems, based on discrete-event simulation models. The methodology was validated by performing different simulation experiments and will be applied to a multistage multiproduct production line, based on a real case, with a closed-loop network configuration of machines and intermediate buffers consisting of conveyors, which is very common in the automobile sector. A simulation model in an Arena environment was developed, which allowed for an analysis of the important aspects not yet studied in specialized literature, namely the assessment of the impact of the production sequence on the automobile assembly line. Various sequence rules were analyzed and the performance of each of the corresponding simulation models was registered.

An early discharge approach for managing hospital capacity

2017 ◽  
Vol 08 (01) ◽  
pp. 1750006 ◽  
Author(s):  
Israa Mohamed ◽  
Ibrahim El-Henawy ◽  
Ramadan Zean El-Din
Keyword(s):  
Discrete Event ◽  
Real Data ◽  
Early Discharge ◽  
University Hospital ◽  
Limited Capacity ◽  
Hospital Capacity ◽  
Empirical Distributions ◽  
Event Simulation ◽  
The Impact ◽  
Des Model

The Kidney and Oncology Departments at Zagazig University Hospital are suffering from increased demand and limited capacity. Arrival patients who find all beds occupied are simply turned away, i.e., no waiting is allowed. This paper investigates the impact of an early discharge approach that can be applied to patients that have been scheduled to discharge within 5 h. A discrete event simulation (DES) model is built using empirical distributions based on real data. The model has been validated against real data and the results have shown that the proposed early discharge approach can reduce the number of turned away patients by 10% in the Kidney Department, equivalent to 182 patients annually and by 11% in the Oncology Department, equivalent to 150 patients annually.

scholarly journals Analysis of the effects of EHR implementation on timeliness of care in a dermatology clinic: a simulation study

2018 ◽  
Vol 25 (7) ◽  
pp. 827-832 ◽  
Author(s):  
Vahab Vahdat ◽  
Jacqueline A Griffin ◽  
James E Stahl ◽  
F Clarissa Yang
Keyword(s):  
Length Of Stay ◽  
Patient Care ◽  
Discrete Event ◽  
Wait Time ◽  
Simulation Models ◽  
Event Simulation ◽  
Dermatology Clinic ◽  
Average Patient ◽  
Encounter Time ◽  
The Impact

Abstract Objective Quantify the downstream impact on patient wait times and overall length of stay due to small increases in encounter times caused by the implementation of a new electronic health record (EHR) system. Methods A discrete-event simulation model was created to examine the effects of increasing the provider-patient encounter time by 1, 2, 5, or 10 min, due to an increase in in-room documentation as part of an EHR implementation. Simulation parameters were constructed from an analysis of 52 000 visits from a scheduling database and direct observation of 93 randomly selected patients to collect all the steps involved in an outpatient dermatology patient care visit. Results Analysis of the simulation results demonstrates that for a clinic session with an average booking appointment length of 15 min, the addition of 1, 2, 5, and 10 min for in-room physician documentation with an EHR system would result in a 5.2 (22%), 9.8 (41%), 31.8 (136%), and 87.2 (373%) minute increase in average patient wait time, and a 6.2 (12%), 11.7 (23%), 36.7 (73%), and 96.9 (193%) minute increase in length of stay, respectively. To offset the additional 1, 2, 5, or 10 min, patient volume would need to decrease by 10%, 20%, 40%, and >50%, respectively. Conclusions Small changes to processes, such as the addition of a few minutes of extra documentation time in the exam room, can cause significant delays in the timeliness of patient care. Simulation models can assist in quantifying the downstream effects and help analyze the impact of these operational changes.

scholarly journals Supporting scale-up of COVID-19 RT-PCR testing processes with discrete event simulation

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255214
Author(s):  
Jad El Hage ◽  
Patti Gravitt ◽  
Jacques Ravel ◽  
Nadia Lahrichi ◽  
Erica Gralla
Keyword(s):  
Discrete Event Simulation ◽  
Discrete Event ◽  
Scale Up ◽  
Simulation Models ◽  
The United States ◽  
Molecular Testing ◽  
Rt Pcr ◽  
Event Simulation ◽  
Trade Offs ◽  
The Impact

Testing is critical to mitigating the COVID-19 pandemic, but testing capacity has fallen short of the need in the United States and elsewhere, and long wait times have impeded rapid isolation of cases. Operational challenges such as supply problems and personnel shortages have led to these bottlenecks and inhibited the scale-up of testing to needed levels. This paper uses operational simulations to facilitate rapid scale-up of testing capacity during this public health emergency. Specifically, discrete event simulation models were developed to represent the RT-PCR testing process in a large University of Maryland testing center, which retrofitted high-throughput molecular testing capacity to meet pandemic demands in a partnership with the State of Maryland. The simulation models support analyses that identify process steps which create bottlenecks, and evaluate “what-if” scenarios for process changes that could expand testing capacity. This enables virtual experimentation to understand the trade-offs associated with different interventions that increase testing capacity, allowing the identification of solutions that have high leverage at a feasible and acceptable cost. For example, using a virucidal collection medium which enables safe discarding of swabs at the point of collection removed a time-consuming “deswabbing” step (a primary bottleneck in this laboratory) and nearly doubled the testing capacity. The models are also used to estimate the impact of demand variability on laboratory performance and the minimum equipment and personnel required to meet various target capacities, assisting in scale-up for any laboratories following the same process steps. In sum, the results demonstrate that by using simulation modeling of the operations of SARS-CoV-2 RT-PCR testing, preparedness planners are able to identify high-leverage process changes to increase testing capacity.

Performance Optimization of Semiconductor Manufacturing Equipment by the Application of Discrete Event Simulation

2008 ◽  
Author(s):  
Markus Pfeffer ◽  
Richard Oechsner ◽  
Lothar Pfitzner ◽  
Heiner Ryssel ◽  
Berthold Ocker ◽  
...  
Keyword(s):  
Discrete Event Simulation ◽  
Cycle Time ◽  
Performance Optimization ◽  
Semiconductor Manufacturing ◽  
Discrete Event ◽  
Simulation Models ◽  
Manufacturing Equipment ◽  
Event Simulation ◽  
Different Types ◽  
The Impact

Semiconductor wafer fabrication facilities (wafer fabs) are amongst the most complex production facilities. State-of-the-art wafer fabs comprise a large product variety, hundreds of processing steps per product, almost hundreds of machines of different types, and automated transportation systems combined with reentrant flows throughout the fab. In addition to the high complexity, wafer fabs require very high capital investment and an undisturbed operation. Semiconductor manufacturers are facing fierce competition as more global capacity is being added. Through this intense competition, semiconductor manufacturers have to improve their processes from a technological as well as from a logistical point of view in order to be successful within the global market. The logistics not only involves fab wide optimization strategies but also the individual equipment performance, for example cycle time and throughput, has to be considered. In this paper, the need for performance optimization of semiconductor manufacturing equipment is identified and the capability of discrete event simulation for such optimizations is being elaborated. Characteristics of different types of simulation models are described and the simulation model selection is explained. For case studies, several simulation models of different semiconductor manufacturing equipment have been developed. Using five examples, different optimization strategies, dependent on the application of the semiconductor manufacturing equipment, have been investigated by discrete event simulation. The paper shows the influence of the integration of metrology into deposition equipment, the impact of different batch sizes for oxidation processes, and the optimized dimensioning of photolithography equipment. Furthermore, the throughput and cycle time of different deposition equipment are optimized by the evaluation of various improvement strategies. All investigations have been performed with real data extracted from already utilized equipment or at least with data from the equipment suppliers of prototype equipment.

Cardiac Arrhythmia Simulations for Comparing Remote Telemetry Monitoring Systems

2018 ◽  
Vol 7 (1) ◽  
pp. 129-131
Author(s):  
Noa Segall ◽  
Ron’Nisha Franklin ◽  
Melanie C. Wright
Keyword(s):  
Response Times ◽  
Direct Method ◽  
Discrete Event ◽  
Heart Rhythm ◽  
Simulation Models ◽  
Monitoring Systems ◽  
Cardiac Events ◽  
Efficient System ◽  
Event Simulation ◽  
The Impact

To increase the potential for timely detection of cardiac events, hospitalized patients who are at risk for critical arrhythmias are put on telemetry to continuously monitor their heart rhythm. However, telemetry monitoring systems vary widely between hospitals, there are few guidelines for decisions regarding optimal practices, and few studies have compared the efficiency of different monitoring systems. Our goal was to determine the impact of different monitoring systems on the time to detect and respond to critical cardiac events. To this end, we compared the process of communicating a critical alarm to the patient’s nurse in 2 hospitals with different monitoring systems, to determine the most efficient system. We conducted in situ unannounced simulations of cardiac arrest in the 2 hospitals to measure the response times of monitor watchers and patient care unit staff. As expected, we found response times to be shorter in the hospital that had a more direct method for monitor watchers to contact patients’ nurses. In addition to the method for communicating arrhythmias, there are many other differences between the monitoring systems in the 2 hospitals that could also have affected response times. We are using discrete event simulation to develop computer simulation models of the hospitals that will allow us to take multiple factors into account when comparing them.

scholarly journals A Discrete Event Simulation Model to Evaluate the Impact of Treatment Sequences on Long-Term Patient Outcomes in Multiple Myeloma (MM)

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 4737-4737
Author(s):  
Jinan Liu ◽  
Daniel Moldaver ◽  
Xinmei Zhu ◽  
Allen Zhou ◽  
Eric M Maiese ◽  
...  
Keyword(s):  
Discrete Event ◽  
Line Treatment ◽  
First Line ◽  
Event Simulation ◽  
First Line Therapy ◽  
Line Therapy ◽  
The Impact ◽  
Des Model ◽  
First Line Treatment ◽  
Treatment Sequences

Purpose: Many new therapies have been approved for the treatment of multiple myeloma (MM) in recent years. In June 2019, daratumumab (Darzalex®), a CD38-directed monoclonal antibody, in combination with lenalidomide and dexamethasone (DRd), received FDA approval for the treatment of newly diagnosed MM (NDMM) patients ineligible for stem cell transplantation. This approval provides NDMM patients with a new option as first-line therapy, however, with many options for MM treatment available, identification of the optimal sequence of treatments becomes of critical importance to healthcare providers and payers. Objective: To develop a treatment sequencing discrete event simulation (DES) model that compares unique treatment sequences and estimates the impact of first-line DRd on longer-term outcomes (i.e. survival through multiple lines of therapy) in a general population of MM patients. Methods: A DES model of MM treatment sequences was developed. FDA approved and NCCN recommended non-transplant therapies were included. Treatment efficacy was estimated by digitizing and extrapolating progression-free and overall survival (OS) Kaplan-Meier (KM) data, using established methods (Guyot et al., 2012). Five common parametric functions were fit, and the curve-of-best-fit was determined by standard statistics (i.e., Akaike & Bayesian information criterion) and visual inspection. The model was developed to allow patients to go through up to 6 lines of treatment. The time of progression and death events were randomly assigned based on the estimated parametric functions. All-cause mortality data (US Census 2016) were considered in the simulation of survival. Where data were not available, previous therapies were assumed not to impact the efficacy of subsequent therapies. Model survival predictions were validated against published real-world estimates of survival from: 1) a retrospective analysis of a large American electronic medical record database, Humedica (Hari et al., 2018) and 2) a retrospective analysis of the Dutch PHAROS registry (Verelst et al., 2017). The Humedica and PHAROS datasets reported on the effects of three and four lines of treatment, respectively, starting with newly diagnosed patients. From the Humedica dataset, first-line therapy consisted of proteasome inhibitor- (PI), immunomodulatory- (IMiD), and PI + IMiD-based regimens in 42%, 34% and 18% of patients, respectively, with other regimens used in 7% of cases. Treatment was primarily doublets (63%) rather than triplets (37%). From the PHAROS registry, first-line therapy was primarily thalidomide-based (66%), bortezomib-based (15%), or lenalidomide-based (7%). Statistically, validation was quantified through the calculation of the mean absolute percent error (MAPE), a commonly used statistic to assess the accuracy of forecasts. To estimate the impact of first-line DRd, a hypothetical analysis was simulated wherein all first-line treatments described within both the Humedica and PHAROS datasets were replaced with DRd. Following DRd, patients received subsequent treatment as described within those datasets. Results: Restriction of the modeled treatment algorithm to treatment patterns described within the Humedica database and PHAROS registry led to valid simulated OS estimates that were in line with the published data (MAPE values of 9% and 19% for Humedica and PHAROS, respectively). Utilization of DRd as first-line treatment was estimated to increase OS in scenario analyses (Table 1). The DES model estimated 5-year survival rates for frontline DRd with subsequent therapy based upon the Humedica and PHAROS datasets were 62% and 57%, respectively. In contrast, the estimated 5-year survival for the Humedica and PHAROS datasets without DRd frontline was 40% and 26% respectively, highlighting a 22-31% incremental survival benefit due to DRd at 5-years. Conclusions: The MM DES model validated well to the published estimates of real-world OS. Preliminary results indicate that first-line treatment with DRd may improve OS in MM compared with historical treatment sequences, that include PI+IMiD based triplets as first-line treatment. The model is being further developed to describe the impact of sequencing other MM treatment regimens to estimate the optimal treatment pathways. Disclosures Liu: Janssen Scientific Affairs, LLC: Employment, Equity Ownership. Moldaver:Janssen Scientific Affairs, LLC: Consultancy. Zhu:Janssen Scientific Affairs, LLC: Other: contractor for Janssen. Zhou:Janssen Scientific Affairs, LLC: Consultancy. Maiese:Janssen: Employment, Equity Ownership. Hollmann:Janssen Scientific Affairs, LLC: Consultancy.

scholarly journals A Discrete Event Simulation Analysis of the Bullwhip Effect in a Multi-Product and Multi-Echelon Supply Chain of Fast Moving Consumer Goods

2020 ◽  
pp. 561-576
Author(s):  
Ramsha Ali ◽  
Ruzelan Khalid ◽  
Shahzad Qaiser
Keyword(s):  
Supply Chain ◽  
Information Sharing ◽  
Discrete Event Simulation ◽  
Fast Moving ◽  
Lead Time ◽  
Discrete Event ◽  
Bullwhip Effect ◽  
Event Simulation ◽  
Stochastic Lead Time ◽  
The Impact

Timely delivery is the major issue in Fast Moving Consumer Good (FMCG) since it depends on the lead time which is stochastic and long due to several reasons; e.g., delay in processing orders and transportation. Stochastic lead time can cause inventory inaccuracy where echelons have to keep high product stocks. Such performance inefficiency reflects the existence of the bullwhip effect (BWE), which is a common challenge in supply chain networks. Thus, this paper studies the impact of stochastic lead time on the BWE in a multi-product and multi-echelon supply chain of FMCG industries under two information-sharing strategies; i.e., decentralized and centralized. The impact was measured using a discrete event simulation approach, where a simulation model of a four-tier supply chain whose echelons adopt the same lead time distribution and continuous review inventory policy was developed and simulated. Different lead time cases under the information-sharing strategies were experimented and the BWE was measured using the standard deviation of demand ratios between echelons. The results show that the BWE cannot be eliminated but can be reduced under centralized information sharing. All the research analyses help the practitioners in FMCG industries get insight into the impact of sharing demand information on the performance of a supply chain when lead time is stochastic.

scholarly journals Simulation modeling and analysis of primary health center operations

SIMULATION ◽  
2021 ◽  
pp. 003754972110309
Author(s):  
Mohd Shoaib ◽  
Varun Ramamohan
Keyword(s):  
Resource Utilization ◽  
Waiting Times ◽  
Discrete Event ◽  
Simulation Models ◽  
Modeling And Analysis ◽  
Healthcare Facilities ◽  
Event Simulation ◽  
Analytical Approximations ◽  
Primary Health ◽  
Generic Modeling

We present discrete-event simulation models of the operations of primary health centers (PHCs) in the Indian context. Our PHC simulation models incorporate four types of patients seeking medical care: outpatients, inpatients, childbirth cases, and patients seeking antenatal care. A generic modeling approach was adopted to develop simulation models of PHC operations. This involved developing an archetype PHC simulation, which was then adapted to represent two other PHC configurations, differing in numbers of resources and types of services provided, encountered during PHC visits. A model representing a benchmark configuration conforming to government-mandated operational guidelines, with demand estimated from disease burden data and service times closer to international estimates (higher than observed), was also developed. Simulation outcomes for the three observed configurations indicate negligible patient waiting times and low resource utilization values at observed patient demand estimates. However, simulation outcomes for the benchmark configuration indicated significantly higher resource utilization. Simulation experiments to evaluate the effect of potential changes in operational patterns on reducing the utilization of stressed resources for the benchmark case were performed. Our analysis also motivated the development of simple analytical approximations of the average utilization of a server in a queueing system with characteristics similar to the PHC doctor/patient system. Our study represents the first step in an ongoing effort to establish the computational infrastructure required to analyze public health operations in India and can provide researchers in other settings with hierarchical health systems, a template for the development of simulation models of their primary healthcare facilities.

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