To maximize its value, the design, development and implementation of structural health monitoring (SHM) should focus on its role in facilitating decision support. In this position paper, we offer perspectives on the synergy between SHM and decision-making. We propose a classification of SHM use cases aligning with various dimensions that are closely linked to the respective decision contexts. The types of decisions that have to be supported by the SHM system within these settings are discussed along with the corresponding challenges. We provide an overview of different classes of models that are required for integrating SHM in the decision-making process to support the operation and maintenance of structures and infrastructure systems. Fundamental decision-theoretic principles and state-of-the-art methods for optimizing maintenance and operational decision-making under uncertainty are briefly discussed. Finally, we offer a viewpoint on the appropriate course of action for quantifying, validating, and maximizing the added value generated by SHM. This work aspires to synthesize the different perspectives of the SHM, Prognostic Health Management, and reliability communities, and provide directions to researchers and practitioners working towards more pervasive monitoring-based decision-support.

Attempts to formalize inspection and monitoring strategies in industry have struggled to combine evidence from multiple sources (including subject matter expertise) in a mathematically coherent way. The perceived requirement for large amounts of data are often cited as the reason that quantitative risk-based inspection is incompatible with the sparse and imperfect information that is typically available to structural integrity engineers. Current industrial guidance is also limited in its methods of distinguishing quality of inspections, as this is typically based on simplified (qualitative) heuristics. In this paper, Bayesian multi-level (partial pooling) models are proposed as a flexible and transparent method of combining imperfect and incomplete information, to support decision-making regarding the integrity management of in-service structures. This work builds on the established theoretical framework for computing the expected value of information, by allowing for partial pooling between inspection measurements (or groups of measurements). This method is demonstrated for a simulated example of a structure with active corrosion in multiple locations, which acknowledges that the data will be associated with some precision, bias, and reliability. Quantifying the extent to which an inspection of one location can reduce uncertainty in damage models at remote locations has been shown to influence many aspects of the expected value of an inspection. These results are considered in the context of the current challenges in risk based structural integrity management.

Over the past decade in the Netherlands, most operators have only developed a single doublet. The learning effect from these single events is suboptimal, and operators have only been capable of developing doublets in areas with relatively low exploration risk. This ‘stand-alone’ approach can be significantly improved by a collective approach to derisk regions with similar subsurface characteristics. Such a play-based portfolio approach, which is common in the oil and gas industry, can help to accelerate the development of the geothermal industry through unlocking resource potential in areas marked by high upfront geological risk, effectively helping reduce costs for the development. The basis of the methodology is to deploy new information to the play portfolio by trading off with the risk of the first wells, resulting in a strong geological risk reduction.

The added value of the portfolio approach is demonstrated for the Netherlands in this paper through a comparison with a ‘stand-alone’ development. In the stand-alone approach, each new project will be equally risky, and therefore relatively unprofitable. In the case of a portfolio approach, all experience about the play is used optimally for derisking. In case of success, subsequent projects will have a higher chance of being successful, due to the experience gained in previous projects. Even if a project fails, this may help in increasing the probability of success for subsequent projects. For plays that are initially considered too risky for the market to start developing, the value of information (VoI) of a play-based portfolio approach will help by derisking the play to such an extent that it becomes attractive for the market to develop, even at high initial risk. It can be demonstrated for several geothermal plays in the Netherlands that by adopting the portfolio approach, the probability of a play being developed becomes higher, the number of successfully developed projects increases and the average profitability of the project will also be higher. Five more advantages are: (1) continuous improvement by integrated project development, (2) cost reduction through synergy, efficiency and standardisation, (3) optimisation of the surface heat demand and infrastructure, (4) the possibility of structural research and development (R&D) and innovation, and (5) financing advantages. The advantages reinforce each other.

A preliminary estimate of the geothermal potential of the Netherlands adopting the portfolio approach is between 90 and 275 Petajoules (PJ). For about 350 doublets being developed, producing about 70 PJ, the value of the advantage of the play-based portfolio approach is €2 billion for the three main plays: Rotliegend, Triassic and Jurassic/Cretaceous. The learning effects of synergy, efficiency and standardisation are expected to be significant.

The objective of this study was to determine the economic value of obtaining timely and more accurate clinical mastitis (CM) test results for optimal treatment of cows. Typically CM is first identified when the farmer observes recognisable outward signs. Further information of whether the pathogen causing CM is Gram-positive, Gram-negative or other (including no growth) can be determined by using on-farm culture methods. The most detailed level of information for mastitis diagnostics is obtainable by sending milk samples for culture to an external laboratory. Knowing the exact pathogen permits the treatment method to be specifically targeted to the causation pathogen, resulting in less discarded milk. The disadvantages are the additional waiting time to receive test results, which delays treating cows, and the cost of the culture test. Net returns per year (NR) for various levels of information were estimated using a dynamic programming model. The Value of Information (VOI) was then calculated as the difference in NR using a specific level of information as compared to more detailed information on the CM causative agent. The highest VOI was observed where the farmer assumed the pathogen causing CM was the one with the highest incidence in the herd and no pathogen specific CM information was obtained. The VOI of pathogen specific information, compared with non-optimal treatment of Staphylococcus aureus where recurrence and spread occurred due to lack of treatment efficacy, was $20.43 when the same incorrect treatment was applied to recurrent cases, and $30.52 when recurrent cases were assumed to be the next highest incidence pathogen and treated accordingly. This indicates that negative consequences associated with choosing the wrong CM treatment can make additional information cost-effective if pathogen identification is assessed at the generic information level and if the pathogen can spread to other cows if not treated appropriately.

In this study, two methods of entering and accessing dairy herd records are compared: the traditional mail-in Dairy Herd Improvement (DHI) system and the Direct Access to Records by Telephone (DART) system, which provides more timely and convenient access to records. An evaluation of DART was carried out using mail survey responses from 117 DART users and telephone surveys of 40 randomly selected users. Results indicate that DART users are generally satisfied with the system and feel that it improves their herd management. Variations in use of the DART system by DART users are explained by herd, cost, and management variables. DART users and comparable non-DART, DHI users are compared with respect to gains in herd production efficiency. Results indicate that DART users made somewhat better gains in most efficiency measures but that the differences were generally not statistically significant.

Forecasts of economic time series are often evaluated according to their accuracy as measured by either quantitative precision or qualitative reliability. We argue that consumers purchase forecasts for the potential utility gains from utilizing them, not for their accuracy. Using Monte Carlo techniques to incorporate the temporal heteroskedasticity inherent in asset returns, the expected utility of a set of qualitative forecasts is simulated for corn and soybean futures prices. Monetary values for forecasts of various reliability levels are derived. The method goes beyond statistical forecast evaluation, allowing individuals to incorporate their own utility function and trading system into valuing a set of asset price forecasts.

Use of seasonal climate forecasts is a rapidly evolving area. Effective research and application of climate forecasts require close cooperation between scientists in diverse disciplines and decision makers. Successful collaboration requires all players to at least partially understand each other's perspectives. Issues associated with seasonal forecasts, through a selected review of both physical and social sciences literature, is presented. Our hope is that the review will improve research in this area by stimulating further collaborations.

Objectives: The aim of this study was to identify and critically appraise the use of Value of Information (VOI) analyses undertaken as part of health technology assessment (HTA) reports in England and Wales.

Methods: A systematic review of National Institute for Health Research (NIHR) funded HTA reports published between 2004 and 2013 identified the use of VOI methods and key analytical details in terms of: (i) types of VOI methodology used; (ii) parameters and key assumptions; and (iii) conclusions drawn in terms of the need for further research.

Results: A total of 512 HTA reports were published during the relevant timeframe. Of these, 203 reported systematic review and economic modeling studies and 25 of these had used VOI method(s). Over half of the twenty-five studies (n = 13) conducted both EVPI (Expected Value of Perfect Information) and EVPPI (Expected Value of Partial Perfect Information) analyses. Eight studies conducted EVPI analysis, three studies conducted EVPI, EVPPI, and EVSI (Expected Value of Sampling Information) analyses and one study conducted EVSI analysis only. The level of detail reporting the methods used to conduct the VOI analyses varied.

Conclusions: This review has shown that the frequency of the use of VOI methods is increasing at a slower pace compared with the published volume of HTA reports. This review also suggests that analysts reporting VOI method(s) in HTA reports should aim to describe the method(s) in sufficient detail to enable and encourage decision-makers guiding research prioritization decisions to use the potentially valuable outputs from quantitative VOI analyses.

Dual-purpose winter wheat (fall-winter forage plus grain) production is an important economic enterprise in the southern Great Plains. Grazing termination to enable grain production is a critical decision. The objective is to determine the optimal grazing termination date for dual-purpose wheat. The value of knowing the occurrence of first hollow stem (FHS), a wheat growth threshold for grazing termination, is also determined. Results indicate that for most price situations grazing should be terminated at or before FHS. Marginal wheat returns from extended grazing were negative and the value of FHS information ranges from $1.50 to $10 per acre.

The U.S. farming industry is evolving quickly. It is therefore important that state Extension services be prepared to evolve as well. This study uses data collected in a 2007 survey of Ohio farmers to evaluate likelihood and frequency of use of various services offered by Extension as well as overall satisfaction with Extension services. Results indicate that tailoring of topics and communication methods to type of farm and/or farmer informational needs could improve the use of Extension resources. This implies that targeting of information products and methods may improve the performance of Extension education programs and customer satisfaction.

Information about cigarettes can help smokers come to an informed decisionabout what cigarettes to purchase. Countermarketing information can helpsmokers make informed decisions, but little is known about the value of thisinformation to smokers. In this article, we use data from experimentalauctions to estimate the value of countermarketing information that countersindustry claims about reduced-risk cigarettes. We find that this informationhas significant value to smokers who have been exposed to marketinginformation from tobacco companies touting reduced-risk cigarettes, but wefind no evidence it provides value to smokers not exposed to this marketinginformation.

Objectives: The use of value of information methods to inform trial design has been widely advocated but there have been few empirical applications of these methods and there is little evidence they are widely used in decision making. This study considers the usefulness of value of information models in the context of a real clinical decision problem relating to alternative diagnostic strategies for patients with a recent non-ST elevated myocardial infarction.

Methods: A pretrial economic model is constructed to consider the cost-effectiveness of two competing strategies: coronary angiography alone or in conjunction with fractional flow reserve measurement. A closed-form solution to the expected benefits of information is used with optimal sample size estimated for a range of models reflecting increasingly realistic assumptions and alternative decision contexts.

Results: Fractional flow reserve measurement is expected to be cost-effective with an incremental cost-effectiveness ratio of GBP 1,621, however, there is considerable uncertainty in this estimate and consequently a large expected value to reducing this uncertainty via a trial. The recommended sample size is strongly affected by the reality of the assumptions of the expected value of information (EVI) model and the decision context.

Conclusions: Value of information models can provide a simple and flexible approach to clinical trial design and are more consistent with the constraints and objectives of the healthcare system than traditional frequentist approaches. However, the variation in sample size estimates demonstrates that it is essential that appropriate model parameters and decision contexts are used in their application.

Objectives: Preterm birth contributes to a range of healthcare problems amongst infants surmounting to sizeable healthcare costs. Twin pregnancies are at particular risk of preterm birth. The objective of this study was to assess the cost-effectiveness of vaginal progesterone gel for the prevention of preterm birth in twin pregnancies.

Methods: An economic evaluation was conducted alongside a randomized placebo controlled trial (the STOPPIT trial) of vaginal progesterone gel for the prevention of preterm birth in twin pregnancies. Five hundred women were recruited from nine maternity hospitals in the United Kingdom. The outcomes of the economic evaluation were presented in terms of net benefit statistics, cost-effectiveness acceptability curves, generated using the nonparametric bootstrap method, and the expected value of perfect information.

Results: Mean health service costs between the period of randomization and discharge for mother and infant were £28,031 in the progesterone group and £25,972 in the placebo group, generating a mean nonsignificant cost difference of £2,059 (bootstrap mean cost difference £2,334; 95 percent confidence interval: −£5,023, £9,142; p = .33). The probability of progesterone being cost-effective was 20 percent at a willingness to pay threshold of £30,000 per preterm birth prevented. There is little economic justification for conducting further research into the use of vaginal progesterone gel in twin pregnancies for the prevention of preterm birth.

Conclusions: Further studies of preventive interventions for preterm birth more generally are required given the scale of the clinical and economic burden of this condition. These studies should be sufficiently powered for economic endpoints and extend beyond hospital discharge.

Objectives: The aim of this study was to estimate the potential cost-effectiveness and expected value of perfect information of a recently derived clinical prediction rule for patients presenting to emergency departments with chest discomfort.

Methods: A decision analytic model was constructed to compare the Early Disposition Prediction Rule (EDPR) with the current standard of care. Results were used to calculate the potential cost-effectiveness of the EDPR, as well as the Value of Information in conducting further research. Study subjects were adults presenting with chest discomfort to two urban emergency departments in Vancouver, British Columbia, Canada. The clinical prediction rule identifies patients who are eligible for early discharge within 3 hours of presentation to the emergency department. The outcome measure used was inappropriate emergency department discharge of patients with acute coronary syndrome (ACS).

Results: The incremental cost-effectiveness ratio of the EDPR in comparison to usual care was (negative) $2,999 per inappropriate ACS discharge prevented, indicating a potential cost-savings in introducing the intervention. The expected value of perfect information was $16.3 million in the first year of implementation, suggesting a high benefit from conducting further research to validate the decision rule.

Conclusions: The EDPR is likely to be cost-effective; however, given the high degree of uncertainty in the estimates of costs and patient outcomes, further research is required to inform the decision to implement the intervention. The potential health and monetary benefits of this clinical prediction rule outweigh the costs of doing further research.

Objectives: Ongoing developments in design have improved the outlook for left-ventricular assist device (LVAD) implantation as a therapy in end-stage heart failure. Nevertheless, early cost-effectiveness assessments, based on first-generation devices, have not been encouraging. Against this background, we set out (i) to examine the survival benefit that LVADs would need to generate before they could be deemed cost-effective; (ii) to provide insight into the likelihood that this benefit will be achieved; and (iii) from the perspective of a healthcare provider, to assess the value of discovering the actual size of this benefit by means of a Bayesian value of information analysis.

Methods: Cost-effectiveness assessments are made from the perspective of the healthcare provider, using current UK norms for the value of a quality-adjusted life-year (QALY). The treatment model is grounded in published analyses of the Randomized Evaluation of Mechanical Assistance for the Treatment of Congestive Heart Failure (REMATCH) trial of first-generation LVADs, translated into a UK cost setting. The prospects for patient survival with second-generation devices is assessed using Bayesian prior distributions, elicited from a group of leading clinicians in the field.

Results: Using established thresholds, cost-effectiveness probabilities under these priors are found to be low (∼.2 percent) for devices costing as much as £60,000. Sensitivity of the conclusions to both device cost and QALY valuation is examined.

Conclusions: In the event that the price of the device in use would reduce to £40,000, the value of the survival information can readily justify investment in further trials.

Abstract: The purpose of this study is to explain the rationale for the value of information approach to priority setting for research and to describe the methods intuitively for those familiar with basic decision analytical modeling. A policy-relevant case study is used to show the feasibility of the method and to illustrate the type of output that is generated and how these might be used to frame research recommendations. The case study relates to the use of glycoprotein IIb/IIIa antagonists for the treatment of patients with non-ST elevation acute coronary syndrome. This is an area that recently has been appraised by the National Institute for Health and Clinical Excellence.