OBJECTIVES: Fuzzy cognitive maps (FCMs) representing causal knowledge of relationships between medical concepts have been used as prediction tools for clinical decision making. Activation functions used for inferences of FCMs are very important factors in helping physicians make correct decision. Therefore, in order to increase the visibility of inference results, we propose a method for designing certain types of activation functions by considering the characteristics of FCMs. METHODS: The activation functions, such as the sinusoidal-type function and linear function, are designed by calculating the domain range of the functions to be reached during the inference process of FCMs. Moreover, the designed activation functions were applied to the decision making process with the inference of an FCM model representing the causal knowledge of pulmonary infections. RESULTS: Even though sinusoidal-type functions oscillate and linear functions monotonously increase within the entire range of the domain, the designed activation functions make the inference stable because the proposed method notices where the function is used in the inference. And, the designed functions provide more visible numeric results than do other functions. CONCLUSIONS: Comparing inference results derived using activation functions designed with the proposed method and results derived using activation functions designed with the existing method, we confirmed that the proposed method could be more appropriately used for designing activation functions for the inference process of an FCM for clinical decision making.
Artificial Intelligence ; Decision Making

OBJECTIVES: Fuzzy cognitive maps (FCMs) representing causal knowledge of relationships between medical concepts have been used as prediction tools for clinical decision making. Activation functions used for inferences of FCMs are very important factors in helping physicians make correct decision. Therefore, in order to increase the visibility of inference results, we propose a method for designing certain types of activation functions by considering the characteristics of FCMs. METHODS: The activation functions, such as the sinusoidal-type function and linear function, are designed by calculating the domain range of the functions to be reached during the inference process of FCMs. Moreover, the designed activation functions were applied to the decision making process with the inference of an FCM model representing the causal knowledge of pulmonary infections. RESULTS: Even though sinusoidal-type functions oscillate and linear functions monotonously increase within the entire range of the domain, the designed activation functions make the inference stable because the proposed method notices where the function is used in the inference. And, the designed functions provide more visible numeric results than do other functions. CONCLUSIONS: Comparing inference results derived using activation functions designed with the proposed method and results derived using activation functions designed with the existing method, we confirmed that the proposed method could be more appropriately used for designing activation functions for the inference process of an FCM for clinical decision making.
Artificial Intelligence ; Decision Making

PURPOSE: The purpose of this study was to explore clinical nurse's reported conflict experience toward end-of-life medical decision making. METHODS: Data were collected by in-depth interviews with eight nurses from three different wards of university hospital in D city of Korea. Conventional qualitative analysis was used to analyze the data. RESULTS: Results were three major themes and twelve categories from the analysis. The three major themes were prioritization of treatment, non-disclosure of diagnosis, and hierarchical and power relations. CONCLUSION: The results of this study suggest that shared decision making in end of life among patient, family members, physician, and nurse may contribute to improve end-of-life care performance as well as dignified dying of patient in end of life.
Decision Making ; Humans ; Korea

OBJECTIVES: This study was intended to investigate the differences of job stressors between managers and low level employees among white-collar workers. Another objective of this study was to examine whether the effects of job stressors on mental health differ between the two groups. METHODS: Data was obtained from 204 managers and 251 low level employees who were employed in white-collar jobs. Fourteen job stressors and seven job stress variables were measured. RESULTS: Among the 14 job stressors, role overload, job insecurity, and work-family conflict were higher job stressors for the manager group whereas role conflict, work-aptitude incongruity, participation in decision making, and promotion problems were higher job stressors for the low level of employees. There were no differences in job stress scores between the two groups. However, differences in the effects of job stressors on job stress were found between the two groups. For the manager group, job insecurity, work-aptitude incongruity, and work-family conflict significantly affected in explanation of job stress whereas for the low level employees, role underload, peer satisfaction, and environmental problems significantly explaining the job stress variables. CONCLUSIONS: There were significant differences in job stressors between managers and low level employees among white-collar workers. Additionally there were differences in the effects of job stressors on job stress between the two groups.
Decision Making ; Mental Health

Humans ; Decision Making ; Surgeons

Most patients want to play an active role in their own health care. There is now a movement from medical paternalism to patient-centered care in the consultation process that is based on the therapeutic alliance and negotiation between the doctor and patient, aptly named “shared decision-making” (SDM). It is a process where doctors work together with patients, including their families and caregivers, to select tests, treatments, management, or support packages, based on clinical evidence and personal informed preferences, health beliefs, and values. Successful implementation of SDM is associated with improved quality of consultations, favorable patient-reported health outcomes, and increased patient and doctor satisfaction. Patients are empowered to make proactive health decisions resulting in decreased anxiety, faster recovery, increased treatment compliance, and reduced unnecessary health care expenditure. There are multiple existing models in facilitating SDM. Two simple and easyto-follow models are the “three-talk model” and “S.H.A.R.E. approach.” The three-talk model endorsed by the NICE divides the SDM consultation into three steps, namely: team talk (explaining the need to consider treatment options as a team), option talk (describing the alternatives in more detail, and making use of patient decision aids [PDA] whenever appropriate), and decision talk (helping patients explore and form their personal preferences). On the other hand, the S.H.A.R.E. approach promoted by the Agency for Healthcare Research and Quality (AHRQ) is a five-step SDM consultation process that includes exploring and comparing the benefits, harms, and risks of each treatment option through meaningful dialogue about what matters most to patients.
Decision Making, Shared

The purpose of this paper is to produce a policy decision making model most appropriate for formulating mental health policy in Korea. This research will also illuminate the legislation process and make accurate predictions about the revision process of the Mental Health Act. Using the Allison Models, we analyzed the legislation process of for the Korean Mental Health Act from the 1980s to 1995, which was largely divided into two periods. We applied the three types of model to each of these two periods. The results of the comparative analysis show that the process of the Mental Health Act enactment can be explained through by each of the three types of Allison models and that there is no dominant model. However, the analysis shows that, compared with the 'Rational Actor' model, the 'Organizational Behavior' model and the 'Governmental Politics' model are better able to explain the decision making process compared to the 'Rational Actor' model.
Decision Making ; Korea ; Mental Health ; Policy Making

Checklist ; Clinical Decision-Making ; Decision Making ; Humans ; Metacognition

China ; Decision Making ; Decision Making, Shared ; Humans ; Physician-Patient Relations

BACKGROUND: Statistical type II error has seemed to be ignored commonly by medical researchers. To control and present a power value could be helpful to reduce this type of error and to improve a quality of scientific decision making. We performed the post-hoc survey of the power of the negative results in Korean Journal of Anesthesiology (KJA). METHODS: One Hundred nineteen articles with negative results published in KJA during a year of 1997 were selected. We collected the numbers of the sample size and calculated the power of the given negative result only when applicable. And each author's attitude to negative results was taken by arbitrary criteria. RESULTS: Median sample size of these negative results was 16 12 (median interquartile range). We can calculate the power only in 43 articles of 119 negative results. Median power is 18.0% (interquartile range 26.0). In thirty six articles (83.8% of 43) the powers are proved to be under 80.0%. And 22 articles (51.2% of 43) have the powers even under 20.0%. We couldn't find any author who included either power or effect size in the article, and there was only one article in which its authors considered their inadequate number of sample size. CONCLUSIONS: We conclude that authors of KJA tend to ignore statistical type II error. In 119 negative results published in KJA during 1997, the calculated powers were very low and were not reported in the text.
Anesthesiology* ; Decision Making ; Sample Size