ABSTRACT

Background

Based on the premise of limited health-care resources, decision-makers pursue to allocate disease management programs (DMP) more targeted.

Methods

Based on routine data from a private health insurance company, a prediction model was developed to estimate the individual risk for future in-patient stays of patients eligible for a DMP Hypertension. The database included anonymous claims data of 38,284 policyholders with a diagnosis in the year 2013. A cutoff point of ≥70% was used for selecting candidates with a risk for future hospitalization. Using a logistic regression model, we estimated the model’s prognostic power, the occurrence of clinical events, and the resource use.

Results

Overall, the final model shows acceptable prognostic power (detection rate = 64.3%; sensitivity = 68.7%; positive predictive value (PPV) = 64.1%, area under the curve (AUC) = 0.72). The comparison between the selected hypothetical DMP-group with a predicted (LOH) ≥70% showed additional costs of about 69% for the DMP-group compared to insure with a LOH <70%.

Conclusion

The predictive analytical approach may identify potential DMP participants with a high risk of increased health services utilization and in-patient stays.

中文翻译：

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用于识别疾病管理项目高血压潜在参与者的预测模型

摘要

背景

在医疗资源有限的前提下，决策者追求更有针对性地分配疾病管理计划（DMP）。

方法

根据来自一家私人健康保险公司的常规数据，开发了一个预测模型来估计符合 DMP 高血压资格的患者未来住院的个人风险。该数据库包括 2013 年诊断为 38,284 名投保人的匿名索赔数据。≥70% 的截止点用于选择未来住院风险的候选人。使用逻辑回归模型，我们估计了模型的预后能力、临床事件的发生率和资源使用情况。

结果

总体而言，最终模型显示出可接受的预后能力（检出率 = 64.3%；灵敏度 = 68.7%；阳性预测值 (PPV) = 64.1%，曲线下面积 (AUC) = 0.72）。选择的假设 DMP 组与预测 (LOH) ≥70% 的比较显示，与 LOH <70% 的保险相比，DMP 组的额外成本约为 69%。

结论

预测分析方法可以识别潜在的 DMP 参与者，这些参与者具有增加卫生服务利用率和住院时间的高风险。