A systematic review of simulation methods applied to cancer care services

Figures & data

Table 2. Search logic used in literature searches.

	Cancer term	Simulation term	Topic term	Medical term
Search space	Abstract/Title/Keywords	Abstract/Title/Keywords	Title/Keywords	Title/Keywords
Search term	Cancer	Agent-Based	Appointment	Biolog*
		Agent Based	Bed occupancy	Biomarker*
		Computer simulation	Bottleneck	Biophysical*
		DES	Capacit*	Cell*
		Discrete event simulation	Capacity allocation	Clinical trial*
		Discrete-Event Simulation	Capacity management	DNA
		Monte Carlo	Capacity planning	Dosimetr*
		Simulation	Cost-benefit	Epidemiolog
		Simulation model	Cost-effective*	Genom*
		Stochastic model*	Cost-util*	Genotyp*
		Stochastic processes	Cost*	Molecul*
		Stochastic analysis	Economic evaluation	Oncology trial*
		System dynamics	Economic*	Patholog*
		Visual simulation	Healthcare OR	Prevent*
			Operational performance	Protein
			Operational problem	Screen*
			Operational research	Signal*
			Operations research	Silico*
			Pathway
			Patient care
			Patient flow
			Patient route
			Patient throughput
			Planning policy
			Planning process
			Process flow
			Queue*
			Queuing
			Resource allocation
			Resource management
			Resource planning
			Resource util*
			Schedul*
			Service improvement
			wait* time
			Waiting list

Table 1. Definitions of the problem type.

Problem Type	Definition
Patient Flow/Pathway Modelling	Papers that aimed to map and model the patient flow along a patient’s disease pathway, or map a workflow
Scheduling	Papers whose focus was on improving cancer service scheduling or analysing scheduling strategies
Resource Allocation	Papers that focused on the assignment or distribution of resources, e.g., hospitals beds, scanners, doctors/nurses
Cost Analysis	Papers whose metrics include financial costs related to cancer services

Table 3. Data extraction parameters.

Field	Description
Techniques	Modelling approach/solution method implemented (e.g., Discrete Event Simulation or Agent-Based).
Model objective	Goal of the model (e.g., simulate waiting times, calculate costs associated with screening strategies).
Project objective	Goal of the project (e.g., optimise patient flow along a pathway, improve efficiency of screening strategies).
Metrics	What metrics were measured to identify the success of the simulation?
Software	What software was used to build the simulation?
Data set	What data were used to parameterise the model?
Constraints	What were the parameters and constraints imposed on the model?
Limitations	What were the limitations of the model and project?
Benefits of the approach/methodology	What was improved by the project?
Summary of the pathway	What portion of the patient pathway was modelled (e.g., referral to treatment)?
Cancer type	Which cancer/treatment did the project focus on?
Operational engagement	Were clinicians or operational managers involved in the project and to what extent?
Implementation	Were the suggested interventions used in practice by the hospital?
Suggested changes	What interventions or changes in policy were suggested by the paper?

Table 4. Exclusion criteria for assessing the papers.

Exclusion reasons
Does not solve a cancer care problem. For example:
(a) Does not focus on cancer care services; exclude papers that are simply “inspired” by cancer centres but do not address cancer care services anywhere else in the paper
(b) Was not within a healthcare context, e.g., identifying online spending habits of cancer patients.
(c) The focus is not on improving cancer care services or would not drive a change in hospital operational decisions.
(d) The primary focus of the paper or model was on the evolution of the disease or progression of health states, e.g., tumour growth models.
(e) Focuses on the improvement of cancer treatment techniques, e.g., intensity-modulated radiation therapy, using clinical metrics, e.g., average dose given
(f) The focus is on cancer prevention or screening, e.g., the effect of smoking, heavy metals in river water, obesity, suboptimal breast feeding on cancer risk
(g) Focuses on drug administration techniques.
(2) Does not use a computer simulation technique. For example:
(a) The techniques used only include integer programming and simplex algorithms, data mining techniques, machine learning, or mixed-integer linear programming
(b) The simulation technique was only used to conduct sensitivity analysis and was not the principal simulation technique.
(3) Is a review of OR techniques or a literature review
(4) Unable to obtain the full text or an English language version.

Figure 1. Flow diagram illustrating the number of papers that were excluded at each step of the data extraction.

Figure 2. Cross analysis of the simulation techniques and problem type. The number of studies is shown by/in each circle. Papers that used multiple simulation techniques were included in each category.

Figure 3. Cross analysis of the cancer/treatments addressed by the paper and the problem type. The number of studies is shown by/in each circle.

Figure 4. Frequency of papers within each type of operational engagement: parameter estimation/model assumptions (red), model validation (green), intervention/policy changes (dark blue), none (turquoise).

Figure 5. Frequency of papers with implementations within each type of operational engagement mentioned.

Figure 6. Frequency of suggested changes found in the papers and the impact level of the changes: hospital-level, organisational-level, or the system-level. The number indicates the count of papers. Colours indicate whether those changes were implemented, are under evaluation, were not described as implemented, or implementation is not applicable.

Table 5. Summary of papers that address problems related to patient flow/pathway modelling.

Reference	Simulation Method	Project focus	Project objective	Software used	Metrics
Arafeh et al. (2014)	DES	All (pharmacy modelling)	Reduce patients’ waiting time in an outpatient pharmacy located in a cancer treatment hospital using a six Sigma process	Microsoft Visio, Promodel, EasyFit	Prescription preparation time
Holm et al. (2017)	DES	All (surgical unit)	Investigate effect of different interventions on patient flow and resource utilisation	FlexSim Healthcare	Number of weekly surgery slots, proportion of finished surgeries within opening hours
Kang and Haswell (2020)	DES	All (oncology scheduling)	Understand the impact of a real-time locating system on oncology clinic and assess potential changes to staff scheduling and care process	FlexSim Healthcare	Wait time, length of stay, proportion of patients waiting more than 30min
McKinley et al. (2020)	DES	All (children’s care)	Evaluate the impact on system flow of a time-specific quality improvement protocol for children with cancer and central venous catheters who present to the paediatric ED with fever	Arena	Wait time, length of stay
Rejeb et al. (2018)	DES	All (cancer treatment)	Assess the impact of a Health Information System on the patient pathway	Arena	Time to discharge of the patient, avg. duration to access information, waiting time, resource utilisations, resource costs, service quality
Santibáñez et al. (2009)	DES	All (ambulatory care)	Reduce wait times and address significant challenges regarding the use of space and resources in an ambulatory care unit	Arena	Wait time, clinic end time, and physician idle time
Singh et al. (2022)	DES	All (outpatient department)	Optimise and predict the resource requirements for an outpatient department	FlexSim Healthcare	Resource utilisation, wait time, total throughput
Suss et al. (2018)	DES	All (outpatient clinic)	Improve flow of patients through outpatient oncology clinic, focusing on the steps upstream of the infusion step	Arena	Wait times, pharmacy resource utilisation
Ahmed et al. (2011)	DES	Chemotherapy	Improve the performance of a chemotherapy treatment unit by increasing throughput and reducing average patient waiting time	Arena	Average num. of patients served, wait time, resource utilisation
Alvarado et al. (2018)	DES	Chemotherapy	Evaluate patient and resource scheduling rules for chemotherapy patients	DevsJava	Patient throughput, resource and nurse utilisation
Bernatchou et al. (2017)	DES	Chemotherapy	Evaluate patient flow performance through the outpatient chemotherapy unit	Arena	Wait times, resource utilisation
Lamé et al. (2020)	DES	Chemotherapy	Map the care process to improve patient flow in a chemotherapy unit and chemotherapy preparation pharmacy	ARENA	Wait time
Lu et al. (2012)	DES	Chemotherapy	Optimise treatment delivery and reduce patients’ average waiting time when receiving chemotherapy for different dose administration strategies	Simul8	Drug preparation and delivery time
Babashov et al. (2017)	DES	Radiotherapy	Reduce wait times and determine bottlenecks in the radiotherapy planning process	Simul8	Mean ready-to-treat to treatment time, percentage of patients treated within target time, resource utilisation
Boonmee et al. (2020)	DES	Radiotherapy	Improve patient flow and identify bottlenecks in radiotherapy services	Arena	Number of patients, time spent in a room, operation time, resource utilisation
Miranda and Miranda (2021)	DES	Radiotherapy	Reduce wait times and improve patient throughput along the radiotherapy pathway	Arena	Wait time, patient throughput, entrance time of technicians, exit time of the last patient
Vieira et al. (2019)	DES	Radiotherapy	Assess the impact of using pull and push scheduling strategies in waiting time between referral and first treatment	Tecnomatix Siemens Plant Simulation	Wait time, percentage of patients treated within target time
Camg ̈oz Akda ̆g and Arsoy-Ilikan (2018)	DES	Breast	Decrease patient wait times in a breast cancer centre	Arena	Wait times
Coelli et al. (2007)	DES	Breast	Understand the global assessment of patient flow, equipment utilisation, and personnel needs in a mammography clinic	Promodel	Length of stay, physician/technician utilisation
Smith et al. (2022)	DES	Breast	Assess the impact of a risk prediction tool on flow of patients through a clinic	Simul8	Throughput time, proportion of patient journeys exceeding target times, percent of patient receiving immediate referral
Walker et al. (2016)	Bootstrap resampling with simulations	Breast	Identify how additional resources could be best utilised to reduce delays	R	Throughput time, proportion of patient journeys exceeding target times
Elliott et al. (2019)	DES, Agent-based	Colorectal	Assess the impact of a risk prediction tool on patient bookings and wait times for colonoscopies	AnyLogic	Wait times, hospital costs
T. J. England et al. (2021)	DES	Lung	Identify areas for improving patient throughput times in the lung cancer pathway	Simul8	Average time in the system, percentage of patients treated within target time
T. England et al. (2021)	DES	Lung	Assess the impact of a modified lung cancer pathway on patient flow through the system	Simul8	Average time spent in the system, percentage of patients treated within target time
H. E. Carter et al. (2019)	DES	Gastric	Reduce wait times along the along endoscopic pathway and forecast the impacts of future endoscopic demand	AnyLogic	QALY, incremental cost-effectiveness ratio
Chalk et al. (2019)	DES	Bladder	Model patient flow along bladder cancer pathway to identify bottlenecks and reduce patient delays	Simul8	Average time spent in the system

Table 6. Summary of papers that address problems related to scheduling.

Reference	Simulation method	Project focus	Project objective	Software used	Metrics
Keshtzari and Norman (2022)	DES	All (outpatient clinics)	Identify the required clinic capacity to give timely appointments for cancer patients	Simio	Mean wait time, capacity and patient deviation from treatment plans
Ma et al. (2016)	DES	All (oncology scheduling)	Assess appointment scheduling for new patient consultations	Java, R	Weekly number of add-on consults, the percentage of patients booked within target times, appointment slot utilisation
Ogulata et al. (2009)	DES	All (oncology scheduling)	Calculate the impact of varying the maximum wait times, arrival rates, and slack capacity on delays and accepted/unaccepted patients	SIMAN	Percentage of unaccepted patients, treatment delay times
Ahmed et al. (2011)	DES	Chemotherapy	Improve the performance of a chemotherapy treatment unit by increasing throughput and reducing average patient waiting time	Arena	Average num. of patients served, wait time, resource utilisation
Hadid et al. (2022)	Stochastic Discrete Simulation-Based Multi-Objective Optimization, Monte Carlo	Chemotherapy	Improve scheduling of outpatient chemotherapy appointments and minimise staff overtime and the length of stay of patients	Minitab, OptQuest	Appt make span and patient length of stay (make span), and overtime of resources, utilisation rates, wait times
Heshmat and Eltawil (2021)	DES	Chemotherapy	Find the optimal chemotherapy schedule and patient appointment schedules	ARENA	Wait times
Masselink et al. (2012)	DES	Chemotherapy	Calculate average waiting time for chemotherapy and pharmacy costs under different scheduling strategies	MATLAB	Wait times, cost per day
Liu et al. (2019)	DES	Chemotherapy	Measure demand and treatment capacity to provide timely and quality care to chemotherapy patients	SIMIO	Clinic overtime, patient wait times, nurse direct care time, number of pharmacy preparations
Slocum et al. (2021)	DES	Chemotherapy	Reduce patient wait times and nurse overtime requirements in outpatient chemotherapy operations	Microsoft Excel, VBA	Wait times, average nurse overtime
Bauza and Chow (2019)	Monte Carlo	Radiotherapy services	Improve scheduling for on-call physicists and decrease the number of overworked physicists	MATLAB	Work shift costs
Werker et al. (2009)	DES	Radiotherapy services	Simulate time taken to conduct radiation therapy planning	ARENA	Planning time
Vieira et al. (2019)	DES	Radiotherapy services	Assess the impact of using pull and push scheduling strategies in waiting time between referral and first treatment	Tecnomatix Siemens Plant Simulation	Wait time, percentage of patients treated within target time
van de Vrugt et al. (2017)	DES	Breast	Evaluate scheduling strategies to provide 90% of patients with an appointment within a week	Microsoft Excel, C++ DES Mersenne Twister	Wait time, time to diagnosis, access time
B. P. Berg et al. (2013)	DES	Colorectal	Calculate the cost of no-shows and evaluate the effects of mitigation strategies as well as overbooking policies	ARENA	Net gain ($)
Kazemian et al. (2017)	DES	Colorectal	Simulate patients of different acuity levels moving along surgery pathway to evaluate scheduling policies	MATLAB	Average overtime per day
Martin et al. (2020)	DES, Monte Carlo	Colorectal	Inform clinics on benefits of various scheduling policies	C++ Visual Studio	Wait time, resource idle time, resource overtime
Burns et al. (2022)	DES	Skin	Simulate patient flow through a surgery clinic to assess scheduling strategies	ARENA	Wait times, patient throughput, clinic overtime
Romero et al. (2013)	DES	Skin	Measure and calculate advantages of average throughput time when implementing a one-stop-shop at a skin cancer clinic	ARENA, VBA, Microsoft Excel	Patient throughput time, number of patients treated with Excision/Photodynamic therapy

Table 7. Summary of papers that address problems related to resource allocation.

Reference	Simulation method	Project focus	Project objective	Software used	Metrics
Holm et al. (2017)	DES	All (surgical unit)	Investigate effect of different interventions on patient flow and resource utilisation	FlexSim Healthcare	Number of weekly surgery slots, proportion of finished surgeries within opening hours
Keshtzari and Norman (2022)	DES	All (outpatient clinics)	Identify the required clinic capacity to give timely appointments for cancer patients	Simio	Mean wait time, capacity and patient deviation from treatment plans
Santibáñez et al. (2009)	DES	All (ambulatory care)	Reduce wait times and address significant challenges regarding the use of space and resources in an ambulatory care unit	ARENA	Wait time, clinic end time, and physician idle time
Singh et al. (2022)	DES	All (outpatient department)	Optimise and predict the resource requirements for an outpatient department	Flexsim	Resource utilisation, wait time, total throughput
S. Thomas et al. (2001)	Monte Carlo	All (outpatient clinics)	Identify how much surplus capacity is needed to maintain low wait times for clinics in the UK	C++	Capacity, percentage of untreated patients
Baril et al. (2020)	DES	Chemotherapy	Optimise nurse workload in a chemotherapy workflow	ARENA	Nurse utilisation, average time tasks wait before being completed
Liu et al. (2019)	DES	Chemotherapy	Measure demand and treatment capacity to provide timely and quality care to chemotherapy patients	SIMIO	Clinic overtime, patient wait times, nurse direct care time, and number of pharmacy preparations
Bauza and Chow (2019)	Monte Carlo	Radiotherapy services	Improve scheduling for on-call physicists and decrease the number of overworked physicists	MATLAB	Work shift costs
S. J. Thomas (2003)	Monte Carlo	Radiotherapy services	Predict the number of linear accelerators required for radiotherapy treatment machines	C++	Percentage of patients starting treatment in less than days
B. Berg et al. (2010)	DES	Colorectal	Optimise resource allocation for colonoscopy screening in a colonoscopy suite	ARENA	Wait time, resource utilisation, turnover time
Romero et al. (2013)	DES	Skin	Measure and calculate advantages of average throughput time when implementing a one-stop-shop at a skin cancer clinic	ARENA, VBA, Microsoft Excel	Patient throughput time, number of patients treated with Excision/Photodynamic therapy

Table 8. Summary of papers that address problems related to cost analysis.

Reference	Simulation method	Project focus	Project objective	Software used	Metrics
Blakely et al. (2015)	Des, Monte Carlo	All (cancer care)	Determine the cost-utility of a cancer care coordinator intervention in stage III colon cancer	Tree Age Pro	Incremental cost-effectiveness ratio, QALYs, incremental costs per patient
Rejeb et al. (2018)	DES	All (cancer treatment)	Assess the impact of a Health Information System on the patient pathway	Arena	Time to discharge of the patient, avg. duration to access information, waiting time, resource utilisations, resource costs, service quality
Round et al. (2014)	Monte Carlo	All (hospice care)	Assess the cost effectiveness of a hospice based day therapy intervention for patients living “beyond” cancer	Not mentioned	Incremental cost-effectiveness ratio, QALYs
B. P. Berg et al. (2013)	DES	Colorectal	Calculate the cost of no-shows and evaluate the effects of mitigation strategies as well as overbooking policies	ARENA	Net gain ($) and cost of no-shows
Pilgrim et al. (2009)	DES	Colorectal	Identify cost effectiveness of different care strategies for bowel cancer patients	Simul8	QALYs gained, marginal costs
Price et al. (2022)	Monte Carlo	Radiotherapy	Assess the cost viability of a fully mobile radiation oncology clinic in rural areas	Python	Profit per year, patients seen per year
Heller et al. (2012)	Markov Model, Monte Carlo	Thyroid	Assess the economic impact of Fine-Needle Aspiration treatment for thyroid cancer patients	TreeAge Pro	Incremental cost-effectiveness ratio per QALY

Table 9. Suggestions for future work and potential scenarios where they may be useful.

Suggestion	Use-case
(1) Use hybrid simulations or expand upon existing simulation techniques	Model patient behaviour; decrease wait times across the system
(2) Model pathways specific to prevalent cancers, e.g., lung cancer	Focusing on specific improvements could increase chance of implementation
(3) Include the patient voice and patient demographics; patient engagement as well as operational	Improve quality of care received; address disparity in access to cancer care
(4) Investigate the viability of new technologies, e.g., Mobile outpatient units or telemedicine	Address disparity in access to cancer care; provide alternatives with the aim of decreasing wait times
(5) Model shared resources across the care pathway and between services	Address complex patient pathways and need for integrated care
(6) Utilise design of experiments techniques	Optimise workflows; provide user-friendly platforms for assessing the system

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