• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.11
• /
• pp.3480-3486
• /
• 2009

This research was based on the purpose of establishing predicted model of prematurely discharged patients using the mandatory information data, recorded in the medical institutes based on discharged patients of a University Hospital for the period of 1 year, from July. The result showed that the regression analysis model was the most excellent method of application model for preventing discharged patients, and when this is applied to discharged patients who are outpatients, the possibility of discharge can be less than staying in the emergency room. In addition, based on threshold 0.7, when we expect the discharged patients, out of 920 discharged patients, the actual patients who are discharged can become 136, showing the extract effectiveness of 14.78%. Based on the perspective of lift value, compared to random extract, this is 2.9 times (14.78/5.15) more effective.

• Quality Improvement in Health Care
• /
• v.9 no.1
• /
• pp.18-32
• /
• 2002

Background : We developed a model for predicting premature discharge and identifying related factors. Methods : Prediction model was developed by data mining techniques. Basic data were collected from the total discharge data base of a university hospital in Chungnam Province during the period from July 1, 1999 to June 30, 2000. Results : 1. Among 22,873 patients, the number of patients discharged with usual discharge orders were 21,695 or 94.8%. The number of the prematurely discharged patients were 1,178 or 5.2%. 2. The primary reason for unusual discharge was transfer to other hospital. Move to a local hospital closer to their home and burdensome medical expenses were main reasons. 3. Predictability of each model was tested using the top 10 percent of patients with the highest probabilities of premature discharge. The neural network model was chosen as the most appropriate model for predicting prematurely discharged patients. 4. Ten percent of the total number of patients had been selected randomly to test the effectiveness of the neural network model. We have chosen the threshold of the neural network model as 0.7. The number of patients who were expected to discharge prematurely was 312. Among them, 241 had been discharged prematurely (77.2%). Conclusion : Of the several data mining techniques used, the neural network model was the most effective, It can be used to identify and manage the patients who are expected to discharge prematurely.

• Journal of Preventive Medicine and Public Health
• /
• v.15 no.1
• /
• pp.139-143
• /
• 1982

A study was carried out in 81 retreatment patients with sputum positive pulmonary tuberculosis who had previously been treated with first-line drugs for more than one year at Health Center. The Following results were obtained; 1. Of the total 81 case of retreatment patients, male patients occupied 63(77.8%) and 18(22.2%) were female. Age group of $30{\sim}49$ years was 54.3% of total cases. 2. By extent of disease, moderate advanced cases were 53.1% and far advanced cases were 35.8%. 3. Of 81 patients admitted to the study. 65(80.3%) completed 1 year treatment 16(19.7%) patients discharged prematurely before 1 year. 4 patients terminated their treatment during $9{\sim}11$ months after registration. 4. Completment rate of chemotherapy was highest (90%) at age of under 30 years. 5. Intractable patients with persistant positive sputum test for A.F.B. even after 12 months of retreatment were occupied 9(13.8%) of total retreatment cases.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.6
• /
• pp.2819-2827
• /
• 2013

The purpose of his study was to analyze the environmental and the medical factors of prematurely discharged patients in a rehabilitation hospital. The subjects were 107 inpatients. The data were collected using self-report questionnaire and analyzed using the SPSS Win 19.0 program. In result, Firstly, general selection of hospitals was the 'Recommendation' 35.5%, Environmental dissatisfaction factors are Hospital facilities 37.4%. Hospital choice is the highest 'Acquaintances' 23.4%. Second, the lowest group about professionalism, kindness, and description of explanation on satisfaction in survey is group of care workers for the sick. The highest group is physical and occupational therapist. Third, the satisfaction regarding medical procedures of administrative work, waiting time and medical expenses is the highest normal. Hospital facilities in one of the highest factors in environmental dissatisfaction is can be considered as an element of patient departure because of not much number of nervous special hospitals. Medical dissatisfaction factors is the lowest satisfaction of care worker even if those spend much time. this is the focus leaving hospital. Therefore, factors care workers on service satisfaction and dissatisfaction in analysis and problem solving is considered that the need to find ways to improve the quality of service care workers.