Expected Value Of Perfect Information Research Articles

Value of information dynamics in Disease X vaccine clinical trials

BackgroundSolutions have been proposed to accelerate the development and rollout of vaccines against a hypothetical disease with epidemic or pandemic potential called Disease X. This may involve resolving uncertainties regarding the disease and the new vaccine. However the value for public health of collecting this information will depend on the time needed to perform research, but also on the time needed to produce vaccine doses. We explore this interplay, and its effect on the decision on whether or not to perform research. MethodWe simulate numerically the emergence and transmission of a disease in a population using a susceptible-infected-recovered (SIR) compartmental model with vaccination. Uncertainties regarding the disease and the vaccine are represented by parameter prior distributions. We vary the date at which vaccine doses are available, and the date at which information about parameters becomes available. We use the expected value of perfect information (EVPI) and the expected value of partially perfect information (EVPPI) to measure the value of information. ResultsAs expected, information has less or no value if it comes too late, or (equivalently) if it can only be used too late. However we also find non trivial dynamics for shorter durations of vaccine development. In this parameter area, it can be optimal to implement vaccination without waiting for information depending on the respective durations of dose production and of clinical research. ConclusionWe illustrate the value of information dynamics in a Disease X outbreak scenario, and present a general approach to properly take into account uncertainties and transmission dynamics when planning clinical research in this scenario. Our method is based on numerical simulation and allows us to highlight non trivial effects that cannot otherwise be investigated.

Optimization of crude oil operations scheduling by applying a two-stage stochastic programming approach with risk management

This paper focuses on the problem of crude oil operations scheduling carried out in a system composed of a refinery and a marine terminal, considering uncertainty in the arrival date of the ships that supply the crudes. To tackle this problem, we develop a two-stage stochastic mixed-integer nonlinear programming (MINLP) model based on continuous-time representation. Furthermore, we extend the proposed model to include risk management by considering the Conditional Value-at-Risk (CVaR) measure as the objective function, and we analyze the solutions obtained for different risk levels. Finally, to evaluate the solution obtained, we calculate the Expected Value of Perfect Information (EVPI) and the Value of the Stochastic Solution (VSS) to assess whether two-stage stochastic programming model offers any advantage over simpler deterministic approaches.

Cost-utility of cardiac contractility modulation in patients with heart failure with reduced ejection fraction in Italy.

Cardiac contractility modulation (CCM) is a device therapy for heart failure, based on the delivery of high-voltage biphasic impulses to the right ventricular septum during the myocardial absolute refractory period. This study evaluated the cost-effectiveness of CCM therapy plus optimal medical therapy (OMT) vs. OMT alone in patients with heart failure with reduced ejection fraction. A Markov model with a lifespan time horizon was developed to assess the cost-utility using the FIX trials as main data sources. A deterministic sensitivity analysis and a probabilistic sensitivity analysis were run to analyse the decision uncertainty in the model through cost-effectiveness acceptability curve (CEAC) and cost-effectiveness acceptability frontier (CEAF). Value of information analysis was also conducted computing the expected value of perfect information (EVPI) and the expected value of partial perfect information. The base case results showed that the CCM plus OMT option was highly cost-effective compared with OMT alone with an incremental cost-utility ratio of €7034/quality-adjusted life year (QALY). The CEAC and CEAF illustrated that for all willingness to pay levels above €5600/QALY, tested up to €50000/QALY, CCM plus OMT alternative had the highest probability of being cost-effective. The EVPI per patient was estimated to be €124412 on a willingness to pay threshold of €30000/QALY. For patients with heart failure with reduced ejection fraction, CCM therapy could be cost-effective when taking a lifetime horizon. Further long-term, post-approval clinical studies are needed to verify these results in a real-world context, particularly concerning the effect of CCM therapy on mortality.

Cost-effectiveness analysis of Next generation sequencing tests in critically ill pediatric patients

Abstract Rare genetic diseases in the pediatric population constitute an urgent global public health issue; overall, more than 300 million people are affected worldwide. Next Generation Sequencing techniques, as Whole genome sequencing (WGS) and Whole exome sequencing (WES), have proven to be significantly supportive in diagnosing these complex conditions. The aim is to evaluate the cost-effectiveness of WGS versus WES in pediatric patients with suspected genetic disorders. A Bayesian Markov model was calibrated among this target population, comparing WGS to WES. Model parameters were retrieved from the scientific literature. Costs and benefits were discounted at a rate of 3%. A lifetime time horizon and the National Health Service perspective was chosen. The Eurozone threshold, ranging from €30,000 to €50,000, was adopted. Markov Chain Monte Carlo was used as the simulation method for Bayesian inference. Uncertainty was explored through a probabilistic sensitivity analysis (PSA) and a value of information analysis (VOI), illustrated through Cost-Effectiveness Acceptability Curve (CEAC) and Expected Value of Perfect Information (EVPI). Results were reported as Incremental Cost-Effectiveness Ratio (ICER), expressed as euros per additional diagnosis. The base case findings highlighted that WGS was cost-effective with an ICER of €31,973. The CEAC showed that for all thresholds over the ICER, WGS had the highest probability of being cost-effective. The EVPI per patient was estimated to be €6,535 on a threshold of €50,000/diagnosis. In addition to being cost-effective, WGS could allow early genetic diagnosis shortening the diagnostic odyssey. The use of WGS in the diagnostic workup has the potential to revolutionise personalised medicine and to play a significant role in achieving SDG 3 by providing personalised healthcare, identifying genetic risk factors for diseases, and informing public health policies for a target population that represents the human capital of the future. Key messages • This analysis informs that WGS is cost-effective and possibly cost-saving in the long term. • WGS could be implemented in the diagnostic workup of children with suspected rare disorders.

Value of Information Analysis of a Web-Based Self-Management Intervention for Chronic Obstructive Pulmonary Disease.

Objective: Technology-based programs can be cost-effective in the management of chronic obstructive pulmonary disease (COPD). However, cost-effectiveness estimates always contain some uncertainty, and decisions based upon them carry some risk. We conducted a value of information (VOI) analysis to estimate the value of additional research of a web-based self-management intervention for COPD to reduce the costs associated with uncertainty. Methods: We used a 10,000-iteration cost-effectiveness model from the health care payer perspective to calculate the expected value of perfect information (EVPI) at the patient- and population-level. An opportunity loss was incurred when the web-based intervention did not produce a greater net monetary benefit than usual care in an iteration. We calculated the probability of opportunity loss and magnitude of opportunity costs as a function of baseline health utility. We aggregated opportunity costs over the projected incident population of inpatient COPD patients over 10 years and estimated it as a function of the willingness-to-pay (WTP) threshold. Costs are in 2022 U.S. Dollars. Results: Opportunity losses were found in 22.7% of the iterations. The EVPIpatient was $78 per patient (95% confidence interval: $75-$82). The probability that the intervention was the optimal strategy varied across baseline health utilities. The EVPIpopulation was $506,666,882 over 10 years for a WTP of $50,000. Conclusions: Research estimated to cost up to $500 million would be warranted to reduce uncertainty. Future research could focus on identifying the impact of baseline health utilities to maximize the cost savings of the intervention. Other considerations for future research priorities include implementation efforts for technology-based interventions.

Intermediate service facility planning in a stochastic and competitive market: Incorporating agent-infrastructure interactions over networks

This paper presents a network-based multi-agent optimization model for the strategic planning of service facilities in a stochastic and competitive market. We focus on the type of service facilities that are of intermediate nature, i.e., users may need to deviate from the shortest path to receive/provide services in between the users’ planned origins and destinations. This problem has many applications in emerging transportation mobility, including dynamic ride-sharing hub design and competitive facility location and allocation problems for alternative fuel vehicle refueling stations. The main contribution of this paper is establishing a new multi-agent optimization framework considering decentralized decision makings of facility investors and users over a transportation network and providing rigorous analyses of its mathematical properties, such as uniqueness and existence of system equilibrium. In addition, we develop an exact convex reformulation of the original multi-agent optimization problems to overcome computational challenges brought by non-convexity. Extensive analysis on case studies showed how the proposed model can capture the complex interaction between different stakeholders in an uncertain environment. Additionally, our model allowed quantifying the value of stochastic modeling and information availability by exploring stochastic metrics, including value of stochastic solution (VSS) and expected value of perfect information (EVPI), in a multi-agent framework.

Toward circular economy for pomegranate fruit supply chain under dynamic uncertainty: A case study

Food security is a fundamental prerequisite of human survival. This study focuses on providing a novel bi-objective model for the design of a green closed-loop pomegranate supply chain based on the value chain. To maximize the benefits of circular economy, we propose a thermochemical conversion process and develop a novel hybrid risk-neutral and risk-averse multi-stage stochastic programming (RNRAMSSP) approach to cope with supply uncertainty. We employ the expected value of perfect information (EVPI) and value of stochastic solution (VSS) to validate the proposed approach. For the VSS metric, we define a novel measure to compare the worst-case cost of the stochastic and deterministic model solutions. To demonstrate the applicability of the proposed model, we provide a real case study in Iran. Based on the VSS metric, the risk-neutral and risk-averse stochastic model solutions save up to 1% and 1.76% in the worst-case cost, respectively, compared to the deterministic model.

Value-of-Information Analysis for External Validation of Risk Prediction Models.

A previously developed risk prediction model needs to be validated before being used in a new population. The finite size of the validation sample entails that there is uncertainty around model performance. We apply value-of-information (VoI) methodology to quantify the consequence of uncertainty in terms of net benefit (NB). We define the expected value of perfect information (EVPI) for model validation as the expected loss in NB due to not confidently knowing which of the alternative decisions confers the highest NB. We propose bootstrap-based and asymptotic methods for EVPI computations and conduct simulation studies to compare their performance. In a case study, we use the non-US subsets of a clinical trial as the development sample for predicting mortality after myocardial infarction and calculate the validation EVPI for the US subsample. The computation methods generated similar EVPI values in simulation studies. EVPI generally declined with larger samples. In the case study, at the prespecified threshold of 0.02, the best decision with current information would be to use the model, with an incremental NB of 0.0020 over treating all. At this threshold, the EVPI was 0.0005 (relative EVPI = 25%). When scaled to the annual number of heart attacks in the US, the expected NB loss due to uncertainty was equal to 400 true positives or 19,600 false positives, indicating the value of further model validation. VoI methods can be applied to the NB calculated during external validation of clinical prediction models. While uncertainty does not directly affect the clinical implications of NB findings, validation EVPI provides an objective perspective to the need for further validation and can be reported alongside NB in external validation studies. External validation is a critical step when transporting a risk prediction model to a new setting, but the finite size of the validation sample creates uncertainty about the performance of the model.In decision theory, such uncertainty is associated with loss of net benefit because it can prevent one from identifying whether the use of the model is beneficial over alternative strategies.We define the expected value of perfect information for external validation as the expected loss in net benefit by not confidently knowing if the use of the model is net beneficial.The adoption of a model for a new population should be based on its expected net benefit; independently, value-of-information methods can be used to decide whether further validation studies are warranted.

Cost-effectiveness analysis of sarcopenia management interventions in Iran

ObjectivesIdentification the optimal management intervention of sarcopenia is a concern of health systems. We aimed to analyze the cost-effectiveness of sarcopenia management strategies in Iran.MethodsWe constructed a lifetime Markov model based on natural history. The strategies comparedincluded exercise training, nutritional supplements, whole body vibration (WBV), and various exercise interventions and nutritional supplement combinations. A total of 7 strategies was evaluated in addition to the non-intervention strategy. Parameter values were extracted from primary data and the literature, and the costs and Quality-adjusted life years (QALYs) were calculated for each strategy. Deterministic and probabilistic sensitivity analysis, including the expected value of perfect information (EVPI), was also performed to determine the robustness of the model. Analyses were performed using the 2020 version of TreeAge Pro software.ResultsAll seven strategies increased lifetime effectiveness (QALYs). The protein and Vitamin D3 (P + D) strategy had the highest effectiveness values among all strategies. After removing the dominated strategies, the estimated ICER for the P + D compared to Vitamin D3 alone (D) strategy was calculated as $131,229. Considering the cost-effectiveness threshold ($25,249), base-case results indicated that the D strategy was the most cost-effective strategy in this evaluation. Sensitivity analysis of model parameters also demonstrated the robustness of results. Also, EVPI was estimated at $273.ConclusionsStudy results, as the first economic evaluation of sarcopenia management interventions, showed that despite the higher effectiveness of D + P, the D strategy was the most cost-effective. Completing clinical evidence of various intervention options can lead to more accurate results in the future.

A Simplified Method for Value of Information Using Constructed Scales

The value of information is a central concept in decision analysis, used to quantify how much the expected outcome of a decision would be improved if epistemic uncertainty could be resolved prior to committing to a course of action. One of the challenges, however, in quantitative analysis of the value of information is that the calculations are demanding, especially in requiring predictions of outcomes as a function of alternative actions and sources of uncertainty. However, the concept of value of information is important in early framing of some decisions, before such predictions are available. We propose a novel measure of the value of information based on constructed scales (CVOI), grounded in the algebra of the expected value of perfect information (EVPI), but requiring less of experts and analysts. The CVOI calculation decomposes EVPI into a contribution representing the relevance of the uncertainty to the decision and a contribution representing the magnitude of uncertainty; constructed ratio scales are then proposed for each contribution. We demonstrate the use of CVOI to identify research priorities related to migratory bird management in the face of climate change. Funding: This work was funded in part by the U.S. Geological Survey National Climate Adaptation Science Center.

Police manpower supply planning for regular duties with stochastic demands

ABSTRACT We develop modeling strategies to alleviate the shortage of police manpower in a stochastic environment, based on flexible manpower supply planning using off-duty police officers and mutual support between police stations, along with flexible working hours and shifts. A stochastic independent assignment model (SIAM) and a stochastic mutual support model (SMSM) are constructed for manpower supply planning for regular policing duties with stochastic demands. These models are designed using mathematical programming techniques to minimize total manpower supply hours, subject to practical constraints. The SIAM is solved using CPLEX, but the SMSM is too complicated for this approach, so we develop our own heuristic algorithm to solve it efficiently. Additionally, we use the expected value of perfect information (EVPI) and the value of the stochastic solution (VSS) to evaluate the stochastic models. Numerical test results indicate that both models perform well, but the SMSM produces better manpower plans for police than the SIAM.

Optimal Epidemic Control under Uncertainty: Tradeoffs between Information Collection and Other Actions.

Recent epidemics and measures taken to control them-through vaccination or other actions-have highlighted the role and importance of uncertainty in public health. There is generally a tradeoff between information collection and other uses of resources. Whether this tradeoff is solved explicitly or implicitly, the concept of value of information is central to inform policy makers in an uncertain environment. We use a deterministic SIR (susceptible, infectious, recovered) disease emergence and transmission model with vaccination that can be administered as 1 or 2 doses. The disease parameters and vaccine characteristics are uncertain. We study the tradeoffs between information acquisition and 2 other measures: bringing vaccination forward and acquiring more vaccine doses. To do this, we quantify the expected value of perfect information (EVPI) under different constraints faced by public health authorities (i.e., the time of the vaccination campaign implementation and the number of vaccine doses available). We discuss the appropriateness of different responses under uncertainty. We show that, in some cases, vaccinating later or with less vaccine doses but more information about the epidemic, and the efficacy of control strategies may bring better results than vaccinating earlier or with more doses and less information, respectively. In the present methodological article, we show in an abstract setting how clearly defining and treating the tradeoff between information acquisition and the relaxation of constraints can improve public health decision making. Uncertainties can seriously hinder epidemic control, but resolving them is costly. Thus, there are tradeoffs between information collection and alternative uses of resources.We use a generic SIR model with vaccination and a value-of-information framework to explore these tradeoffs.We show in which cases vaccinating later with more information about the epidemic and the efficacy of control measures may be better-or not-than vaccinating earlier with less information.We show in which cases vaccinating with fewer vaccine doses and more information about the epidemic and the efficacy of control measures may be better-or not-than vaccinating with more doses and less information.

Cost-utility and value of information analysis of tisagenlecleucel for relapsed/refractory diffuse large B-cell lymphoma in the Irish healthcare setting

ABSTRACT Background The evidence base of tisagenlecleucel is uncertain. Objective To evaluate the cost-effectiveness of tisagenlecleucel. To conduct expected value of perfect information (EVPI) and partial EVPI (EVPPI) analyses. Study Design A three-state partitioned survival model. A short-term decision tree partitioned patients in the tisagenlecleucel arm according to infusion status. Survival was extrapolated to 5 years; general population mortality with a standardised mortality ratio was then applied. EVPI and EVPPI were scaled up to population according to the incidence of the decision. Setting Irish healthcare payer. Participants Patients with relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL). Interventions Tisagenlecleucel versus Salvage Chemotherapy (with or without haematopoietic stem cell transplant). Main Outcome Measure Incremental cost-effectiveness ratio (ICER). Population EVPI and EVPPI. Results At list prices, the ICER was €119,509 per quality-adjusted life year (QALY) (incremental costs €218,092; incremental QALYs 1.82). Probability of cost-effectiveness, at a €45,000 per QALY threshold, was 0%. Population EVPI was €0.00. Population EVPI, at the price of tisagenlecleucel that reduced the ICER to €45,000 per QALY, was €3,989,438. Here, survival analysis had the highest population EVPPI (€1,128,053). Conclusion Tisagenlecleucel is not cost-effective, versus salvage chemotherapy (with or without haematopoietic stem cell transplant), for R/R DLBCL in Ireland. At list prices, further research to decrease decision uncertainty may not be of value.

Appraising the value of compositional information and its implications to scrap-based production of steel

The current nature of steel design and production is a response to meet increasingly demanding applications but without much consideration of end-of-life scenarios. The scrap handling infrastructure, particularly the characterization and sorting, is unable to match the complexity of scrapped products. This is manifested in problems of intermixing and contamination in the scrap flows, especially for obsolete scrap. Also, the segmentation of scrap classes in standards with respect to chemical compositions is based on tolerance ranges. Thus, variation in scrap composition exists even within the same scrap type. This study applies the concept of expected value of perfect information (EPVI) to the context of steel recycling. More specifically, it sets out to examine the difference between having partial and full information on scrap composition by using a raw material optimization software. Three different scenarios with different constraints were used to appraise this difference in terms of production and excess costs. With access to perfect information, production costs decreased by 8–10%, and excess costs became negligible. Overall, comparing the respective results gave meaningful insights on the value of reestablishing the compositional information of scrap at the end of its use phase. Furthermore, the results provided relevant findings and contribute to the ongoing discussions on the seemingly disparate prioritization of economic and environmental incentives with respect to the recycling of steel.

EE569 Axicabtagene Ciloleucel for Relapsed/Refractory Diffuse Large B-Cell Lymphoma in the Irish Healthcare Setting: Cost-Utility and Value of Information Analysis

This study evaluates the cost effectiveness of axicabtagene ciloleucel (CAR T-cell therapy), versus salvage chemotherapy, for the treatment of relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL) in the Irish healthcare setting. The value of conducting further research, to investigate the value of uncertainty associated with the decision problem, is assessed by expected value of perfect information (EVPI).

MSR127 Generalizability of Trial-Based Economic Evaluations. Methods to Account for Covariates and Impact on Cost-Effectiveness and Value of Information

Economic evaluations based on clinical trials may lack generalizability if heterogeneity is not accounted for. We explore different methods to account for covariates to obtain cost-effectiveness results in different populations and assess the expected value of perfect information (EVPI).

Estimated Monetary Value of Future Research Clarifying Uncertainties Around the Optimal Adult Hearing Screening Schedule

Adult hearing screening is not routinely performed, and most individuals with hearing loss (HL) have never had their hearing tested as adults. To project the monetary value of future research clarifying uncertainties around the optimal adult hearing screening schedule. In this economic evaluation, a validated decision model of HL (DeciBHAL-US: Decision model of the Burden of Hearing loss Across the Lifespan) was used to simulate current detection and treatment of HL vs hearing screening schedules. Key model inputs included HL incidence (0.06%-10.42%/y), hearing aid uptake (0.54%-8.14%/y), screening effectiveness (1.62 × hearing aid uptake), utility benefits of hearing aids (+0.11), and hearing aid device costs ($3690). Distributions to model parameters for probabilistic uncertainty analysis were assigned. The expected value of perfect information (EVPI) and expected value of partial perfect information (EVPPI) using a willingness to pay of $100 000 per quality-adjusted life-year (QALY) was estimated. The EVPI and EVPPI estimate the upper bound of the dollar value of future research. This study was based on 40-year-old persons over their remaining lifetimes in a US primary care setting. Screening schedules beginning at ages 45, 55, 65, and 75 years, and frequencies of every 1 or 5 years. The main outcomes were QALYs and costs (2020 US dollars) from a health system perspective. The average incremental cost-effectiveness ratio for yearly screening beginning at ages 55 to 75 years ranged from $39 200 to $80 200/QALY. Yearly screening beginning at age 55 years was the optimal screening schedule in 38% of probabilistic uncertainty analysis simulations. The population EVPI, or value of reducing all uncertainty, was $8.2 to $12.6 billion varying with willingness to pay and the EVPPI, or value of reducing all screening effectiveness uncertainty, was $2.4 billion. In this economic evaluation of US adult hearing screening, large uncertainty around the optimal adult hearing screening schedule was identified. Future research on hearing screening has a high potential value so is likely justified.

Analytics with stochastic optimisation: experimental results of demand uncertainty in process industries

This study reports the test results of a two-stage stochastic linear programming (SLP) model with recourse using a user-friendly generic decision support system (DSS) in a North American steel company. This model has the flexibility to configure multiple material facilities, activities and storage areas in a multi-period and multi-scenario environment. The value of stochastic solution (VSS) with a real-world example has a potential benefit of US$ 24.61 million. Experiments were designed according to the potential joint probability distribution scenarios and the magnitude of demand variability. Overall, 144 SLP optimisation model instances were solved across four industries, namely, steel, aluminium, polymer and pharmaceuticals. The academic contribution of this research is two-fold: first, the potential contribution to profit in a steel company using an SLP model; and second, the optimisation empirical experiments confirm a pattern that the VSS and expected value of perfect information (EVPI) increase with the increase in demand variability. This study has implications for practicing managers seeking business solutions with prescriptive analytics using stochastic optimisation-based DSS. This study will attract more industry attention to business solutions, and the prescriptive analytics discipline will garner more scholarly and industry attention.

Early economic modeling of magnetic resonance image-guided high intensity focused ultrasound compared to radiotherapy for pain palliation of bone metastases.

IntroductionMagnetic Resonance Image-guided High Intensity Focused Ultrasound (MR-HIFU) is a non-invasive treatment option for palliative patients with painful bone metastases. Early evidence suggests that MR-HIFU is associated with similar overall treatment response, but more rapid pain palliation compared to external beam radiotherapy (EBRT). This modelling study aimed to assess the cost-effectiveness of MR-HIFU as an alternative treatment option for painful bone metastases from the perspective of the German Statutory Health Insurance (SHI).Materials and methodsA microsimulation model with lifelong time horizon and one-month cycle length was developed. To calculate the incremental cost-effectiveness ratio (ICER), strategy A (MR-HIFU as first-line treatment or as retreatment option in case of persistent pain or only partial pain relief after EBRT) was compared to strategy B (EBRT alone) for patients with bone metastases due to breast, prostate, or lung cancer. Input parameters used for the model were extracted from the literature. Results were expressed as EUR per quality-adjusted life years (QALYs) and EUR per pain response (i.e., months spent with complete or partial pain response). Deterministic and probabilistic sensitivity analyses (PSA) were performed to test the robustness of results, and a value of information analysis was conducted.ResultsCompared to strategy B, strategy A resulted in additional costs (EUR 399) and benefits (0.02 QALYs and 0.95 months with pain response). In the base case, the resulting ICERs (strategy A vs. strategy B) are EUR 19,845/QALY and EUR 421 per pain response. Offering all patients MR-HIFU as first-line treatment would increase the ICER by 50% (31,048 EUR/QALY). PSA showed that at a (hypothetical) willingness to pay of EUR 20,000/QALY, the probability of MR-HIFU being cost-effective was 52%. The expected value of perfect information (EVPI) for the benefit population in Germany is approximately EUR 190 Mio.ConclusionAlthough there is considerable uncertainty, the results demonstrate that introducing MR-HIFU as a treatment alternative for painful bone metastases might be cost-effective for the German SHI. The high EVPI indicate that further studies to reduce uncertainty would be worthwhile.

Economic evaluation of using polygenic risk score to guide risk screening and interventions for the prevention of type 2 diabetes in individuals with high overall baseline risk.

Type 2 diabetes (T2D) with increasing prevalence is a significant global public health challenge. Obesity, unhealthy diet, and low physical activity are one of the major determinants of the rise in T2D prevalence. In addition, family history and genetic risk of diabetes also play a role in the process of developing T2D. Therefore, solutions for the early identification of individuals at high risk for T2D for early targeted detection of T2D, prevention, and intervention are highly preferred. Recently, novel genomic-based polygenic risk scores (PRSs) have been suggested to improve the accuracy of risk prediction supporting the targeting of preventive interventions to those at highest risk for T2D. Therefore, the aim of the present study was to assess the cost-utility of an additional PRS testing information (as a part of overall risk assessment) followed by a lifestyle intervention and an additional medical therapy when estimated 10-year overall risk for T2D exceeded 20% among Finnish individuals screened as at the high-risk category (i.e., 10%–20% 10-year overall risk of T2D) based on traditional risk factors only. For a cost-utility analysis, an individual-level state-transition model with probabilistic sensitivity analysis was constructed. A 1-year cycle length and a lifetime time horizon were applied in the base-case. A 3% discount rate was used for costs and QALYs. Cost-effectiveness acceptability curve (CEAC) and estimates for the expected value of perfect information (EVPI) were calculated to assist decision makers. The use of the targeted PRS strategy reclassified 12.4 percentage points of individuals to be very high-risk individuals who would have been originally classified as high risk using the usual strategy only. Over a lifetime horizon, the targeted PRS was a dominant strategy (i.e., less costly, more effective). One-way and scenario sensitivity analyses showed that results remained dominant in almost all simulations. However, there is uncertainty, since the probability (EVPI) of cost-effectiveness at a WTP of 0€/QALY was 63.0% (243€) indicating the probability that the PRS strategy is a dominant option. In conclusion, the results demonstrated that the PRS provides moderate additional value in Finnish population in risk screening leading to potential cost savings and better quality of life when compared with the current screening methods for T2D risk.