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How to Address Uncertainty in Health Economic Discrete-Event Simulation Models: An Illustration for Chronic Obstructive Pulmonary Disease.

ABSTRACT: Background. Evaluation of personalized treatment options requires health economic models that include multiple patient characteristics. Patient-level discrete-event simulation (DES) models are deemed appropriate because of their ability to simulate a variety of characteristics and treatment pathways. However, DES models are scarce in the literature, and details about their methods are often missing. Methods. We describe 4 challenges associated with modeling heterogeneity and structural, stochastic, and parameter uncertainty that can be encountered during the development of DES models. We explain why these are important and how to correctly implement them. To illustrate the impact of the modeling choices discussed, we use (results of) a model for chronic obstructive pulmonary disease (COPD) as a case study. Results. The results from the case study showed that, under a correct implementation of the uncertainty in the model, a hypothetical intervention can be deemed as cost-effective. The consequences of incorrect modeling uncertainty included an increase in the incremental cost-effectiveness ratio ranging from 50% to almost a factor of 14, an extended life expectancy of approximately 1.4 years, and an enormously increased uncertainty around the model outcomes. Thus, modeling uncertainty incorrectly can have substantial implications for decision making. Conclusions. This article provides guidance on the implementation of uncertainty in DES models and improves the transparency of reporting uncertainty methods. The COPD case study illustrates the issues described in the article and helps understanding them better. The model R code shows how the uncertainty was implemented. For readers not familiar with R, the model's pseudo-code can be used to understand how the model works. By doing this, we can help other developers, who are likely to face similar challenges to those described here.

SUBMITTER: Corro Ramos I

PROVIDER: S-EPMC7401182 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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How to Address Uncertainty in Health Economic Discrete-Event Simulation Models: An Illustration for Chronic Obstructive Pulmonary Disease.

Corro Ramos Isaac I Hoogendoorn Martine M Rutten-van Mölken Maureen P M H MPMH

Medical decision making : an international journal of the Society for Medical Decision Making 20200701 5

<b>Background</b>. Evaluation of personalized treatment options requires health economic models that include multiple patient characteristics. Patient-level discrete-event simulation (DES) models are deemed appropriate because of their ability to simulate a variety of characteristics and treatment pathways. However, DES models are scarce in the literature, and details about their methods are often missing. <b>Methods</b>. We describe 4 challenges associated with modeling heterogeneity and struct ...[more]

PMID: 32608322

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Project description:BACKGROUND:Different strategies toward implementing competing risks in discrete-event simulation (DES) models are available. This study aims to provide recommendations regarding modeling approaches that can be defined based on these strategies by performing a quantitative comparison of alternative modeling approaches. METHODS:Four modeling approaches were defined: 1) event-specific distribution (ESD), 2) event-specific probability and distribution (ESPD), 3) unimodal joint distribution and regression model (UDR), and 4) multimodal joint distribution and regression model (MDR). Each modeling approach was applied to uncensored individual patient data in a simulation study and a case study in colorectal cancer. Their performance was assessed in terms of relative event incidence difference, relative absolute event incidence difference, and relative entropy of time-to-event distributions. Differences in health economic outcomes were also illustrated for the case study. RESULTS:In the simulation study, the ESPD and MDR approaches outperformed the ESD and UDR approaches, in terms of both event incidence differences and relative entropy. Disease pathway and data characteristics, such as the number of competing risks and overlap between competing time-to-event distributions, substantially affected the approaches' performance. Although no considerable differences in health economic outcomes were observed, the case study showed that the ESPD approach was most sensitive to low event rates, which negatively affected performance. CONCLUSIONS:Based on overall performance, the recommended modeling approach for implementing competing risks in DES models is the MDR approach, which is defined according to the general strategy of selecting the time-to-event first and the corresponding event second. The ESPD approach is a less complex and equally performing alternative if sufficient observations are available for each competing event (i.e., the internal validity shows appropriate data representation).

Project description:OBJECTIVES:The aim of this study was to examine the impact of transient ischaemic attack (TIA) service modification in two hospitals on costs and clinical outcomes. DESIGN:Discrete event simulation model using data from routine electronic health records from 2011. PARTICIPANTS:Patients with suspected TIA were followed from symptom onset to presentation, referral to specialist clinics, treatment and subsequent stroke. INTERVENTIONS:Included existing versus previous (less same day clinics) and hypothetical service reconfiguration (7-day service with less availability of clinics per day). OUTCOME MEASURES:The primary outcome of the model was the prevalence of major stroke after TIA. Secondary outcomes included service costs (including those of treating subsequent stroke) and time to treatment and attainment of national targets for service provision (proportion of high-risk patients (according to ABCD2 score) seen within 24?hours). RESULTS:The estimated costs of previous service provision for 490 patients (aged 74±12 years, 48.9% female and 23.6% high risk) per year at each site were £340?000 and £368?000, respectively. This resulted in 31% of high-risk patients seen within 24?hours of referral (47/150) with a median time from referral to clinic attendance/treatment of 1.15 days (IQR 0.93-2.88). The costs associated with the existing and hypothetical services decreased by £5000 at one site and increased £21?000 at the other site. Target attainment was improved to 79% (118/150). However, the median time to clinic attendance was only reduced to 0.85 days (IQR 0.17-0.99) and thus no appreciable impact on the modelled incidence of major stroke was observed (10.7 per year, 99%?CI 10.5 to 10.9 (previous service) vs 10.6 per year, 99%?CI 10.4 to 10.8 (existing service)). CONCLUSIONS:Reconfiguration of services for TIA is effective at increasing target attainment, but in services which are already working efficiently (treating patients within 1-2 days), it has little estimated impact on clinical outcomes and increased investment may not be worthwhile.

Project description:BackgroundIn recent years, the rapid development of information and communications technology enabled by innovations in videoconferencing solutions and the emergence of connected medical devices has contributed to expanding the scope of application and expediting the development of telemedicine.ObjectiveThis study evaluates the use of teleconsultations (TCs) for specialist consultations at hospitals in terms of costs, resource consumption, and patient travel time. The key feature of our evaluation framework is the combination of an economic evaluation through a cost analysis and a performance evaluation through a discrete-event simulation (DES) approach.MethodsThree data sets were used to obtain detailed information on the characteristics of patients, characteristics of patients' residential locations, and usage of telehealth stations. A total of 532 patients who received at least one TC and 18,559 patients who received solely physical consultations (CSs) were included in the initial sample. The TC patients were recruited during a 7-month period (ie, 2020 data) versus 19 months for the CS patients (ie, 2019 and 2020 data). A propensity score matching procedure was applied in the economic evaluation. To identify the best scenarios for reaping the full benefits of TCs, various scenarios depicting different population types and deployment strategies were explored in the DES model. Associated break-even levels were calculated.ResultsThe results of the cost evaluation reveal a higher cost for the TC group, mainly induced by higher volumes of (tele)consultations per patient and the substantial initial investment required for TC equipment. On average, the total cost per patient over 298 days of follow-up was €356.37 (US $392) per TC patient and €305.18 (US $336) per CS patient. However, the incremental cost of TCs was not statistically significant: €356.37 - €305.18 = €51.19 or US $392 - US $336 = US $56 (95% CI -35.99 to 114.25; P=.18). Sensitivity analysis suggested heterogeneous economic profitability levels within subpopulations and based on the intensity of use of TC solutions. In fact, the DES model results show that TCs could be a cost-saving strategy in some cases, depending on population characteristics, the amortization speed of telehealth equipment, and the locations of telehealth stations.ConclusionsThe use of TCs has the potential to lead to a major organizational change in the health care system in the near future. Nevertheless, TC performance is strongly related to the context and deployment strategy involved.

Project description:It is recognized that the study of the disaster medical response (DMR) is a relatively new field. To date, there is no evidence-based literature that clearly defines the best medical response principles, concepts, structures and processes in a disaster setting. Much of what is known about the DMR results from descriptive studies and expert opinion. No experimental studies regarding the effects of DMR interventions on the health outcomes of disaster survivors have been carried out. Traditional analytic methods cannot fully capture the flow of disaster victims through a complex disaster medical response system (DMRS). Computer modelling and simulation enable to study and test operational assumptions in a virtual but controlled experimental environment. The SIMEDIS (Simulation for the assessment and optimization of medical disaster management) simulation model consists of 3 interacting components: the victim creation model, the victim monitoring model where the health state of each victim is monitored and adapted to the evolving clinical conditions of the victims, and the medical response model, where the victims interact with the environment and the resources at the disposal of the healthcare responders. Since the main aim of the DMR is to minimize as much as possible the mortality and morbidity of the survivors, we designed a victim-centred model in which the casualties pass through the different components and processes of a DMRS. The specificity of the SIMEDIS simulation model is the fact that the victim entities evolve in parallel through both the victim monitoring model and the medical response model. The interaction between both models is ensured through a time or medical intervention trigger. At each service point, a triage is performed together with a decision on the disposition of the victims regarding treatment and/or evacuation based on a priority code assigned to the victim and on the availability of resources at the service point. The aim of the case study is to implement the SIMEDIS model to the DMRS of an international airport and to test the medical response plan to an airplane crash simulation at the airport. In order to identify good response options, the model then was used to study the effect of a number of interventional factors on the performance of the DMRS. Our study reflects the potential of SIMEDIS to model complex systems, to test different aspects of DMR, and to be used as a tool in experimental research that might make a substantial contribution to provide the evidence base for the effectiveness and efficiency of disaster medical management.

Project description:ObjectivesThis feasibility study aimed to model in silico the current healthcare system for patients triaged to a primary care disposition following a call to National Health Service (NHS) 111 and determine the effect of reconfiguring the healthcare system to ensure a timely primary care service contact.DesignDiscrete event simulation.SettingSingle English NHS 111 call centre in Yorkshire.ParticipantsCallers registered with a Bradford general practitioner who contacted the NHS 111 service in 2021 and were triaged to a primary care disposition.Primary and secondary outcome measuresFace validity of conceptual model. Comparison between real and simulated data for quarterly counts (and 95% CIs) for patient contact with emergency ambulance (999), 111, and primary and secondary care services. Mean difference and 95% CIs in healthcare system usage between simulations and difference in mean proportion of avoidable admissions for callers who presented to an emergency department (ED).ResultsThe simulation of the current system estimated that there would be 39 283 (95% CI 39 237 to 39 328) primary care contacts, 2042 (95% CI 2032 to 2051) 999 calls and 1120 (95% CI 1114 to 1127) avoidable ED attendances. Modifying the model to ensure a timely primary care response resulted in a mean percentage increase of 196.1% (95% CI 192.2% to 199.9%) in primary care contacts, and a mean percentage decrease of 78.0% (95% CI 69.8% to 86.2%) in 999 calls and 88.1% (95% CI 81.7% to 94.5%) in ED attendances. Avoidable ED attendances reduced by a mean of -26 (95% CI -35 to -17).ConclusionIn this simulated study, ensuring timely contact with a primary care service would lead to a significant reduction in 999 and 111 calls, and ED attendances (although not avoidable ED attendance). However, this is likely to be impractical given the need to almost double current primary care service provision. Further economic and qualitative research is needed to determine whether this intervention would be cost-effective and acceptable to both patients and primary care clinicians.

Dataset Information

How to Address Uncertainty in Health Economic Discrete-Event Simulation Models: An Illustration for Chronic Obstructive Pulmonary Disease.

Publications

How to Address Uncertainty in Health Economic Discrete-Event Simulation Models: An Illustration for Chronic Obstructive Pulmonary Disease.

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