• 대한영양사협회학술지
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• 제22권3호
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• pp.193-213
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• 2016

The purpose of this study is to provide a standard job description for a nutrition teacher placed in primary and secondary schools by analyzing their duties. DACUM is an acronym for 'Developing A CUrriculuM'. It was used by experts to determine the job duties, tasks and task elements to establish the job descriptions of a nutrition teachers through the development of a DACUM chart. An expert panel consisting of 10 nutrition teachers participated in a DACUM workshop and derived nutrition teacher's DACUM chart. A total of 1,550 nutrition teachers across the country were targeted as the survey subjects for validation of the DACUM chart through their perception of the frequency, importance, and difficulty of each item in the job description. A tree structure, criticality analysis, and contents validity index were added for verification. The definition of the nutrition teacher's job and DACUM chart with 5 duties, 28 tasks, and 107 task elements were derived by the DACUM method. The definition of a nutrition teacher was 'A teacher who is responsible for food service management and nutrition education and counseling for health promotion and disease prevention for students in primary and secondary schools'. The validation results of the tree structure were the priorities of the 28 tasks with ranks 1 and 2. Because there was no third priority, it was considered to be a good representation of the tasks of nutrition teachers. The DACUM chart was found to be evenly distributed with relative importances of more than 17 or less than 11 through the criticality analysis. Since the C5-4 (Conduct simulation exercise against food poisoning)'s content validity index was significantly less than the reference value of 0.78, it was not included in the final job description. 5 duties, 28 tasks, 102 task elements were included in the final job description of a nutrition teacher.

• Journal of Preventive Medicine and Public Health
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• 제45권4호
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• pp.259-266
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• 2012

Objectives: This study aims to determine the risk factors predicting rehospitalization by comparing three models and selecting the most successful model. Methods: In order to predict the risk of rehospitalization within 28 days after discharge, 11 951 inpatients were recruited into this study between January and December 2009. Predictive models were constructed with three methods, logistic regression analysis, a decision tree, and a neural network, and the models were compared and evaluated in light of their misclassification rate, root asymptotic standard error, lift chart, and receiver operating characteristic curve. Results: The decision tree was selected as the final model. The risk of rehospitalization was higher when the length of stay (LOS) was less than 2 days, route of admission was through the out-patient department (OPD), medical department was in internal medicine, 10th revision of the International Classification of Diseases code was neoplasm, LOS was relatively shorter, and the frequency of OPD visit was greater. Conclusions: When a patient is to be discharged within 2 days, the appropriateness of discharge should be considered, with special concern of undiscovered complications and co-morbidities. In particular, if the patient is admitted through the OPD, any suspected disease should be appropriately examined and prompt outcomes of tests should be secured. Moreover, for patients of internal medicine practitioners, co-morbidity and complications caused by chronic illness should be given greater attention.

• 한국의료질향상학회지
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• 제8권2호
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• pp.186-207
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• 2001

Background : This study presented an analysis of healthcare quality indicators using data mining and a development of decision support system for quality improvement. Method : Specifically, important factors influencing the key quality indicators were identified using a decision tree method for data mining based on 8,405 patients who discharged from a medical center during the period between December 1, 2000 and January 31, 2001. In addition, a decision support system was developed to analyze and monitor trends of these quality indicators using a Visual Basic 6.0. Guidelines and tutorial for quality improvement activities were also included in the system. Result : Among 12 selected quality indicators, decision tree analysis was performed for 3 indicators ; unscheduled readmission due to the same or related condition, unscheduled return to intensive care unit, and inpatient mortality which have a volume bigger than 100 cases during the period. The optimum range of target group in healthcare quality indicators were identified from the gain chart. Important influencing factors for these 3 indicators were: diagnosis, attribute of the disease, and age of the patient in unscheduled returns to ICU group ; and length of stay, diagnosis, and belonging department in inpatient mortality group. Conclusion : We developed a decision support system through analysis of healthcare quality indicators and data mining technique which can be effectively implemented for utilization review and quality management in a healthcare organization. In the future, further number of quality indicators should be developed to effectively support a hospital-wide Continuous Quality Improvement activity. Through these endevours, a decision support system can be developed and the newly developed decision support system should be well integrated with the hospital Order Communication System to support concurrent review, utilization review, quality and risk management.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.352-357
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• 2001

This study presents an analysis of healthcare quality indicators using data mining for developing quality improvement strategies. Specifically, important factors influencing the inpatient mortality were identified using a decision tree method for data mining based on 8,405 patients who were discharged from the study hospital during the period of December 1, 2000 and January 31, 2001. Important factors for the inpatient mortality were length of stay, disease classes, discharge departments, and age groups. The optimum range of target group in inpatient healthcare quality indicators were identified from the gains chart. In addition, a decision support system was developed to analyze and monitor trends of quality indicators using Visual Basic 6.0. Guidelines and tutorial for quality improvement activities were also included in the system. In the future, other quality indicators should be analyze to effectively support a hospital-wide continuous quality improvement (CQI) activity and the decision support system should be well integrated with the hospital OCS (Order Communication System) to support concurrent review.

• 한국전통조경학회지
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• 제38권2호
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• pp.95-107
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• 2020

본 연구는 양동마을에 분포하는 노거수를 생육실태 및 분석하여 기초자료로 제공하기 위한 목적이 있다. 조사항목은 수목정보, 토양정보, 수목건강도에 대해 조사하였다. 연구결과를 요약하면 다음과 같다. 양동마을 내 수목정보는 향나무, 왕버들, 능수버들, 팽나무, 느티나무, 주엽나무, 조각자나무로 총 7종 30주이며, 수고는 4.0~17.0m, 흉고직경은 0.51~1.34m로 17번 향나무가 가장 넓었다. 토양분석 결과로는 산도 pH4.1~6.3, 경도 5~48mm, 유기물함량 21.2~29.1g/kg, 전기전도도 0.34~1.76dS/m, 유효인산 79.8~451.6mg/kg, 치환성 칼륨 0.22~1.71cmol+/kg, 치환성 칼슘 4.98~7.44cmol+/kg, 치환성 마그네슘 0.67~2.19cmol+/kg, 치환성 나트륨 0.19~1.04cm ol+/kg, 양이온치환용량 7.23~13.02cmol+/kg으로 나타났다. 이에 따라 수목건강도의 건강수치 중 가장 높은 노거수는 팽나무 11주 중 8주, 느티나무 7주 중 2주, 조각자나무 3주로 전체 30주 중 13주이며, 감염과 부패 및 공동수치가 높은 노거수는 건강수치를 제외한 나머지 수목으로 나타났다. 상대적으로 대상 노거수의 절반이상이 감염·부패 및 공동부위로 차지하고 있는 실태로, 손상이 부위는 외과수술을 진행할 필요가 있다. 또한 감염부위가 건강부위로 전이가 되는 2·3차 피해가 발생하지 않도록 수세회복을 위한 뿌리영양공급 및 체내 양분조절을 통해 보존보호조치가 시행되어야 하며, 지속적인 수목 모니터링을 노거수 입지환경개선 및 관리방안이 모색되어야 할 것이다.