Longer stays at healthcare facilities, driven by uncertain patient load, inefficient patient flow, and lack of real-time information about medical care, pose significant challenges for patients and healthcare providers. Providing patients with estimates of their expected real-time length of stay (RT-LOS), generated as a function of the operational state of the healthcare facility at their anticipated time of arrival (as opposed to estimates of average LOS), can help them make informed decisions regarding which facility to visit within a network. In this study, we develop a healthcare facility assignment (HFA) algorithm that assigns healthcare facilities to patients using RT-LOS predictions at facilities within the network of interest. We describe the generation of RT-LOS predictions via two methodologies: (a) an analytical queuing-theoretic approach, and (b) a hybrid simulation-driven machine learning approach. Because RT-LOS predictors are highly specific to the queuing system in question, we illustrate the development of RT-LOS predictors using both approaches by considering the outpatient experience at primary health centers. Via computational experiments, we compare outcomes from the implementation of the RT-HFA algorithm with both RT-LOS predictors to the case where patients visit the facility of their choice. Computational experiments also indicated that the RT-HFA algorithm substantially reduced patient wait times and LOS at congested facilities and led to more equitable utilization of medical resources at facilities across the network. Finally, we show numerically that the effectiveness of the RT-HFA algorithm in improving outcomes is contingent on the level of compliance with the assignment decision.

In this paper, we consider an agent-based modeling situation where individual-level stochastic outcomes for a subset of agents have to be estimated over time. This situation is commonly found in agent-based models of infectious disease transmission, where outcomes such as life years survived are accumulated over time as a stochastic process. For an ABM with a large agent cohort, this longitudinal outcome estimation process can incur substantial computational expense. We present two methods to alleviate this expense, both of which do not involve any curve-fitting; however, they involve one-time generation of repositories of outcome realizations stratified based on agent characteristics. The first method, the outcomes accumulation and allocation (OAA) approach, involves accumulation of outcomes between relevant changes in agent status as well as random sampling and allocation of outcomes from the repository at relevant points of change in agent status. The second method, the sample path-based sampling and allocation (SPSA) approach, eliminates the need for outcomes accumulation within the ABM execution by allocating outcomes based on the sample path experienced by an agent. Computational experiments with an ABM of the spread of hepatitis C virus in the Indian context indicate that runtimes under the OAA and SPSA approaches are approximately three-fifth and one-third of the benchmark accumulation-only outcomes estimation approach, while yielding comparable outcome distributions and their expectations. We also provide theoretical support for equivalence of outcome expectations between the OAA, SPSA and the benchmark accumulation-only approaches, and discuss the worst-case space complexity of repository generation for each method.

In this paper, we consider an outpatient consultation scheduling system with equal-length slots wherein a set of slots each day are reserved for walk-ins. Specifically, we consider the following questions in deciding slot start times to communicate to scheduled patients: (a) should information regarding patient arrival with respect to the slot start time communicated to them (arrival offset with respect to slot start - i.e., are they typically late or early) be considered in deciding the slot start time for communication, and (b) what impact does rounding the slot start time to the nearest 5th or 10th minute have on relevant outcomes? We answer these questions using a validated discrete-event simulation of an FCFS outpatient appointment system in a hospital accommodating both scheduled and walk-in patients. We also describe the development of the simulation itself, which is designed to optimize policies regarding management of walk-in patients and integration of telemedicine.

Healthcare facility location (HFL) aims to ensure optimal demand coverage using predominantly mathematical optimisation (MO) techniques. However, stochastic characteristics in HFL problems cannot often be estimated directly as they are emergent properties of complex operational dynamics. Discrete-event simulation (DES) is often used to capture such stochastic operational dynamics and estimate such parameters. Therefore, we solve the HFL problem in two stages, wherein a system DES is used to characterise problem stochasticity in the first stage, and facilities are optimally located in the second stage through either an MO formulation parameterised by the DES (the DES + MO approach), or through simulation optimisation (SO) methods that directly engage with problem stochasticity. In this study, using a case study on the public healthcare facility network (PHN) response to the COVID-19 pandemic, we examine how the DES + MO approach compares with a DES-based SO approach in terms of solution quality and ease of computation.

Abstraction or substitution and aggregation are the most widely used simulation model simplification operations. Abstraction involves replacing subsystems within a discrete-event simulation (DES) with one or more quantities - typically random variables - representing the lengths of stay in the subsystems(s) in question to create a `simplified' system comprising only of subsystems of interest to the analysis at hand. Aggregation involves replacing more than one subsystem of the original `parent' simulation with a single subsystem. However, the model simplification process itself can be expensive, in terms of the computational runtime and effort required to collect the data required to estimate the distributions of the length of stay variables, the distribution-fitting process, and testing and validation of the simplified model. Moreover, the savings in simulation runtime that the simplification process yields is a priori unknown to the modeller. In this context, a method that predicts the runtime savings (RS) from DES model simplification operations before their execution - at the conceptualisation stage of the simplified model development process - may help judge whether its development is indeed worth undertaking. In this paper, we present a queueing-theoretic framework for the prediction of RS from model simplification operations. Our framework is applicable for DES models comprising M/M/, M/G/ and G/G/ subsystems. The performance of the RS prediction framework is demonstrated using multiple computational experiments. Our proposed framework contributes to the literature around DES model complexity and more broadly to DES runtime prediction.

BACKGROUND:Public healthcare delivery in India faces several operational challenges, including congestion and long wait times at higher-level facilities and low utilization of lower-level facilities. Effective referral mechanisms can help address these issues. However, before designing and implementing potential new referral mechanisms, it is essential to understand patient and provider views. The objective of this study is to quantitatively assess patient and provider perceptions of both existing and potential new referral mechanisms and their association with patient socioeconomic and demographic attributes. METHODS:A cross-sectional survey was conducted to record and examine patient referral mechanisms - from patient and provider perspectives - currently operational at public healthcare facilities in an urban Indian district. Patient and doctor perceptions regarding potential new referral mechanisms were also assessed, such as medical referral with noncompliance penalties for out-of-turn visits to higher-level facilities, and operational referral, which involves referring patients to same-tier facilities with lower patient loads. Multinomial logistic regression was used to identify statistically significant associations between patient perceptions and socioeconomic and demographic attributes. RESULTS:Survey results provided quantitative evidence of nonadherence to existing referral mechanisms by a significant proportion of patients, and logistic regression analyses showed statistical associations between patient socioeconomic and demographic variables and their willingness towards implementation of potential new referral mechanisms. CONCLUSIONS:Based on study findings, existing referral mechanisms can be strengthened, and potential new referral mechanisms, with appropriate refinements, can be introduced to mitigate overcrowding, care provision delays, and other operational challenges in Indian healthcare delivery.

This paper is motivated by a panel organized by the Healthcare and Life Sciences track at the 2025 Winter Simulation Conference (WSC). We summarize the panelists' perspectives and reflect on current trends and future research directions for simulation applications in healthcare and life sciences. We begin with a brief review of key methodologies and application trends from the past decade of WSC proceedings. We then present expert insights from a range of application areas, including (bio)pharmaceutical manufacturing, hospital operations, public health and epidemiology, and modeling human behavior. The panelists provide diverse perspectives from academia and industry, and highlight emerging challenges, opportunities, and future research directions to advance simulation in healthcare and life sciences.

In the dynamic landscape of technological advancements, the Digital Twin is emerging as a powerful aid with the potential to transform traditional decision-making approaches for diverse complex systems. Technically, it offers a paradigm shift in our understanding and resolution of intricate problems by effectively mirroring real-world entities. This enables us to comprehend their behaviors through simulation, anticipate anomalies for outlier environmental conditions, and derive evidence-driven solutions. While proven effective in physical and cyber-physical systems, its untapped potential lies in the realm of techno-socio-economic systems that operate in dynamic and uncertain environments. The use of such technology in informed decision-making is significant for enterprises and society, as modern businesses and societal structures demand innovative solutions that transcend conventional approaches. With the growing need, the global digital twin market is expected to expand from $11.51 billion in 2023 to $137.67 billion by 2030, impacting domains such as manufacturing, healthcare, sustainability, smart cities, and more. However, realizing this vast business potential hinges on advancing technological capabilities and fostering innovation. This workshop aims to discuss the existing landscape of digital twins and explore uncharted territory by harnessing the latent potential of Digital Twins within techno-socio-economic systems.

Background: Telemedicine is viewed as a crucial tool for addressing the challenges of limited medical resources at health care facilities. However, its adoption in health care is not entirely realized due to perceived barriers. This systematic review outlines the critical facilitators and barriers that influence the implementation of telemedicine in the Indian health care system, observed at the infrastructural, sociocultural, regulatory, and financial levels, from the perspectives of health care providers, patients, patient caregivers, society, health organizations, and the government. Methods: This review complies with the current Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols 2015. A total of 2,706 peer-reviewed studies published from December 2016 to September 2023 in the PubMed, Cochrane, Scopus, Web of Science, CINAHL, MEDLINE, and PsycInfo databases were considered for the title and abstract screening, after which 334 articles were chosen for the full-text review. In the end, 46 studies were selected for data synthesis. Results: Analysis of the literature revealed key barriers such as data privacy and security concerns, doctor and patient resistance to information and communications technology (ICT), infrastructure issues, and ICT training gaps. Facilitators included reduced health care delivery costs, enhanced patient access to health care in remote areas, and shorter patient wait times. The real-world experiences of Indian telemedicine practitioners and pioneers are also explored to complement literature-based perspectives on telemedicine implementation. Both stress the need for reliable internet connectivity, technological adoption, comprehensive ICT training, positive sociocultural attitudes, stringent data privacy measures, and viable business models as crucial for effective telemedicine adoption, with experts emphasizing practical adaptability alongside the literature-recognized facilitators.

In this study, we consider hybrid simulations consisting of an agent-based simulation (ABS) to model disease transmission in a population, and a discrete-time Markov chain executed as a Monte Carlo simulation to model the heterogeneous progression of the disease in infected agents. In such scenarios, execution of the ABS is stopped at a certain time point. At this point, disease-related outcomes for infected agents are estimated by executing the disease progression Monte Carlo simulation for each infected agent over their lifespans, well beyond the execution horizon of the ABS. This can incur substantial computational expense. We present a novel method to alleviate this computational burden by randomly sampling and allocating disease-related outcomes from a repository of outcomes generated and stored as a one-time exercise prior to execution of the hybrid simulation. We demonstrate the effectiveness of our approach via a stylized hybrid simulation of a hypothetical infectious disease transmission scenario.

In the dynamic landscape of technological advancements, the Digital Twin is emerging as a powerful aid with the potential to transform traditional decision-making approaches for diverse complex systems. Technically, it offers a paradigm shift in our understanding and resolution of intricate problems by effectively mirroring real-world entities. This enables us to comprehend their behaviors through simulation, anticipate anomalies for outlier environmental conditions, and derive evidence-driven solutions. While proven effective in physical and cyber-physical systems, its untapped potential lies in the realm of techno-socio-economic systems that operate in dynamic and uncertain environments. The use of such technology in informed decision-making is significant for enterprises and society, as modern businesses and societal structures demand innovative solutions that transcend conventional approaches. With the growing need, the global digital twin market is expected to expand from $11.51 billion in 2023 to $137.67 billion by 2030, impacting domains such as manufacturing, healthcare, sustainability, smart cities, and more. However, realizing this vast business potential hinges on advancing technological capabilities and fostering innovation. This workshop aims to discuss the existing landscape of digital twins and explore uncharted territory by harnessing the latent potential of Digital Twins within techno-socio-economic systems.

Cost-effectiveness analyses, based on decision-analytic models of disease progression and treatment, are routinely used to assess the economic value of a new intervention and consequently inform reimbursement decisions for the intervention. Many decision-analytic models developed to assess the economic value of highly effective directly acting antiviral (DAA) treatments for the hepatitis C virus (HCV) infection do not incorporate the transmission dynamics of HCV, accounting for which is required to estimate the number of downstream infections prevented by curing an infection. In this study, we develop and validate a comprehensive agent-based simulation (ABS) model of HCV transmission dynamics in the Indian context and use it to: (a) quantify the extent to which the cost-effectiveness of a DAA is underestimated - as a function of its uptake rate - if disease transmission dynamics are not considered in a cost-effectiveness analysis model; and (b) quantify the impact of the frequency and timing of treatment with DAAs, also as a function of their uptake rate, within a disease surveillance period on its cost-effectiveness.

Health economic studies for making decisions on disease management interventions usually focus on evaluating cost-effectiveness or net monetary benefits of the interventions. But can be other societal benefits of these interventions which can motivate policymakers to adopt them. Our disease of interest is Hepatitis C virus (HCV) infection, which causes more deaths than HIV infection in India. In advanced stages, the only cure is liver transplantation whose costs are more than ten times of India's annual per-capita income. Indian Punjab has a high HCV prevalence of 3.6% and is also struggling with the menace of injecting drug use which is a major contributor to HCV spread. In early stages of HCV infection, injecting drug users (IDUs) might not know of their infection status; thus, increasing screening and treatment uptake can lead to more IDUs leaving the practice. Nearly 75% of the injecting drug users in Punjab are below 30 years of age, and as most people in the state are also below this age, the risk of infection in young people becomes a concern for economic productivity and life expectancy- the latter becomes enhanced for young individuals as HCV is a slowly progressing disease and while the lifespans of older people are not expected to be affected significantly, reduction in the lifespans of younger people can be significant if not treated. The most effective standard-of-treatment is directly-acting antivirals (DAAs). Models assessing impacts of DAAs in the Indian context have neither incorporated HCV transmission nor evaluated impacts on important societal parameters like age structure of infected patients, IDU prevalence, contribution of HCV to overall mortality and liver transplants needed. We developed an agent-based simulation for modelling HCV transmission in an open cohort. We modelled disease progression using a widely used discrete-time Markov chain. The model was calibrated and validated after execution over a fifty-year period; then we added a treatment component where infected individuals were treated at the end of every year for a ten-year period to achieve a targeted treatment uptake rate. The experiments were carried out for five uptake rates- 10%, 30%, 50%, 70% and 90% of all infected agents. By increasing the uptake rate from 10% to 90%, we observed the following trends in parameter estimates (reported as means of three replications, with respective standard deviations inside parentheses): At the end of the treatment period, the percentage of infected agents below the age of 18 years monotonically decreased from 13% (0.4%) to 7.5% (0.5%), while that above the age of 30 years monotonically increased from 66.5% (0.7%) to 72.4% (0.3%). The IDU prevalence monotonically reduced from 0.081% (0.01%) to 0.044% (0.006%). Over the sixty-year period, the contribution of HCV to overall mortality monotonically decreased from 6.7% (0.2%) to 1.4% (0.09%). The percentage of the general population undergoing liver transplants monotonically decreased from 0.21% (0.015%) to 0.05% (0.005%). A more noticeable reduction was when this percentage was calculated based on the number of infected agents- there was a monotonic decrease from 5.4% (0.27%) to 1.8% (0.16%).

Machine learning (ML) methods are used in most technical areas such as image recognition, product recommendation, financial analysis, medical diagnosis, and predictive maintenance. An important aspect of implementing ML methods involves controlling the learning process for the ML method so as to maximize the performance of the method under consideration. Hyperparameter tuning is the process of selecting a suitable set of ML method parameters that control its learning process. In this work, we demonstrate the use of discrete simulation optimization methods such as ranking and selection (R&S) and random search for identifying a hyperparameter set that maximizes the performance of a ML method. Specifically, we use the KN R&S method and the stochastic ruler random search method and one of its variations for this purpose. We also construct the theoretical basis for applying the KN method, which determines the optimal solution with a statistical guarantee via solution space enumeration. In comparison, the stochastic ruler method asymptotically converges to global optima and incurs smaller computational overheads. We demonstrate the application of these methods to a wide variety of machine learning models, including deep neural network models used for time series prediction and image classification. We benchmark our application of these methods with state-of-the-art hyperparameter optimization libraries such as $hyperopt$ and $mango$. The KN method consistently outperforms $hyperopt$'s random search (RS) and Tree of Parzen Estimators (TPE) methods. The stochastic ruler method outperforms the $hyperopt$ RS method and offers statistically comparable performance with respect to $hyperopt$'s TPE method and the $mango$ algorithm.

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.

This work demonstrates how real-time delay prediction can be used to determine patient diversion across a healthcare facility network. This work presents diversion mechanisms based on real-time predictions of delays at the facilities from which the patient is being diverted as well as at the facility to which diversion is planned. This involves predicting real-time delays not only at the point in time at which the patient arrives at the facility of origin, but also at the near future time point at which the patient may arrive at the facility considered for diversion. Both actual delays and delay predictions based on system state information such as queue length and elapsed service time are considered in the implementation of our proposed diversion mechanism. The implementation of the diversion mechanism is illustrated via a discrete-event simulation of a network of nine primary healthcare facilities in a given region. Diversion is implemented for childbirth patients and inpatients treated at these facilities. As part of this, a novel approximate real-time delay predictor is developed for the queuing systems represented by the childbirth and inpatient care processes, and its performance is compared to existing delay predictors for these queuing systems. With regard to simulation experiments, the conditions under which generation of delay predictions in the near future is relevant is first investigated. The proposed diversion mechanism is simulated, and it is shown that the extent to which operational outcomes become equitably distributed across the PHC networks depends upon the accuracy of the delay predictor.

Model simplification is the process of developing a simplified version of an existing discrete-event simulation (DES) to study the performance of specific system subcomponents relevant to the analysis. The simplified model is referred to as a ‘metasimulation’. A widely used model simplification operation is abstraction, which involves replacing the subcomponents, not core to the analysis, from the parent DES model with random variables representing the lengths of stay in said subcomponents. However, the one-time computational cost of developing metasimulations via abstraction can itself be considerable, as the approach necessitates executing the parent model for generating the necessary data for developing the metasimulation. Thus, this study proposes a queuing-theoretic approach for estimating the computational runtime reduction (CRR) achieved through abstraction, wherein the prediction of CRR precedes the development of the metasimulation. Towards this, we present preliminary results from applying this approach for simplification of DES models made up of M/M/n workstations.

In this work, we examined healthcare seeking behavior (HSB) of patients visiting public healthcare facilities in an urban context. We conducted a cross-sectional survey across twenty-two primary and secondary public healthcare facilities in the South-west Delhi district in India. The quantitative survey was designed to ascertain from patients at these facilities their HSB—i.e., on what basis patients decide the type of healthcare facility to visit, or which type of medical practitioner to consult. Based on responses from four hundred and forty-nine participants, we observed that factors such as wait time, prior experience with care providers, distance from the facility, and also socioeconomic and demographic factors such as annual income, educational qualification, and gender significantly influenced preferences of patients in choosing healthcare facilities. We used binomial and multinomial logistic regression to determine associations between HSB and socioeconomic and demographic attributes of patients at a 0.05 level of significance. Our statistical analyses revealed that patients in the lower income group preferred to seek treatment from public healthcare facilities (OR = 3.51, 95% CI = (1.65, 7.46)) irrespective of the perceived severity of their illness, while patients in the higher income group favored directly consulting specialized doctors (OR = 2.71, 95% CI = (1.34, 5.51)). Other factors such as having more than two children increased the probability of seeking care from public facilities. This work contributes to the literature by: (a) providing quantitative evidence regarding overall patient HSB, especially at primary and secondary public healthcare facilities, regardless of their presenting illness, (b) eliciting information regarding the pathways followed by patients visiting these facilities while seeking care, and (c) providing operational information regarding the surveyed facilities to facilitate characterizing their utilization. This work can inform policy designed to improve the utilization and quality of care at public primary and secondary healthcare facilities in India.