Decision-relevant Information Research Articles

Are We Missing the Opportunity to Disseminate GOLD Recommendations Through AECOPD Discharge Letters?

Acute exacerbations in chronic obstructive pulmonary disease (AECOPD) lead to poor outcomes and increased burden for patients and healthcare systems. The Global Initiative for COPD (GOLD) includes specific recommendations for AECOPD interventions, discharge criteria, and follow-up. Aligning the AECOPD discharge letters (DL) with GOLD guidelines could facilitate dissemination of recommendations among general practitioners (GPs). This study was conducted to assess the compliance of DL with the GOLD recommendations in Croatia. Pre-pandemic DL of patients presenting for AECOPD to emergency room (ER) were analyzed and stratified by clinical decision to hospitalize (HDL) or discharge patients for outpatient treatment (ERDL). Experienced pulmonologists checked the information from DL against guidelines by using online study-specific questionnaires. In total, 225 HDL and 368 ERDL were analyzed. In most cases, the GOLD ABCD categories (85% HDL, 92% ERDL) or the spirometry-based degree of severity (90% HDL, 91% ERDL) were not included. The number of AEs in the previous year was recorded, but the specific frequent exacerbator phenotype not explicitly stated. The AE phenotype was included in two thirds of HDL and one third of ERDL. The blood eosinophil count was frequently available, but not considered decision-relevant information. Adjustments of previous maintenance therapy, mostly escalation, were recommended in 58.4% HDL and 27.9% ERDL, respectively. Education on proper use of inhalers was recommended only in 15.6% of HDL. Smoking cessation measures were advised in 23.1% HDL and 7.9% ERDL; pulmonary rehabilitation in 35.6% HDL and 0.8% ERDL. Early follow-up was frequently advised (>50%), but rarely appointed. Significant deficiencies in compliance with the GOLD guidelines were identified, translating into a missed opportunity for GPs to become acquainted with GOLD recommendations. These findings emphasize the necessity to increase compliance with guidelines first at specialist level and consequent standardization of DL.

Analysis of Scientific Production on the Use of Big Data Analytics in Performance Measurement Systems

Performance measurement systems have a critical role in organizations management, transforming data into relevant information for decision makers. In recent decades, the amount of data and information generated and shared has increased immensely, providing unprecedented opportunities and challenges for such systems. Faced with this scenario, this article aims to analyze the use of big data analytics in performance measurement systems to clarify the nexus between them. Furthermore, the aim is also to identify the trends and opportunities for future research. To achieve that, we carried a scientific map out using bibliometric analysis. The major results of the research show that the use of big data analytics in PMS has increased in recent years without considering the performance measurement systems characteristics. Incorporating artificial intelligence technologies such as machine learning and deep learning could improve the domain, creating opportunities for empirical works such as the use of unstructured data and applications in Industry 4.0.

Results from a survey of life cycle assessment-aligned socioenvironmental priorities in US and Australian communities hosting oil, natural gas, coal, and solar thermal energy production

Large energy infrastructure is often socially and environmentally disruptive, even as it provides services that people have come to depend on. Residents of areas affected by energy development often note both negative and positive impacts. This reflects the multicategory nature of socioenvironmental outcomes and emphasizes the importance of careful, community-oriented decision making about major infrastructural transitions for processes like decarbonization. Quantitative tools like life cycle assessment (LCA) seek to collect and report comprehensive impact data, but even when successful, their value for decision support is limited by a lack of mechanisms to systematically engage with values-driven tradeoffs across noncommensurable categories. Sensitivity analyses designed to help decision makers and interested parties make sense of data are common in LCA and similar tools, but values are rarely explicitly addressed. This lack of attention to values—arguably the most meaningful set of decision inputs in such tools—can lead to overreliance on single issue (e.g. climate change impact) or proxy (e.g. monetized cost) outputs that reduce the value of holistic evaluations. This research presents results from preregistered hypotheses for a survey of residents of energy-producing communities in the United States (US) and Australia, with the goal of with the goal of uncovering energy transition-relevant priorities by collecting empirical, quantitative data on people’s priorities for outcomes aligned with LCA. The survey was designed to identify diverse value systems, with the goal of making it easier for users to identify and consider value conflicts, potentially highlighting needs for further data collection, system redesign, or additional engagement. Notably, results reveal remarkably consistent priority patterns across communities and subgroups, suggesting that the common LCA practice of equal prioritization might be masking decision-relevant information. Although this effort was designed specifically to support research on energy transitions, future work could easily be extended more broadly.

Advances in application of sub-seasonal weather forecast in Indian agriculture

Under the climatic variability and climate change, skilful weather forecast in different spatial and temporal scale encourages the farmers to organize and activate their own resources in the best possible way to increase the crop production. Though medium range weather forecast is used extensively in operational agromet advisory services, sub-seasonal forecast provides additional decision-relevant information to support the timing of crop planting, irrigation scheduling, and harvesting, particularly in water-stressed regions. In view of that, dynamical and statistical and sub-seasonal seasonal forecast is generated and delivered to the farmers as climate information services in number of countries in the world. Under the Gramin Krishi Mausam Sewa (GKMS) Project, Agricultural Meteorology Division, India Meteorological Department (IMD) in collaboration with Indian Institute of Tropical Meteorology (IITM), Pune, All India Coordinated Research Project on Agrometeorology (AICRPAM), CRIDA, Indian Council of Agricultural Research, Hyderabad prepared Agromet Advisory fortnightly taking into consideration realized rainfall during previous fortnight and extended range rainfall forecast for next fortnight and crop information i.e., state and stage of the crops. In the present article agricultural applications of sub-seasonal forecasts on agricultural management in India has been explored. It has been showed how the extended range weather forecast i.e., sub-seasonal forecast has been developed and translated into agromet advisories for the farming communities to increase crop production in India and whether the present state of accuracy could be used for generating advisory under contingent crop planning conditions and other advisories by citing different case studies and ultimately helping the farming communities to improve their economic conditions. It has been demonstrated here that sub-seasonal forecasts are increasingly being used across agriculture in the country. The sub-seasonal forecasting time scale is therefore a new concept for many users. Because of the additional value of sub-seasonal forecasts for decision-making, it is increasingly gaining interest among users. Present case studies clearly suggest the forecast at sub-seasonal time scale is need of the hour.

Human optional stopping in a heteroscedastic world.

When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or "heteroscedasticity"). We asked humans to perform a categorization task in which discrete, continuously valued samples (oriented gratings) arrived in series until the observer made a choice. Human behavior was best described by a model that adaptively weighted sensory signals by their inverse prediction error and integrated the resulting quantities with a linear urgency signal to a decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in heteroscedastic natural environments. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The Implementation of Management Information System Under the Impact of COVID-19

In 2019, the outbreak of COVID-19 had a serious negative impact on the development of all levels of society, accelerating the development and frequency of use of management information systems for governments and enterprises. In this paper, the implementation of the management information system (MIS) under the impact of COVID-19 are investigated and discussed. After investigation and research, the MIS can gather, consolidation, store, analysis and maintain information. With the development of the age of big data, the usage of computers instead of manual data processing, management information systems can help staff improve work efficiency. The difference between MIS and traditional information systems is that MIS can not only process data, but also combine data analysis and economic management models to provide users with relevant decision-making information. The emergency capability of the management information system can reduce the work pressure of relevant personnel in data entry and management, it can reduce unnecessary losses. These results shed light on guiding further exploration of MIS in risk management.

Do Managers learn from institutional investors through direct interactions?

I examine whether corporate managers learn from institutional investors through direct interactions at investor conferences. I find that managers seek more direct interactions with institutional investors at conferences when they have a greater need for information about their firm's product markets and supply chains. This relation is stronger when managers expect investors to be knowledgeable. I also show that the information acquired from conference interactions is reflected in subsequent manager decisions. First, direct interactions help managers to extract information embedded in stock prices and later use it to make investments. Second, within the same firm and month, managers' personal stock trades immediately after attending a conference earn higher abnormal returns, consistent with direct interactions expanding their private information set. Overall, these findings suggest that managers can acquire decision-relevant information from direct interactions with institutional investors of their firm.

An approach for cross-data querying and spatial reasoning of tunnel alignments

In mechanized tunneling projects, finding a low-risk and cost-effective alignment is an important task. Several alignment variants are usually created and each one is intensely scrutinized. Variants often have individual advantages and disadvantages and can lead to different constructive designs of a tunnel. In order to find the best alignment possible the variants have to be analyzed and evaluated based on requirements and evaluation criteria, such as safety, cost, built environment and operational requirements. To perform this evaluation and to enable comprehensive decision making, a holistic planning environment is examined that includes documents and models of different domains. In general, these domain specific data differ schematically and semantically, which consequently makes it challenging to combine and compare such diverse data. For this purpose, information from different sources must be linked and evaluated in a structured way. In particular, spatial relationships have to be investigated. Therefore, in this paper, ontology databases are utilized to merge BIM and GIS at data level to create an integrated model of the entire tunneling project. Relevant information for decision-making can then be derived, such as the location of private and public buildings that are in a certain vicinity of the planned alignment. On the one hand, the implementation of queries is a popular and frequently used approach to check for semantic properties. On the other hand, using a query language to derive information from geometric data can be challenging, due to the necessity of processing geometric data prior to and during query execution. Additionally, geometric definitions can differ in the considered coordinate reference system, the dimension or the structure. To handle geometry information by employing query languages, representations are methodically transformed to well-known text literals. A simplified and uniform geometric representation can be utilized for spatial reasoning, for example by adopting GeoSPARQL methods.

Production and Price Cycles in Beef Cattle in Rondônia State, Brazilian Amazon

The study evaluates the behavior of production and prices in the beef cattle market in Rondônia, Brazilian Amazon. Covering the period from 1995 to 2020, we used statistical analysis based on the classical multiplicative method of time series. Resultingly, we found that the occurrence of annual seasonality in prices, with a reduction in the first half of the year and an increase in the second half of the year, corresponding to the bovine and off-season harvests, respectively. It was possible to observe the existence of three complete livestock cycles, with an average duration of five years. Trend, seasonality, and cycles of livestock in Rondônia, over the last 25 years, expose a series of relevant information for decision-making by agents in the livestock production chain, in addition to contributing to the understanding of the dynamics of this important agricultural market for Rondônia and Brazilian economy.

Supporting habitat restoration in the northern Gulf of Mexico through synthesis of data on multiple and interacting benefits and stressors.

Outcomes of landscape scale restoration and conservation can be maximized when planning is based upon quantitative and decision-relevant information. Existing tools to support data-driven planning are hindered by regionally inconsistent information and a need for advanced methods to analyze data of varying spatial resolution and coverage. We present a synthesis methodology for region-wide derived metrics to characterize natural resource value, ecosystem stress, and social vulnerability to inform implementation of conservation and restoration projects. Our three-part methodology was developed and tested for the Gulf of Mexico in support of the Southeast Conservation Blueprint that was created to advance the Southeast Conservation and Adaptation Strategy. The first step included integration of prioritized natural resource metrics alongside socio-ecological metrics to create a data layer of synthesized natural resource priority across the northern Gulf of Mexico. The second component was calculation of ecosystem stress indices based on ecologically relevant thresholds and a cumulative ecosystem stress layer, in addition to analyzing correlations between individual stressors and their relative importance. The final component was development of a social vulnerability (SoVI) index. Analysis of these metrics illustrate their ability to effectively capture variability at multiple scales in the Gulf of Mexico, including expected spatial correlation of stressors such as road density and non-point source pollution in populated areas and the dominance of sea-level rise as a future stressor along the coast. Significant composite components of social vulnerability for the northern Gulf of Mexico region were identified and include economic status, professional workforce, elderly population, population stability, migrant workforce, and rural population. To demonstrate the utility of the data synthesis approach, we used the developed data layers to evaluate proposed marsh creation projects in southern Louisiana. The synthesized data layers were capable of distinguishing differences at the scale of individual habitat restoration projects, and high-value projects could be aligned with the goals of key funding streams. This pilot application illustrates how restoration programs could use the methodology developed here to maximize benefits from conservation and restoration actions along the northern Gulf of Mexico or other regions globally.

Differences in comprehending and acting on pandemic health risk information: a qualitative study using mental models

BackgroundA worldwide pandemic of a new and unknown virus is characterised by scientific uncertainty. However, despite this uncertainty, health authorities must still communicate complex health risk information to the public. The mental models approach to risk communication describes how people perceive and make decisions about complex risks, with the aim of identifying decision-relevant information that can be incorporated into risk communication interventions. This study explored how people use mental models to make sense of scientific information and apply it to their lives and behaviour in the context of COVID-19.MethodsThis qualitative study enrolled 15 male and female participants of different ages, with different levels of education and occupational backgrounds and from different geographical regions of Norway. The participants were interviewed individually, and the interview data were subjected to thematic analysis. The interview data were compared to a expert model of COVID-19 health risk communication based on online information from the Norwegian Institute of Public Health. Materials in the interview data not represented by expert model codes were coded inductively. The participants’ perceptions of and behaviours related to health risk information were analysed across three themes: virus transmission, risk mitigation and consequences of COVID-19.ResultsThe results indicate that people placed different meanings on the medical and scientific words used by experts to explain the pandemic (e.g., virus transmission and the reproduction number). While some people wanted to understand why certain behaviour and activities were considered high risk, others preferred simple, clear messages explaining what to do and how to protect themselves. Similarly, information about health consequences produced panic in some interviewees and awareness in others.ConclusionThere is no one-size-fits-all approach to public health risk communication. Empowering people with decision-relevant information necessitates targeted and balanced risk communication.

Age and information preference: Neutral information sources in decision contexts.

Do adults of different ages differ in their focus on positive, negative, or neutral information when making decisions? Some research suggests an increasing preference for attending to and remembering positive over negative information with advancing age (i.e., an age-related positivity effect). However, these prior studies have largely neglected the potential role of neutral information. The current set of three studies used a multimethod approach, including self-reports (Study 1), eye tracking and choice among faces reflecting negative, neutral, or positive health-related (Study 2) and leisure-related information (Study 3). Gaze results from Studies 2 and 3 as well as self-reports from Study 1 showed a stronger preference for sources of neutral than for positive or negative information regardless of age. Findings also suggest a general preference for decision-relevant information from neutral compared to positive or negative sources. Focusing exclusively on the difference between positive (happy) and negative (angry) faces, results are in line with the age-related positivity effect (i.e., the difference in gaze duration between happy and angry faces was significantly larger for older than for younger adults). These findings underscore the importance of neutral information across age groups. Thus, most research on the positivity effect may be biased in that it does not consider the strong preference for neutral over positive information.

Decision-Making Using Big Data Relevant to Sustainable Development Goals (SDGs)

Policymakers, practitioners, and researchers around the globe have been acting in a coordinated manner, yet remaining independent, to achieve the seventeen Sustainable Development Goals (SDGs) defined by the United Nations. Remarkably, SDG-centric activities have manifested a huge information silo known as big data. In most cases, a relevant subset of big data is visualized using several two-dimensional plots. These plots are then used to decide a course of action for achieving the relevant SDGs, and the whole process remains rather informal. Consequently, the question of how to make a formal decision using big data-generated two-dimensional plots is a critical one. This article fills this gap by presenting a novel decision-making approach (method and tool). The approach formally makes decisions where the decision-relevant information is two-dimensional plots rather than numerical data. The efficacy of the proposed approach is demonstrated by conducting two case studies relevant to SDG 12 (responsible consumption and production). The first case study confirms whether or not the proposed decision-making approach produces reliable results. In this case study, datasets of wooden and polymeric materials regarding two eco-indicators (CO2 footprint and water usage) are represented using two two-dimensional plots. The plots show that wooden and polymeric materials are indifferent in water usage, whereas wooden materials are better than polymeric materials in terms of CO2 footprint. The proposed decision-making approach correctly captures this fact and correctly ranks the materials. For the other case study, three materials (mild steel, aluminum alloys, and magnesium alloys) are ranked using six criteria (strength, modulus of elasticity, cost, density, CO2 footprint, and water usage) and their relative weights. The datasets relevant to the six criteria are made available using three two-dimensional plots. The plots show the relative positions of mild steel, aluminum alloys, and magnesium alloys. The proposed decision-making approach correctly captures the decision-relevant information of these three plots and correctly ranks the materials. Thus, the outcomes of this article can help those who wish to develop pragmatic decision support systems leveraging the capacity of big data in fulfilling SDGs.

Self-Repeats-as-Unit-Ends: A Practice for Promoting Interactivity During Surgeons’ Decision-Related Informings

ABSTRACT Although information provision is a prerequisite of informed decision making in surgical consultations, research has shown that patients’ understanding of such information is often limited. We use conversation analysis to illustrate patients’ and surgeons’ management of interactivity, intersubjectivity, and progressivity during information provision, which frequently takes the form of extended tellings. In the midtelling phase of extended tellings, the surgeon is the primary speaker and patients orient to the temporary suspension of the usual turn-taking system. On the rare occasions that patients do take the floor midtelling, it is overwhelmingly following surgeons’ self-repeats-as-unit-ends, which include gist formulations. We argue that surgeons’ self-repeats-as-unit-ends are a practice for encouraging interactivity during extended tellings and as a consequence for facilitating shared understanding of decision-relevant information. Data are in English.

Cocreation with Dutch patients of decision-relevant information to support shared decision-making about adjuvant treatment in breast cancer care.

BackgroundTo support patients in shared decision‐making about treatment options, patient decision aids (PtDAs) usually provide benefit/harm information and value clarification methods (VCMs). Recently, personalized risk information from prediction models is also being integrated into PtDAs. This study aimed to design decision‐relevant information (i.e., personalized survival rates, harm information and VCMs) about adjuvant breast cancer treatment in cocreation with patients, in a way that suits their needs and is easily understandable.MethodsThree cocreation sessions with breast cancer patients (N = 7–10; of whom N = 5 low health literate) were performed. Participants completed creative assignments and evaluated prototypes of benefit/harm information and VCMs. Prototypes were further explored through user testing with patients (N = 10) and healthcare providers (N = 10). The researchers interpreted the collected data, for example, creative and homework assignments, and participants' presentations, to identify key themes. User tests were transcribed and analysed using ATLAS.ti to assess the understanding of the prototypes.ResultsImportant information needs were: (a) need for overview/structure of information directly after diagnosis and; (b) need for transparent benefit/harm information for all treatment options, including detailed harm information. Regarding VCMs, patients stressed the importance of a summary/conclusion. A bar graph seemed the most appropriate way of displaying personalized survival rates; the impact of most other formats was perceived as too distressful. The concept of ‘personalization’ was not understood by multiple patients.ConclusionsA PtDA about adjuvant breast cancer treatment should provide patients with an overview of the steps and treatment options, with layers for detailed information. Transparent information about the likelihood of benefits and harm should be provided. Given the current lack of information on the likelihood of side effects/late effects, efforts should be made to collect and share these data with patients. Further quantitative studies are needed to validate the results and to investigate how the concept of ‘personalization’ can be communicated.Patient or Public ContributionTen breast cancer patients participated in three cocreation sessions to develop decision‐relevant information. Subsequent user testing included 10 patients. The Dutch Breast Cancer Association (BVN) was involved as an advisor in the general study design.

Capital budgeting and managerial empire building

I examine how a company's headquarters use an empire-building manager's report about decision-relevant private information to make capital budgeting decisions and compensate the manager. To this end, I construct a model comprising the headquarters (principal) and the manager (agent), who reports on the investment project's expected profitability. I identify the optimal investment sizes and compensation payments, where the headquarters trade off managerial information rents – arising from empire-building benefits inducing the manager to favour overinvestment – for investment efficiency. The headquarters counteract the manager's desire for overinvestment with investment distortions in the form of underinvestment (or overinvestment) for a high (or low) expected profitability. Due to these distortions, the expected compensation is not monotone in the level of empire-building benefits. Unlike previous capital budgeting studies, in this study, I show that managerial empire-building benefits can multi-directionally affect companies' optimal capital budgeting decisions and related compensation schemes.

Learning Methods of Business Intelligence and Group Related Diagnostics on Patient Management by Using Artificial Dynamic System

Every recognized hospital’s patient management unit (PMU) has focused its efforts on improving clinical patient care, with a process approach, analyzing it from adult emergency overcrowding to prolonged stays in clinical services. It is generating many patients waiting for beds in the emergency service. The PMU does not have a business intelligence (BI) platform that provides information in real-time, generating a blind browsing problem. The purpose is to demonstrate the need for a BI platform using Artificial Intelligence (AI) to analyze in real-time the relevant information for decision making. The methodology consists of analyzing qualitative and quantitatively the statistics of the last three years, both from the emergency service and from the clinical services. This study shows that the saturation of the emergency service responds to the number of patients waiting for beds, which interferes with outpatient care. The projections for 2020 underestimated the demand, and the efforts to open hospital beds and home hospitalization quotas allowed to shovel said excess demand. The average stay numbers continue to increase, as does the number of hospitalized patients for emergencies, generating a progressive growth in demand. It is necessary to have a BI system adapted with AI to perform real-time analysis of the GRD, to be able to act during hospitalization and not afterward.

Do Critical Audit Matters Provide Decision-Relevant Information to Investors? Evidence from Merger and Acquisition Announcements

Do Critical Audit Matters Provide Decision-Relevant Information to Investors? Evidence from Merger and Acquisition Announcements

(In)Significance of Financial Indicators as Stock Price Determinants- The Role of Value-Oriented Vs. Traditional Kpis in the Context of Shareholder Return

As comprehensively discussed by prominent authors such as Fama or Schiller, stock return research is ever since its origin of major importance for the academic and business community given that decision-relevant information is analyzed and discussed, which can be used for investment decisions, to guide and improve corporate performance, to upgrade accounting standards, and disclosure requirements. In summary, partly inconclusive results of the past, the faster-changing environment, and the focus on the pharmaceutical industry motivate further research regarding the subject of stock return analysis. In this regard, this paper provides empirical evidence to the question of which financial indicators are significantly related to stock returns. First, it was theoretically argued that a meaningful indicator of operating and strategic performance should be highly associated with stock returns, given that management decisions that change shareholder wealth should simultaneously cause corresponding stock returns. Based on this assumption, this research analyzed several traditional indicators and EVA (as reported by Bloomberg). Second, the relationship between these financial determinants and stock returns was studied using the data of 29 pharmaceutical companies in a five-year period. Given the dataset's characteristics, a panel regression was the most appropriate method to enlighten the questions of interest. Finally, as a result of this study, empirical evidence shows that several financial indicators such as Equity PS, Turnover PS, or Cash PS are helpful to explain shareholder wealth but only to a very limited extend. Nevertheless, as outlined in the course of this paper, in terms of management control, the advantage of value-oriented indicators should not be denied, as this may contribute to less budgetary slack and better incentive systems.

Decoding climate adaptation governance: A sociotechnical perspective of U.S. airports

Inadequate governance is considered a major barrier for implementing policy, particularly those concerning global and complex challenges such as climate change adaptation. Literature in adaptation policy point to the lack of methods that monitor and assess how decision-making takes place and by whom. Based on a review of over 200 policy documents, this article benchmarks for the first time, the current airport climate adaptation regime in the United States and applies a sociotechnical system framework to scrutinize institutional capacity to address climate change impacts. An innovative policy review system is designed to decode how airport policies create conditions to use climate data as decision-relevant information and produce adaptation actions. Potential climate-cognizant policies are identified and characterized based on their target, timescale, and governance mode. Review results show that the assumption of climate stationarity is widespread. However, there is high potential for technical and, especially, organizational airport policies to incorporate climate science and adaptation pathways. Results also uncover governance barriers related to institutional path-dependence that include: (1) conflicting rationales between adaptation and reliability values, and (2) overpowering technical policies and market governance. These barriers perpetuate scale-mismatch between airport policies and expected impacts from climate change. Finally, we highlight the latent capacity for collaborative governance to advance adaptation regimes in airports and other multiscalar complex infrastructure systems. Our proposed methods and review results identify pathways to enhance institutional capacity for designing and operationalizing transformative adaptation policies.