人口老龄化促进了老年长期护理行业的发展,但是对长期护理保险的评估等级、过程管理等相关制度还不完善。本文结合OLAP技术与贝叶斯算法,首先通过OLAP技术分析评估数据,提取关键影响因素,随后利用贝叶斯算法进行概率建模,量化各因素对系统可靠性的影响,构建长护险异常预警模型并进行验证分析,发现评估机构存在质量均质化程度不高、沟通协调不顺畅、问题评估表偏离数据均值等问题,最后提出相关的政策建议。

The aging population has promoted the development of demand in the long-term care (LTC) insurance for the elderly, but the assessment and process management of LTC are not yet perfect, which can easily lead to losses in the medical insurance fund. Firstly, OLAP analysis is used to evaluate data, extract key influencing factors, and then Bayesian algorithm is used for probability modeling to quantify the impact of various factors on system reliability. A LTC insurance anomaly warning model is constructed and verified through analysis. It is found that the evaluation institution has problems such as low homogenization, poor communication and coordination, deviation from the mean, and relevant policy recommendations are proposed.