• Journal of Korean Academy of Nursing
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• v.31 no.2
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• pp.328-339
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• 2001

The purpose of this study was to describe decision making model of 180 public health nurses in Korea and their knowledge structure for decision making. The differences of decision making models by nurse's knowledge structure were also tested. Research concepts were measured using the instrument based on systemic and interpretive decision making approaches that were developed by Lauri & Salantera (1995). The results were as follows. 1. The public health nurses turned to, most commonly, a mixed practical-theoretical knowledge structure (45.9%), followed by practical knowledge (32%) and theoretical knowledge (22.1%). 2. The six different decision making models were identified. These were named for decision making theories and nursing process. These were client-oriented decision making, rule-oriented systemic decision making, wholistic and intuitive decision making, decision making depending on subjective view and experience, systemic decision making for defining problems. 3. The public nurses who had practical and practical-theoretical knowledge structure and community health practitioner (CHP) retold that decision making depends on subjective view and experience. Also the public health nurses who had 5~19 years clinical experience represented hypothetico-deductive decision making for defining problems.

• Health Policy and Management
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• v.10 no.4
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• pp.99-115
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• 2000

This study was conducted to get a resonable set of budget allocation to public health programs. Matrix Delphi technique was used to obtain the logic of study results and eventually to form a human model which could predict opinion of professionals on budget allocation. Thirty-two professionals in academic and governmental area responded to Delphi survey. Questionnaire was developed using matrix formation, and the matrix was formed by 6 decision criteria on budget allocation and 26 public health programs. The decision criteria are as following: size of problem(morbidity), severity of problem, social equity, importance of prevention, technical feasibility and efficiency of programs. Severity of problem dropped out of the model because it had significant correlation with the size of problem. A total score of each program was obtained by weighting the relative importance of each criteria which also were given by survey respondents. These total scores indicate that the most important public health program is vaccination for infants and children in terms of budget allocation. Monitoring communicable diseases, mental health program, and anti-smoking program are the next. In addition, respondents were asked of the desirable budget size of each program. The result was rearranged by multiple regression model using the scores of each decision criteria. In this process, the current budget size of central government was provided to the respondents, and included in the model. h set of desirable budgets modified using tile model was obtained. Considering the current size of budget, tile results of the model is very different from that of the total score. Managing dementia is ranked the first. Health promotion program for the elderly, rehabilitation of the disabled and monitoring communicable diseases are the next. The need to increase the budget of vaccination for the infants and children was not found as so high. The matrix structure in Delphi survey gave us the precise basis to make optimal decision, and made it possible to develop an opinion predicting model. However the plentifulness and diversity of professional opinions were not fully obtained due to the limited number of decision criteria.

• Health Policy and Management
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• v.24 no.3
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• pp.205-218
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• 2014

Background: Most studies on the national health insurance benefit expansion policy have focused on policy tools or decision-making process. Hence there was not enough understanding on how policies are actually implemented within the specific policy context in Korea which has a national mandatory health insurance system with a dominant proportion of private providers. The main objectives of this study is to understand the implementation process of the benefit coverage expansion policy. Unlike other implementation studies, we tried to examine both the process of implementation and decision making and how they interact with each other. Methods: Interviews were conducted with the ex-members of the Health Insurance Policy Review Committee. Medical doctors who implement the policy at the 'street-level' were also interviewed. To figure out major variables and the degree of their influences, the data were analyzed with Winter's Policy Implementation Model which integrates the decision making and implementation phases. Results: As predicted by the Winter model, problems in the decision making phase, such as conflicts among the members of committee, lack of applicable causal theories application of highly symbolic activities, and limited attention of citizen to the issue are key variables that cause the 'implementation failure.' In the implementation phase, hospitals' own financial interests and practitioners' dependence on the hospitals' guidance were barriers to meeting the policy goals of providing a better coverage for patients. Patients, the target group, tend to prefer physicians who prescribe more treatment and medicine. To note, 'fixers' who can link and fill the gap between the decision-makers and implementers were not present. Conclusion: For achieving the policy goal of providing a better and more coverage to patients, the critical roles of medical providers as street-level implementers should be noted. Also decision making process of benefit package expansion policy should incorporate its influence on the implementation phase.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.429-436
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• 2013

Context aware environment u-health service is to provide health service with recognition of a computer. The computer recognizes that a patient can contact real life in many context. Context aware environment service for recommend have to definition of context data and service recommendations related to factors shall be identified. In this paper, Context aware environment of u-health service will be provide context data related to identifies recommendations factors using multivariate analysis method and recommendations factors creation to decision tree, association rule based decision model. health service recommend for significantly context data can be distinguish through recommendation factors of identify. Also, context data of patient can know preference factors through preference decision model.

• Journal of Society of Preventive Korean Medicine
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• v.24 no.2
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• pp.83-94
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• 2020

Backgrounds : To reduce the patients' economic burden of herbal decoctions use, in 2012, Korean government decided to implement the pilot project of herbal decoctions coverage in the National Health Insurance. Objectives : This study aimed to analyze the policy decision-making process for the pilot insurance project in 2012. Methods : Official documents, research papers, statistical reports, and news articles, etc. on the coverage of herbal decoctions were searched and collected. We used the Kingdon's Policy Stream Model to analyze how the policy of pilot project of herbal decoctions coverage was decided, and who were the main activists for the decision-making process. Results : Components to be included in the 'Problem stream' were the decline in the profits of Korean Medicine institutions, the contraction of the herbal decoctions use, and the fiscal surplus of National Health Insurance. In the 'Policy stream', there were several model studies for herbal decoctions coverage, and examples of herbal benefits in other social insurances. In the 'Political stream', there were the legislative initiatives by member of the National Assembly and the promotion of insurance coverage by the Association of Korean Medicine(AKOM), etc. Policy window for herbal decoctions coverage was opened by the combination of these three streams with the efforts of policy activists, such as the executives of AKOM, and policy researchers. Conclusions : The policy decision process for health insurance coverage of herbal decoctions was analyzed using Kingdon's model, and the analysis shows that the combination of political streams and entrepreneurs' competencies can be an important driving force in policy decision making.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• Journal of Korean Society for Quality Management
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• v.46 no.4
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• pp.1029-1042
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• 2018

Purpose: The purpose of this study is to explore the health care decision making of Ethiopian women at household level. Moreover it is to understand the factors that influence to potential customers in healthcare industry from the social quality level perspective. Methods: We used Ethiopia Demographic Health Survey (EDHS) 2005 & 2016, which provided data about currently married women aged 15-49 years (N=2003, N=2017, respectively). We performed a chi-square test, and a Pearson correlation and a logistic regression. Andersen model is considered as well. Results: This study revealed that the mobility decision making has an association with health care decision making of women. Furthermore, there is a moderate effect of an economic decision making of women. Lastly, the women's decision making empowerment level increase year by year. Conclusion: Health care industry has to consider potential costumers among women like in Ethiopia, whose decision making empowerment will enhance on their own healthcare in future. It is very important to figure out factors from the social quality management domain. It helps finding a new market from downstream approach. From this point, the impact of decision making of women empowerment has a significant implication from the holistic perspective.

• Safety and Health at Work
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• v.1 no.2
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• pp.140-148
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• 2010

Objectives: Determining the work-relatedness of lung cancer developed through occupational exposures is very difficult. Aims of the present study are to develop a decision tree of occupational lung cancer. Methods: 153 cases of lung cancer surveyed by the Occupational Safety and Health Research Institute (OSHRI) from 1992-2007 were included. The target variable was whether the case was approved as work-related lung cancer, and independent variables were age, sex, pack-years of smoking, histological type, type of industry, latency, working period and exposure material in the workplace. The Classification and Regression Test (CART) model was used in searching for predictors of occupational lung cancer. Results: In the CART model, the best predictor was exposure to known lung carcinogens. The second best predictor was 8.6 years or higher latency and the third best predictor was smoking history of less than 11.25 pack-years. The CART model must be used sparingly in deciding the work-relatedness of lung cancer because it is not absolute. Conclusion: We found that exposure to lung carcinogens, latency and smoking history were predictive factors of approval for occupational lung cancer. Further studies for work-relatedness of occupational disease are needed.

• Journal of Korean Academy of Nursing
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• v.36 no.4
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• pp.662-670
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• 2006

Purpose. This study was performed to develop and test a decision-tree model of treatment-seeking behaviors about when Korean patients visit a doctor after experiencing stroke symptoms. Methods. The study used methodological triangulation. The model was developed based on qualitative data collected from in-depth interviews with 18 stroke patients. The model was tested using quantitative data collected from interviews and a structured questionnaire involving 150 stroke patients. The predictability of the decision-tree model was quantified as the proportion of participants who followed the pathway predicted by the model. Results. Decision outcomes of the model were categorized into immediate and delayed treatment-seeking behavior. The model was influenced by lowered consciousness, social-group influences, perceived seriousness of symptoms, past history of hypertension or stroke, and barriers to hospital visits. The predictability of the model was found to be 90.7%. Conclusions. The results from this study can help healthcare personnel understand the education needs of stroke patients regarding treatment-seeking behaviors, and hence aid in the development of educational strategies for stroke patients.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.639-647
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• 2008

This study used the characteristics of the knowledge discovery and data mining algorithms to develop tailored hypertension follow up management model - hypertension care predictive model and hypertension care compliance segmentation model - for hypertension management using the Korea National Health Insurance Corporation database(the insureds’ screening and health care benefit data). This study validated the predictive power of data mining algorithms by comparing the performance of logistic regression, decision tree, and ensemble technique. On the basis of internal and external validation, it was found that the model performance of logistic regression method was the best among the above three techniques on hypertension care predictive model and hypertension care compliance segmentation model was developed by Decision tree analysis. This study produced several factors affecting the outbreak of hypertension using screening. It is considered to be a contributing factor towards the nation’s building of a Hypertension follow up Management System in the near future by bringing forth representative results on the rise and care of hypertension.