Decision-relevant Information Research Articles

Information and communication priorities of patients and healthcare professionals in shared decision making regarding adjuvant systemic breast cancer treatment: A survey study

PurposeTo assess information and communication priorities of patients and healthcare professionals in Shared Decision Making about adjuvant systemic treatment of primary breast cancer and identify key decision-relevant information accordingly. MethodsPatients (N = 122) and professionals working with breast cancer patients (N = 118), of whom 38 were nurse practitioners and 32 nurses, were recruited using convenience sampling, and surveyed about information/communication aspects key to decision-making, using ranking assignments. We further posed a simple open question, questions about receiving population-based statistics versus personalized statistics concerning treatment outcomes, and their attitude and experience concerning Shared Decision Making. Data were analyzed using descriptive analysis and a qualitative analysis. ResultsBoth patients and professionals prioritized information about treatment outcomes (i.e., survival, recurrence) as key decision-relevant information for patients. Patients prioritized information about relatively severe treatment side-effects and late effects (e.g., blood clot, stroke), whilst professionals prioritized information about effects that occur relatively often (e.g., hair loss, fatigue). Patients specifically wanted to know if the benefit of treatment is worth the negative impact. Both groups prioritized personalized statistics over population-based statistics. ConclusionsSome differences between patients and professionals were found in information and communication priorities, specifically related to the different side-effects. It seems worthwhile to precisely address these side-effects in Shared Decision Making concerning adjuvant systemic treatment. Furthermore, it seems important to deliberate together on the question if expected benefit of treatment is worth the potential negative impact for the individual patient.

The online metacognitive control of decisions

Difficult decisions typically involve mental effort, which scales with the deployment of cognitive (e.g., mnesic, attentional) resources engaged in processing decision-relevant information. But how does the brain regulate mental effort? A possibility is that the brain optimizes a resource allocation problem, whereby the amount of invested resources balances its expected cost (i.e. effort) and benefit. Our working assumption is that subjective decision confidence serves as the benefit term of the resource allocation problem, hence the “metacognitive” nature of decision control. Here, we present a computational model for the online metacognitive control of decisions or oMCD. Formally, oMCD is a Markov Decision Process that optimally solves the ensuing resource allocation problem under agnostic assumptions about the inner workings of the underlying decision system. We demonstrate how this makes oMCD a quasi-optimal control policy for a broad class of decision processes, including -but not limited to- progressive attribute integration. We disclose oMCD’s main properties (in terms of choice, confidence and response time), and show that they reproduce most established empirical results in the field of value-based decision making. Finally, we discuss the possible connections between oMCD and most prominent neurocognitive theories about decision control and mental effort regulation.

Development of Efficient Models of Artificial Intelligence for Autonomous Decision Making in Dynamic Information Systems

This research paper focuses on the development of efficient artificial intelligence models for autonomous decision-making in dynamic information systems. Using innovative approaches in data analysis and algorithm optimization, we explore ways to improve model performance in dynamic environments. The results of this research can provide a deeper understanding of how artificial intelligence can operate effectively in real time, thus opening up new perspectives for application in different industries. The research includes the implementation of advanced machine learning techniques, as well as the analysis of adaptive models that can adapt to changes in the environment. Key attention is devoted to the optimization of resources in order to ensure quick and precise decision-making in dynamic situations. In addition, the work addresses the integration of the model with high-performance sensors to improve the system's ability to gather relevant information for decision-making. Through this interdisciplinary analysis, we aim to contribute to the development of intelligent systems that can autonomously react to changes and unforeseen situations in real time.

Analisis Efektivitas Sistem Informasi Akuntansi pada Yayasan Global Excellence: Studi Kasus

Accounting Information System (AIS) is a key component within organizations that facilitates the collection, processing, and reporting of financial information. The effective performance of AIS is crucial for organizations to provide accurate and relevant information for decision-making. Yayasan Global Excellence, as a non-profit institution, relies on AIS to manage its finances effectively and meet reporting requirements. This research aims to analyze the effectiveness of implementing the accounting information system at Yayasan Global Excellence and identify constraints that hinder the system's performance in providing necessary information. The research method used is descriptive qualitative analysis. Data collection is conducted through observation of organizational structure, related documents, and financial reports generated by the system. Both primary and secondary data are used to gain a comprehensive understanding of the state of AIS at Yayasan Global Excellence. The analysis results indicate that AIS at Yayasan Global Excellence has not yet met the expected standards for an effective AIS. Several critical aspects such as human resources, technological infrastructure, documentation, procedures, and compliance with accounting standards are not fully met. Therefore, it can be concluded that AIS at Yayasan Global Excellence has not been operating effectively so far. Appropriate improvement measures are needed to enhance the quality of AIS to provide accurate, relevant, and reliable financial information for decision-makers at Yayasan Global Excellence.

Administrative Decentralization and the Role of Information: The Case of Intimate Partner Violence During the COVID-19 Pandemic

The U.S. federal government often devolves administrative processes and decision making to state and local governments. Prior studies have found that the success of decentralization and implementation depends on several mediating factors at the subnational level, such as a state's political ideology or administrative capacity. This study focuses on one mechanism—the ability of states to leverage their information advantage about the local context vis-à-vis the federal government. We are interested in whether the information advantage of state-level firearm background checks decreased the rates of intimate partner violence (IPV) involving a firearm compared to states that relied on federally administered background checks. We take advantage of data from the period of state-mandated stay-at-home (SAH) orders during the COVID-19 pandemic, a temporal context with increased IPV rates. Using a Poisson fixed effects regression, we find that rates of IPV involving a firearm did not increase when the SAH orders were in effect. However, using decision-relevant information in state-administered background checks decreased the rates of IPV resulting in injury and murder-suicide, compared to states that relied on federal background checks.

A decision-making system based on case-based reasoning for predicting stroke rehabilitation demands in heterogeneous information environment

Recently, the increasing number of stroke survivors has created a greater demand for rehabilitation services. How to predict rehabilitation demands is important for policy makers in various countries. The demand prediction is essentially a multi-attribute decision-making problem. The diverse forms of information or data are often neglected or simplified in previous studies of decision-making problems. In this paper, heterogeneous multi-attributes including crisp numbers, interval numbers, and linguistic variables are defined to represent decision-relevant information concerning the patient's condition in cases when applied to real-life scenarios. Moreover, a deviation minimization optimization model combining CRITIC method and H2TLWMSM operator is proposed to assign the attribute weights with dual information. The WHMACBR system can be used to predict stroke rehabilitation demands for making the diagnosis and treatment decisions. The proposed decision-making system based on the CBR approach simulates the operations of human memory and reasoning, thus assisting physicians in addressing the ever-increasing demand for rehabilitation services of stroke survivors. Finally, the effectiveness and applicability of the case retrieval process of the presented CBR model are demonstrated through a stroke rehabilitation case study (top 3 cases ranked by similarity to the target case are C6, C2, and C3) and comparison analysis with relevant existing CBR models. This indicates that the proposed decision-making system, by considering heterogeneous multi-attributes and their weights, is capable of providing more comprehensive and rational decision-related information in the field of rehabilitation medicine.

Motivational drivers of costly information search

Acquiring information that aids decision-making is subject to a trade-off of accuracy versus cost, given that time, effort, or money are required to obtain decision-relevant information. Three studies (N = 2010) investigated the motivational dynamics shaping the priorities that govern this trade-off. Motivational orientations related to both the decision-making process and its outcome were examined. Regulatory focus theory describes two broad orientations to goal pursuit: promotion focus, prioritizing eager achievement, versus prevention focus, prioritizing vigilant security. We hypothesized that when the framing of a decision-making task activates a prevention focus rather than a promotion focus, individuals would be more willing to assume the costs of acquiring additional information before making their decisions. To test this hypothesis, participants made incentivized decisions with the option of acquiring additional information before making a final decision; importantly, obtaining this information incurred financial costs. Results consistently confirmed that prevention-focused decision makers were indeed more willing to assume the costs of acquiring additional information than promotion-focused individuals. The first two studies involved a scenario where participants were indifferent to the specific outcome of the decision process; accuracy was their only concern. In the final study, searchable, accuracy-enhancing information was also related to decision makers' partisan political preferences. Regulatory focus and the preference for partisan-congenial information were observed to be co-occurring but functionally orthogonal drivers of costly information search. Thus, prevention-framed messages can motivate the search for decision-relevant information, even when this search is costly and could lead to disagreeable data.

THE ROLE OF VERBATIM VERSUS GIST FORMATTING IN AGE-RELATED DIFFERENCES IN INFORMATION AVOIDANCE

Abstract Older adults are more likely to underuse and actively avoid decision-relevant information. Although the mechanisms underlying age-related differences in information avoidance are still unknown, Fuzzy-Trace Theory (Reyna, 2012) suggests they may be due to age-related differences in information processing preferences: According to this framework, information is stored using both gist (subjective meaning) and verbatim representations (objective details/numbers). Older adults prefer gist information and gist-based information processing, whereas younger adults favor verbatim information and verbatim processing. To examine whether age-related differences in information avoidance are affected by age-related information and processing preferences, an adult lifespan sample (N = 432) completed a pre-registered online study. Participants responded to scenarios containing potentially aversive information and chose between accepting or avoiding the information. Information formatting was experimentally manipulated, with participants either being faced with receiving gist or verbatim-formatted information. Across age groups, participants were more likely to avoid verbatim numbers than qualitative gist information (p=.026); no associations were found between avoidance and information or processing preferences. At odds with prior research, age was associated with lower information avoidance (p=.010). Follow-up analyses revealed that older adults were less likely to avoid information in health-related contexts but not in contexts unrelated to health (e.g., consumer information; p=.010). Overall, participants were more likely to avoid non-health information than health information (p<.001). In sum, our findings revealed that older adults show a decreased likelihood of avoiding potentially aversive health information, but that age-related differences in information avoidance cannot be linked to age-related information or processing preferences.

Reducing Facial Stereotype Bias in Consequential Social Judgments: Intervention Success With White Male Faces

Initial impressions of others based on facial appearances are often inaccurate yet can lead to dire outcomes. Across four studies, adult participants underwent a counterstereotype training to reduce their reliance on facial appearance in consequential social judgments of White male faces. In Studies 1 and 2, trustworthiness and sentencing judgments among control participants predicted whether real-world inmates were sentenced to death versus life in prison, but these relationships were diminished among trained participants. In Study 3, a sequential priming paradigm demonstrated that the training was able to abolish the relationship between even automatically and implicitly perceived trustworthiness and the inmates’ life-or-death sentences. Study 4 extended these results to realistic decision-making, showing that training reduced the impact of facial trustworthiness on sentencing decisions even in the presence of decision-relevant information. Overall, our findings suggest that a counterstereotype intervention can mitigate the potentially harmful effects of relying on facial appearance in consequential social judgments.

Testing Housing Price Drivers in Santiago de Chile: A Hedonic Price Approach

Hedonic modelling is an empirical analysis technique that is widely used to review the housing market and unpack the main determinants of price. A correct determination of housing price drivers allows a weighted prediction of the value of each dwelling for sale or rent. This paper reviews the fundamental determinants of housing prices that have been published in the literature to see which ones have a better predictive fit for the case of the housing market in the city of Santiago, Chile. From a record of 456,000 property transactions, a dataset composed of 11 explanatory variables is elaborated. Through a semi-logarithmic regression, 4 variables that contribute to explaining the formation of housing prices in Santiago, Chile, are identified. The results indicate that the socioeconomic characterisation of urban areas where housing is traded, the price of copper on the London Metal Exchange, the mortgage interest rate, the age of residential buildings are the main drivers for the main drivers of prices in Santiago. Given the crisis of access to housing in Chile, the article provides relevant information for decision-makers in housing policy.

The Influence of Campaign Contribution Disclosure on Voter Support for Tax Initiatives: Evidence from Washington’s “Keep Groceries Affordable Act of 2018”

ABSTRACT We examine the effect of campaign financial disclosures on support for Washington State Initiative 1634, a ballot initiative aimed at limiting tax increases on soda. In an experimental setting, we assess the effect of financial disclosures when paired with the actual textual arguments provided to Washington State voters and the effect of financial disclosures when paired with more complementary textual arguments. We find evidence that financial disclosures influence changes in voter support for the tax initiative. Specifically, our findings indicate that disclosing the top financial contributors “for” and “against” the initiative provides decision-relevant information to voters. Thus, policymakers should consider (1) making donor disclosures freely available in voter information guides, (2) eliminating special-interest group techniques that hide the sources of contributions, and (3) erring on the side of caution when constituents seek to remove ballot-related financial reporting requirements.

The effect of conscientiousness on managerial learning from stock prices

ABSTRACT We examine whether and how CEOs’ conscientiousness affects managerial learning from stock prices. We use a deep learning-based natural language processing model to detect personality from textual records of an individual’s spoken language. Following personality psychologists’ description of the personality trait of conscientiousness, we hypothesize that conscientious CEOs are more willing and able to collect comprehensive decision-relevant information compared to others, and to the extent that they possess more relevant information prior to the arrival of news in the stock market, they have less to learn from it. From the sample of large U.S. public firms for which earnings call transcripts are available, we replicate the well-stylized investment-to-price relationship while reporting a weaker relationship for firms with conscientious CEOs. This finding supports the hypothesis that conscientious managers learn less from stock prices.

Analisis produktivitas pariwisata: Studi kasus pada beberapa negara berdasarkan perbedaan karakter wilayah

Tourism productivity has a role in driving economic growth, creating jobs, increasing people's income, and building tourism infrastructure. Measurement of tourism productivity is important because it provides the information needed to measure the performance of the tourism sector, plan sustainable development, identify growth opportunities and make smart investment decisions. The type of approach used is a descriptive qualitative approach, namely an approach that uses more systematic analysis based on assumptions and critical thinking. This approach is used to answer the research objective, namely to determine tourism productivity in a country. The type of data used is secondary data obtained from several relevant and valid scientific journals regarding tourism productivity that have been published previously. Not only scientific journals, this research also uses government publication data. The results of the research discussion aim to make readers understand and know tourism productivity in several countries and provide relevant information for decision makers in local governments and the tourism industry.

Assessing the utility of pregnant women as a sentinel surveillance population for malaria in Geita, Tanzania, 2019 - 2021

ObjectivesEstimates of malaria burden and intervention uptake in Africa are primarily based on household surveys. However, their expense and infrequency limit their utility. We investigated whether data collected during antenatal care (ANC) can provide relevant information for decision-makers. MethodsMalaria test positivity rates and questionnaire data from ANC attendees at 39 health facilities were compared to questionnaire data and positivity rates among children from two cross-sectional surveys in the facilities’ corresponding catchment areas. ResultsTrends in parasitemia among ANC attendees were predictive of trends in parasitemia among children at the council level (mean absolute error 6.0%). Primigravid ANC attendees had the lowest rates of net ownership (modeled odds ratio [OR] 0.28, 95% CI 0.19-0.40) and use (OR 0.58, 95% CI 0.42-0.79). ANC attendees reported higher levels of care-seeking (OR 1.78, 95% CI 1.48-2.14), malaria testing (OR 4.16, 95% CI 3.44-5.04), and treatment for children with fever (OR 7.66, 95% CI 4.89-11.98) compared to women surveyed in households, raising concerns about social desirability bias disproportionately impacting ANC surveys. ConclusionANC surveillance is an effective strategy for tracking trends in malaria burden. More work is required to elucidate the value of administering questionnaires to ANC attendees.

Data Mining Techniques in Decision Making

Data mining aims to find relevant information for decision-making, forecasting, optimising, and other business or research reasons. In this study, data mining is used in the domain of decision-making. Various problems for this domain exist in the literature, including selection problems, considering unnecessary attributes, and removing irrelevant attributes in the dataset by applying different preprocessing techniques. For this purpose, a smartphone dataset is used, and different machine learning classifiers are applied. Decision Tree, Naive Bayes, SMO, bagging, and Random Forest were chosen for precision, recall, and F-measure. Results demonstrate that Random Forest obtains 57% accuracy and performs better on average than the other algorithms. The class "Saving Account" was classified with 52% accuracy as of the other attributes, but it has the fewest errors depending on the attributes. This study can be extended by applying the proposed methodology to a rich volume dataset and deep-learning techniques.

Sustainability 4.0 in services: a systematic review of the literature

PurposeThis paper aimed to identify the impact that Industry 4.0 can have on the service sector and how its combination can generate benefits for Sustainability 4.0 in the context of the Triple Bottom Line.Design/methodology/approachA Systematic Literature Review (RSL) was developed using the following combinations of keywords on the Web of Science and Scopus databases: Industry 4.0 And Sustainability, Industry 4.0 And Service, and Industry 4.0 And Sustainable Development, in which 24 papers were selected.FindingsAs a result, the authors found 100 benefits for sustainable development, which were further categorized into 54 advantages for the economic area, 25 for the social segment and 21 for environmental aspects. In this way, the results can help private companies understand the use of these technologies aimed at sustainable growth, plus bolstering the government to conduct public policies to encourage these practices in technology organizations.Practical implicationsAs a practical implication, this study offers insight into the evolution of sustainable development. It enables the emergence of works that wish to explore the service sector, providing relevant information for decision-making and influencing managers and policymakers on the importance of applying Industry 4.0 toward the sustainability of the service sector and its applications.Originality/valueThe originality of this study lies in expanding the understanding generated by 4.0 technologies, by diverting attention from the manufacturing and agricultural sectors, which have abundant literature on the subject. With this, this work demonstrated that modern technologies have greater possibilities of an action directed at the service sector, in addition to being able to contribute to Sustainability 4.0 from the perspective of the sustainability tripod.

Evaluation of the implementation progress through key performance indicators in a new multimorbidity patient-centered care model in Chile

BackgroundComplex health interventions involve deep organizational, structural, and cultural changes that challenge health teams and decision-makers. The explosion of chronic diseases has made the multimorbidity approach a global priority. The Centro de Innovación en Salud ANCORA UC implemented a Multimorbidity Patient-Centered Care Model in the Chilean public health system.ObjectiveThis study aims to evaluate the progress of the implementation of the Multimorbidity Patient-Centered Care Model in seven primary care centers through key performance indicators.Methodsa set of indicators was designed to evaluate change management, operations, installation of new roles, and services and activities of the intervention strategy of the model. Key performance indicators were identified to monitor the implementation progress on minimal components for the model’s sustainability. Each item was assigned against an expected minimum score of 67% of progress from the overall score. They were monitored twice in seven primary health centers in 2019 and 2020, which intervened 22,642 patients with the intervention.ResultsThe results showed that six of the seven primary care centers reached the minimum implementation threshold. The main advances were in operational conditions, and those with minor progress in implementation were the clinical services. Population size, organization, coordination of the health care teams, additional training, and decision-makers support were key factors that determined the degree of progress in a complex intervention.ConclusionIt was possible to measure the progression of the implementation of a complex intervention through key performance indicators delivering relevant information for decision-makers that pursue a successful and faithful implementation. This study provides a valuable tool for the national scale-up of a similar model started in Chile by the Ministry of Health and other countries.

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.