Dataset Information

Decision Maker Profiling Using Their Mental Behavior Pattern.

ABSTRACT: This study describes a method to assist the task of predicting the result of the decision-making process of an individual based on psychological and emotional aspects and using artificial intelligence (AI) techniques. This study presents indicators created for profile identification, which are organized in primary and circumstantial categories. These indicators are merged according to the ultimate purpose of profile identification, including the expected behavioral pattern for a person who performs a decision-making process. The person behavior hypothesis was successfully tested and can be approximated by an indicator such as mental functioning pattern, and the mental functioning pattern hypothesis can signal the most likely decisions of an individual. Four debtor decision variables were assessed in a debt negotiation process, in order to validate the method, which is applicable to other decision-making domains. The best signaling of the most likely decision of the debtor was seven times greater than that of a random prediction, while the gain of the worst decision signaling variable was 20%.

SUBMITTER: de Mello FL

PROVIDER: S-EPMC8416518 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Decision Maker Profiling Using Their Mental Behavior Pattern.

de Mello Flávio Luis FL de Souza Sebastião Alves SA

Frontiers in psychology 20210818

This study describes a method to assist the task of predicting the result of the decision-making process of an individual based on psychological and emotional aspects and using artificial intelligence (AI) techniques. This study presents indicators created for profile identification, which are organized in primary and circumstantial categories. These indicators are merged according to the ultimate purpose of profile identification, including the expected behavioral pattern for a person who perfo ...[more]

PMID: 34489788

Similar Datasets

Project description:Underuse and overuse of diagnostic tests have important implications for health outcomes and costs. Decision support technology purports to optimize the use of diagnostic tests in clinical practice. The objective of this review was to assess whether computerized clinical decision support systems (CCDSSs) are effective at improving ordering of tests for diagnosis, monitoring of disease, or monitoring of treatment. The outcome of interest was effect on the diagnostic test-ordering behavior of practitioners.We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for eligible articles published up to January 2010. We included randomized controlled trials comparing the use of CCDSSs to usual practice or non-CCDSS controls in clinical care settings. Trials were eligible if at least one component of the CCDSS gave suggestions for ordering or performing a diagnostic procedure. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of test ordering outcomes.Thirty-five studies were identified, with significantly higher methodological quality in those published after the year 2000 (p = 0.002). Thirty-three trials reported evaluable data on diagnostic test ordering, and 55% (18/33) of CCDSSs improved testing behavior overall, including 83% (5/6) for diagnosis, 63% (5/8) for treatment monitoring, 35% (6/17) for disease monitoring, and 100% (3/3) for other purposes. Four of the systems explicitly attempted to reduce test ordering rates and all succeeded. Factors of particular interest to decision makers include costs, user satisfaction, and impact on workflow but were rarely investigated or reported.Some CCDSSs can modify practitioner test-ordering behavior. To better inform development and implementation efforts, studies should describe in more detail potentially important factors such as system design, user interface, local context, implementation strategy, and evaluate impact on user satisfaction and workflow, costs, and unintended consequences.

Project description:BackgroundAlmost 20 million children under one year of age did not receive basic vaccines in 2019, and most of these children lived in low- and middle-income countries. Implementation research has been recognized as an emerging area that is critical to strengthen the implementation of interventions proven to be effective. As a component of strengthening implementation, WHO has called for greater embedding of research within decision-making processes. One strategy to facilitate the embedding of research is to engage decision-makers as Principal Investigators of the research. Since 2015, the Alliance for Health Policy and Systems Research within the WHO and the United Nations Children's Fund have supported decision-maker led research by partnering with Gavi, the Vaccine Alliance, in an initiative called "Decision-Maker Led Implementation Research". This synthesis paper describes the cross-cutting lessons from the initiative to further understand and develop future use of the decision-maker led strategy.MethodsThis study used qualitative methods of data collection, including a document review and in-depth interviews with decision-makers and researchers engaged in the initiative. Document extraction and thematic content analysis were applied. The individual project was the unit of analysis and the results were summarized across projects.ResultsResearch teams from 11 of the 14 projects participated in this study, for an overall response rate of 78.6%. Most projects were carried out in countries in Africa and conducted at the sub-state or sub-district level. Seven enablers and five barriers to the process of conducting the studies or bringing about changes were identified. Key enablers were the relevance, acceptability, and integration of the research, while key barriers included unclear results, limited planning and support, and the limited role of a single study in informing changes to strengthen implementation.ConclusionsDecision-maker led research is a promising strategy to facilitate the embedding of research into decision-making processes and contribute to greater use of research to strengthen implementation of proven-effective interventions, such as immunization. We identified several lessons for consideration in the future design and use of the decision-maker led strategy.

Project description:BackgroundThe use of computerized clinical decision support systems (CCDSSs) may improve chronic disease management, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification. The objective of this review was to determine if CCDSSs improve the processes of chronic care (such as diagnosis, treatment, and monitoring of disease) and associated patient outcomes (such as effects on biomarkers and clinical exacerbations).MethodsWe conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for potentially eligible articles published up to January 2010. We included randomized controlled trials that compared the use of CCDSSs to usual practice or non-CCDSS controls. Trials were eligible if at least one component of the CCDSS was designed to support chronic disease management. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of relevant outcomes.ResultsOf 55 included trials, 87% (n = 48) measured system impact on the process of care and 52% (n = 25) of those demonstrated statistically significant improvements. Sixty-five percent (36/55) of trials measured impact on, typically, non-major (surrogate) patient outcomes, and 31% (n = 11) of those demonstrated benefits. Factors of interest to decision makers, such as cost, user satisfaction, system interface and feature sets, unique design and deployment characteristics, and effects on user workflow were rarely investigated or reported.ConclusionsA small majority (just over half) of CCDSSs improved care processes in chronic disease management and some improved patient health. Policy makers, healthcare administrators, and practitioners should be aware that the evidence of CCDSS effectiveness is limited, especially with respect to the small number and size of studies measuring patient outcomes.

Dataset Information

Decision Maker Profiling Using Their Mental Behavior Pattern.

Publications

Decision Maker Profiling Using Their Mental Behavior Pattern.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets