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

No abstract available.
Decision Support Techniques* ; Stroke*

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

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

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

Humans ; Decision Making ; Surgeons

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

The clinical decision analysis (CDA) has used to overcome complexity and uncertainty in medical problems. The CDA is a tool allowing decision-makers to apply evidence-based medicine to make objective clinical decisions when faced with complex situations. The usefulness and limitation including six steps in conducting CDA were reviewed. The application of CDA results should be done under shared decision with patients' value.
Decision Support Techniques* ; Decision Trees* ; Evidence-Based Medicine ; Uncertainty

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