국민건강보험공단 빅데이터를 활용한 장기요양 요구 발생 고위험군 선정 방안 연구
The identification of high-risk groups for long-term care insurance: A retrospective study using national health insurance service database
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Purpose: This study aimed to identify high-risk groups for Long-Term Care Insurance (LTCI) in older people not approved for LTCI and to examine the characteristics of each high-risk group. Methods: This study was a retrospective study using the National Health Insurance Service database and included 7,724,101 older A decision- tree model was used to predict the high-risk groups for LTCI. The dependent variable was defined as LTCI eligibility. As independent variables, 78 variables consisting of personal factors, environmental factors, health status, and physical and cognitive abilities were used. Results: The prediction model to identify high-risk groups for LTCI was developed as the decision-tree model consisting of 19 end nodes with 10 risk factors. Eleven groups were identified as high-risk groups. The results showed the model could predict about 72% of the older people at high risk for LTCI needs using the NHIS database without the assessment of LTCI eligibility. Conclusion: The findings might be useful for the development of evidence-based preventative services and can contribute to preemptively discovering those who need preventive services in older adults.
Abstract
Purpose: This study aimed to identify high-risk groups for Long-Term Care Insurance (LTCI) in older people not approved for LTCI and to examine the characteristics of each high-risk group. Methods: This study was a retrospective study using the National Health Insurance Service database and included 7,724,101 older A decision- tree model was used to predict the high-risk groups for LTCI. The dependent variable was defined as LTCI eligibility. As independent variables, 78 variables consisting of personal factors, environmental factors, health status, and physical and cognitive abilities were used. Results: The prediction model to identify high-risk groups for LTCI was developed as the decision-tree model consisting of 19 end nodes with 10 risk factors. Eleven groups were identified as high-risk groups. The results showed the model could predict about 72% of the older people at high risk for LTCI needs using the NHIS database without the assessment of LTCI eligibility. Conclusion: The findings might be useful for the development of evidence-based preventative services and can contribute to preemptively discovering those who need preventive services in older adults.
참고문헌 (30)
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