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KMID : 0387320230330030295
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Korean Journal of Health Policy and Administration
2023 Volume.33 No. 3 p.295 ~ p.310
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The Effect of Long-Term Care Ratings and Benefit Utilization Characteristics on Healthcare Use
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Kim Hyun-Taek
Oh Seung-Jin
Yoon Jong-Min
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Abstract
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Background: The long-term care (LTC) group has higher rates of chronic disease and disability registration compared to the generalolder people population. There is a need to provide integrated medical services and care for LTC group. Consequently, this studyaimed to identify medical usage patterns based on the ratings of LTC and the characteristics of benefits usage in the LTC group.
Methods: This study employed the National Health Insurance Service Database to analyze the effects of demographic andLTC-related characteristics on medical usage from 2015 to 2019 using a repeated measures analysis. A longitudinal logit model wasapplied to binary data, while a linear mixed model was utilized for continuous data.
Results: In the case of LTC ratings, a positive correlation was observed with overall medical usage. In terms of LTC benefit usagecharacteristics, a higher overall level of medical usage was found in the group using home care benefits. Detailed analysis by medicalinstitution classification revealed a maintained correlation between care ratings and the volume of medical usage. However, medicalusage by classification varied based on the characteristics of LTC benefit usage.
Conclusion: This study identified a complex interaction between LTC characteristics and medical usage. Predicting the requisitemedical services based on the LTC rating presented a challenge. Consequently, it becomes essential for the LTC group tocontinuously monitor medical and care needs, even after admission into the LTC system. To facilitate this, it is crucial to devise anLTC rating system that accurately reflects medical needs and to broaden the implementation of integrated medical-care policies.
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KEYWORD
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Long-term care, Medical service, Medical usage, Repeated measures analysis, Longitudinal logit model, Linear mixed model, Medical institution classification
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