A Survey on Laboratory Biosafety Status of Public Healthcare Centers in Korea

The purpose of this study was to explore the laboratory biosafety status of Public Health Centers (PHCs) in Korea during Oct.7~26, 2012. We surveyed the environment of biosafety management, especially for the recognition level for biosafety of workers in the organizations. The questionnaires given out to 98 workers who are working for PHCs are to research the recognition level of workers for the knowledge of biosafety, related laws and regulations. The level was the highest in the Research Institute of the Public Health & Environment (RIPHE) followed by quarantine station, and the health center was assessed as the last. It was turned out that the biosafety educational program in the RIPHE was implemented on a regular basis (65.2%) with irregular cases (21.7%), and some outsourcing chances (8.7%). However, quarantine stations and health centers didn't practice actively biosafety training programs compared to RIPHE. In addition, there was a majority of opinions that the most important thing to improve biosafety level of PHCs is to strengthen current poor training and education system. In conclusion, it is necessary to develop more improved training system for biosafety on exposure risks including injuries, personal protective equipment, and chemical hazards.

Classification and Sequential Pattern Analysis for Improving Managerial Efficiency and Providing Better Medical Service in Public Healthcare Centers / 대한의료정보학회지

OBJECTIVES: This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center? 2) Can we anticipate diseases of patients who revisit the center? METHODS: For the first question, we applied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naive Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. RESULTS: We determined: 1) In general, the most influential variables which impact whether a patient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. 2) The best plain classification model is dependent on the dataset. 3) Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. CONCLUSIONS: Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers.