| 환자이탈군 특성요인과 이탈환자 예측모형에 관한 연구 |
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| 민경진1, 송규문2, 김광환3 |
1계명대학교 공중보건학과
2계명대학교 통계학과
3단국대학교병원 의무기록과 |
| A Study on the Characteristics of Prematurely Discharged Patients and the Model for Predicting Premature Discharge |
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| Kyung-Jin Min1, Kyu-Moon Song2, Kwang-Hwan Kim3 |
1Department of Public Health, Keimyung University
2Department of Statistics, Keimyung University
3Dep. of Medical Record, Dankook University Hospital |
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| Abstract |
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. |
| Key words
Data Mining;Patient Discharge;UHDDS; |
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