Optimizing patient flow in a large hospital surgical centre by means of discrete-event computer simulation models

Leveraging multi-tier healthcare facility network simulations for capacity planning in a pandemic

The COVID-19 pandemic has placed severe demands on healthcare facilities across the world, and in several countries, makeshift COVID-19 centres have been operationalised to handle patient overflow. In developing countries such as India, the public healthcare system (PHS) is organised as a hierarchical network with patient flows from lower-tier primary health centres (PHC) to mid-tier community health centres (CHC) and downstream to district hospitals (DH). In this study, we demonstrate how a network-based modelling and simulation approach utilising generic modelling principles can (a) quantify the extent to which the existing facilities in the PHS can effectively cope with the forecasted COVID-19 caseload; and (b) inform decisions on capacity at makeshift COVID-19 Care Centres (CCC) to handle patient overflows. We apply the approach to an empirical study of a local PHS comprising ten PHCs, three CHCs, one DH and one makeshift CCC. Our work demonstrates how the generic modelling approach finds extensive use in the development of simulations of multi-tier facility networks that may contain multiple instances of generic simulation models of facilities at each network tier. Further, our work demonstrates how multi-tier healthcare facility network simulations can be leveraged for capacity planning in health crises.

A discrete event simulation to evaluate impact of radiology process changes on emergency department computed tomography access

BACKGROUND

Hospitals face the challenge of managing demand for limited computed tomography (CT) resources from multiple patient types while ensuring timely access.

METHODS

A discrete event simulation model was created to evaluate CT access time for emergency department (ED) patients at a large academic medical center with six unique CT machines that serve unscheduled emergency, semi-scheduled inpatient, and scheduled outpatient demand. Three operational interventions were tested: adding additional patient transporters, using an alternative creatinine lab, and adding a registered nurse dedicated to monitoring CT patients in the ED.

RESULTS

All interventions improved access times. Adding one or two transporters improved ED access times by up to 9.8 minutes (Mann-Whitney (MW) CI: [-11.0,-8.7]) and 10.3 minutes (MW CI [-11.5, -9.2]). The alternative creatinine and RN interventions provided 3-minute (MW CI: [-4.0, -2.0]) and 8.5-minute (MW CI: [-9.7, -8.3]) improvements.

CONCLUSIONS

Adding one transporter provided the greatest combination of reduced delay and ability to implement. The projected simulation improvements have been realized in practice.

Discrete-event models for the simulation of computed tomography sectors according to hospital structural/organizational changes and expected patient arrival rates

OBJECTIVE

To analyze the types of computed tomography (CT) scanners most suitable for different hospital sizes and 'scenarios' (exam rates and structural/organizational changes), using discrete-event simulation models.

MATERIALS AND METHODS

CT exams were divided into stages, measured during on-site surveys at CT services in small and average size private hospitals. Ten devices in nine health units, five cities and two states of Brazil were studied to this end, and the following data were collected: Time spent in each stage for each type of exam; average monthly number of exams performed and general characteristics of exams. Three arrival rates were defined (103, 154 and 206 patients/day), representing expected demand for the studied units. From these parameters, six scenarios were simulated, consisting of changes in personnel and hospital structure (e.g., 'adding a changing room') in a base scenario (one CT, one changing room, no nursing assistance, arrival rate 1).

RESULTS

It was possible to identify a scenario most useful for very large demands, such as large emergency hospitals in big cities, (a CT, nursing assistance and three changing rooms added to the base scenario). Another identified scenario was more adequate for small demands (adding a changing room to the base scenario).

CONCLUSION

Administrative/organizational measures are a very important factor in defining productivity in a hospital imaging sector. The focus of these measures should be on detecting bottlenecks and improving processes, regardless of the type of equipment used.

Using Discrete-Event Simulation to Promote Quality Improvement and Efficiency in a Radiation Oncology Treatment Center

BACKGROUND

To meet demand for radiation oncology services and ensure patient-centered safe care, management in an academic radiation oncology department initiated quality improvement efforts using discrete-event simulation (DES). Although the long-term goal was testing and deploying solutions, the primary aim at the outset was characterizing and validating a computer simulation model of existing operations to identify targets for improvement.

METHODS

The adoption and validation of a DES model of processes and procedures affecting patient flow and satisfaction, employee experience, and efficiency were undertaken in 2012-2013. Multiple sources were tapped for data, including direct observation, equipment logs, timekeeping, and electronic health records.

RESULTS

During their treatment visits, patients averaged 50.4 minutes in the treatment center, of which 38% was spent in the treatment room. Patients with appointments between 10 AM and 2 PM experienced the longest delays before entering the treatment room, and those in the clinic in the day's first and last hours, the shortest (<5 minutes). Despite staffed for 14.5 hours daily, the clinic registered only 20% of patients after 2:30 PM. Utilization of equipment averaged 58%, and utilization of staff, 56%.

CONCLUSION

The DES modeling quantified operations, identifying evidence-based targets for next-phase remediation and providing data to justify initiatives.

Operations research in global health: a scoping review with a focus on the themes of health equity and impact

BACKGROUND

Operations research (OR) is a discipline that uses advanced analytical methods (e.g. simulation, optimisation, decision analysis) to better understand complex systems and aid in decision-making. Herein, we present a scoping review of the use of OR to analyse issues in global health, with an emphasis on health equity and research impact. A systematic search of five databases was designed to identify relevant published literature. A global overview of 1099 studies highlights the geographic distribution of OR and common OR methods used. From this collection of literature, a narrative description of the use of OR across four main application areas of global health - health systems and operations, clinical medicine, public health and health innovation - is also presented. The theme of health equity is then explored in detail through a subset of 44 studies. Health equity is a critical element of global health that cuts across all four application areas, and is an issue particularly amenable to analysis through OR. Finally, we present seven select cases of OR analyses that have been implemented or have influenced decision-making in global health policy or practice. Based on these cases, we identify three key drivers for success in bridging the gap between OR and global health policy, namely international collaboration with stakeholders, use of contextually appropriate data, and varied communication outlets for research findings. Such cases, however, represent a very small proportion of the literature found.

CONCLUSION

Poor availability of representative and quality data, and a lack of collaboration between those who develop OR models and stakeholders in the contexts where OR analyses are intended to serve, were found to be common challenges for effective OR modelling in global health.

A Literature Review on Validated Simulations of the Surgical Services

The surgical department is a critical unit that oversees multiple surgical-based clinical pathways and works with various other units in a hospital. This department faces numerous challenges relating to variability in demand and management of resources. The aim of this article is to review the application of validated simulation models on hospital-wide surgical services. Each of these models is broadly classified by (i) simulation method and (ii) level of detail given to the management of "patient pathways" and "staff workflows". We remark that very few studies have given attention to the management of staff workflows in their validated simulation models.

Computer modeling of lung cancer diagnosis-to-treatment process

We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed.

Managing health care decisions and improvement through simulation modeling

Simulation modeling is a way to test changes in a computerized environment to give ideas for improvements before implementation. This article reviews research literature on simulation modeling as support for health care decision making. The aim is to investigate the experience and potential value of such decision support and quality of articles retrieved. A literature search was conducted, and the selection criteria yielded 59 articles derived from diverse applications and methods. Most met the stated research-quality criteria. This review identified how simulation can facilitate decision making and that it may induce learning. Furthermore, simulation offers immediate feedback about proposed changes, allows analysis of scenarios, and promotes communication on building a shared system view and understanding of how a complex system works. However, only 14 of the 59 articles reported on implementation experiences, including how decision making was supported. On the basis of these articles, we proposed steps essential for the success of simulation projects, not just in the computer, but also in clinical reality. We also presented a novel concept combining simulation modeling with the established plan-do-study-act cycle for improvement. Future scientific inquiries concerning implementation, impact, and the value for health care management are needed to realize the full potential of simulation modeling.

A cost simulation for mammography examinations taking into account equipment failures and resource utilization characteristics

OBJECTIVE

This work develops a cost analysis estimation for a mammography clinic, taking into account resource utilization and equipment failure rates.

MATERIALS AND METHODS

Two standard clinic models were simulated, the first with one mammography equipment, two technicians and one doctor, and the second (based on an actually functioning clinic) with two equipments, three technicians and one doctor. Cost data and model parameters were obtained by direct measurements, literature reviews and other hospital data. A discrete-event simulation model was developed, in order to estimate the unit cost (total costs/number of examinations in a defined period) of mammography examinations at those clinics. The cost analysis considered simulated changes in resource utilization rates and in examination failure probabilities (failures on the image acquisition system). In addition, a sensitivity analysis was performed, taking into account changes in the probabilities of equipment failure types.

RESULTS

For the two clinic configurations, the estimated mammography unit costs were, respectively, US$ 41.31 and US$ 53.46 in the absence of examination failures. As the examination failures increased up to 10% of total examinations, unit costs approached US$ 54.53 and US$ 53.95, respectively. The sensitivity analysis showed that type 3 (the most serious) failure increases had a very large impact on the patient attendance, up to the point of actually making attendance unfeasible.

CONCLUSIONS

Discrete-event simulation allowed for the definition of the more efficient clinic, contingent on the expected prevalence of resource utilization and equipment failures.

[Primary hip and knee replacement: time required for surgical training]

BACKGROUND

The aim of the present study is to analyse the increased surgical time required due to supervised surgery as an element of costs of education.

MATERIAL AND METHODS

Incision to closure times of 353 primary hip and knee prostheses were evaluated according to educational level. Differences between planned and real operation times were recorded, and the mean DRG proceeds per minute of surgical time were determined.

RESULTS

The difference between incision to closure times of the board certified surgeons for the respective surgical interventions and that of the supervised surgery is statistically significant (p<0.01) and clinically relevant (+15 min for THA, +13 min for TKA). The correlation between planned and real operation time was significantly lower in the category of supervised surgery.

CONCLUSION

There is an increased surgical time required for surgical training. It is the responsibility of health care policy to ensure an appropriate financial compensation.