An evaluation of the applicability of system dynamics to patient flow modelling

2010 ◽  
Vol 61 (11) ◽  
pp. 1572-1581 ◽  
Author(s):  
S Vanderby ◽  
M W Carter
Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 2378-2378
Author(s):  
Jonathan Wang ◽  
Saba Vahid ◽  
Hertz Sherrie ◽  
C. Tom Kouroukis

Abstract Objectives: Cancer Care Ontario (CCO) is the provincial governmental organization responsible for planning hematopoietic cell transplantation (HCT) services in Ontario, Canada. The objective of this project is to develop a capacity planning model to investigate the effects on wait times of adding extra bed capacity for allogeneic transplant (ALLOHCT) in HCT centers. Approach: A high-level process flow diagram was generated to understand patient flow at a 6-bed HCT unit within a hospital in Ontario and validated through consultation. This flow diagram was used to construct a system dynamics model to simulate patient flow. The model was parameterized with data from CCO, Discharge Abstract Database, and with hospital and clinical expert input. The effects at six months were projected for five scenarios: 1) current state; 2) increase bed capacity by 1 bed; or 3) increase bed capacity by 2 beds; 4) increasing patient demand by 20 patients per year; 5) combination of scenarios 3 and 4. Provincial clinical consensus established a benchmark wait time of 42 days for ALLOHCT from ready to transplant to the transplant date. In addition, the estimated number of beds required to reduce the wait times to the provincial benchmark within 1 year was calculated. Results:The addition of 1 ALLOHCT bed resulted in a reduction of 22% and 11% to the ALLOHCT wait times and wait lists, respectively. The addition of 2 beds resulted in a reduction of 38% and 22% to the wait times and wait lists, respectively. If the demand increases by 20 patients per year, the addition of 2 beds resulted in a reduction of 16% in the wait times and while the wait list may experience a brief reduction, after 6 months, the wait list size will have increased by 9% as a result of the increased demand. In order to reduce the wait times to the provincial benchmark within 1 year, an additional 8 beds are needed. Considerations: Concurrent planning for additional health human resources (physicians, nurses, etc…) needs to be done to ensure the additional beds are adequately staffed. This model also only considers the effects of adding beds within 1 year. There may be instances where bed space cannot be immediately opened and new capital is required. Additionally, the demand for ALLOHCT continues to increase, which in turn drives up the number of arrivals to the queue. A multi-year model will be built to account for timing of bed openings and increasing demand for ALLOHCT. Conclusion:Using a system dynamics model, we are able to quantify the relationship between ALLOHCT bed capacity and wait times at an HCT center. This model can be used to estimate the ALLOHCT bed requirements for sites in other jurisdictions where ALLOHCT demand and wait time benchmarks are known. Disclosures Kouroukis: Janssen: Research Funding; Karyopharm: Research Funding.


Author(s):  
Mohammad Reza Davahli ◽  
Waldemar Karwowski ◽  
Redha Taiar

In recent years, there has been significant interest in developing system dynamics simulation models to analyze complex healthcare problems. However, there is a lack of studies seeking to summarize the available papers in healthcare and present evidence on the effectiveness of system dynamics simulation in this area. The present paper draws on a systematic selection of published literature from 2000 to 2019, in order to form a comprehensive view of current applications of system dynamics methodology that address complex healthcare issues. The results indicate that the application of system dynamics has attracted significant attention from healthcare researchers since 2013. To date, articles on system dynamics have focused on a variety of healthcare topics. The most popular research areas among the reviewed papers included the topics of patient flow, obesity, workforce demand, and HIV/AIDS. Finally, the quality of the included papers was assessed based on a proposed ranking system, and ways to improve the system dynamics models’ quality were discussed.


2013 ◽  
Vol 26 (5) ◽  
pp. 401-411 ◽  
Author(s):  
Hui Yang ◽  
Satish T.S. Bukkapatnam ◽  
Leandro G. Barajas

1998 ◽  
Vol 11 (3) ◽  
pp. 174-181 ◽  
Author(s):  
T. Hindle ◽  
E. Roberts ◽  
D. Worthington

A soft systems approach, largely based on soft systems methodology, was used to steer a study (completed in 1996) of the National Health Service contracting process. It led to action research projects on a number of related issues. One such area that emerged very strongly concerns service rationalization and service planning, and in particular the location of ‘small’ specialties. A Trust-based study involving patient flow modelling demonstrates the form these problems can take within the internal market and highlights the challenge they make to the contracting process or the new primary care group based commissioning process if they are to be resolved in a rational manner.


2019 ◽  
Vol 21 (3) ◽  
pp. 221-229
Author(s):  
George Hadjipavlou ◽  
Jill Titchell ◽  
Christina Heath ◽  
Richard Siviter ◽  
Hilary Madder

Purpose We sought a bespoke, stochastic model for our specific, and complex ICU to understand its organisational behaviour and how best to focus our resources in order to optimise our intensive care unit’s function. Methods Using 12 months of ICU data from 2017, we simulated different referral rates to find the threshold between occupancy and failed admissions and unsafe days. We also modelled the outcomes of four change options. Results Ninety-two percent bed occupancy is our threshold between practical unit function and optimal resource use. All change options reduced occupancy, and less predictably unsafe days and failed admissions. They were ranked by magnitude and direction of change. Conclusions This approach goes one step further from past models by examining efficiency limits first, and then allowing change options to be quantitatively compared. The model can be adapted by any intensive care unit in order to predict optimal strategies for improving ICU efficiency.


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