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A Study on the Development of Readmission Predictive Model

재입원 예측 모형 개발에 관한 연구

  • 조윤정 (경희대학교 대학원 생체의과학과) ;
  • 김유미 (상지대학교 의료경영학과) ;
  • 함승우 (원자력병원) ;
  • 최준영 (원광보건대학교 의무행정과) ;
  • 백설경 (아주대학교병원) ;
  • 강성홍 (인제대학교 보건행정학과)
  • Received : 2019.01.23
  • Accepted : 2019.04.05
  • Published : 2019.04.30

Abstract

In order to prevent unnecessary re-admission, it is necessary to intensively manage the groups with high probability of re-admission. For this, it is necessary to develop a re-admission prediction model. Two - year discharge summary data of one university hospital were collected from 2016 to 2017 to develop a predictive model of re-admission. In this case, the re-admitted patients were defined as those who were discharged more than once during the study period. We conducted descriptive statistics and crosstab analysis to identify the characteristics of rehospitalized patients. The re-admission prediction model was developed using logistic regression, neural network, and decision tree. AUC (Area Under Curve) was used for model evaluation. The logistic regression model was selected as the final re-admission predictive model because the AUC was the best at 0.81. The main variables affecting the selected rehospitalization in the logistic regression model were Residental regions, Age, CCS, Charlson Index Score, Discharge Dept., Via ER, LOS, Operation, Sex, Total payment, and Insurance. The model developed in this study was limited to generalization because it was two years data of one hospital. It is necessary to develop a model that can collect and generalize long-term data from various hospitals in the future. Furthermore, it is necessary to develop a model that can predict the re-admission that was not planned.

Table 1. Classification and definition of independent variablesPEter

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Table 2. Sociodemographic characteristics of subjects

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Table 3. Clinical characteristics of subjects

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Table 4. Hospital Utilization of subjects

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Table 5. Primary Diagnosis by CCS category

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Table 6. Distribution of Charlson comorbidity index

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Table 7. Distribution of Comorbidity by CCS

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Table 8. Difference in readmission according to sociodemographic characteristics in subjects

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Table 9. Difference in readmission according to clinical characteristics in subjects

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Table 10. Difference in medical use according to readmission

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Table 11. Medical use according to primary diagnosis by CCS

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Table 12. Readmission by CCI

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Table 13. Assessment of Model

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Table 14. Characteristics affecting Readmission(Logistic regression analysis)

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Table 15. Odds Ratio for Readmission

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Table 16. Odds Ratio for Readmission to discharge dept.

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Table 17. Odds Ratio for other characteristics

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Acknowledgement

Supported by : 인제대학교

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