Dutch Healthcare Authority (NZa), Utrecht, The Netherlands.
Department of Economics, Tilburg University, Tilburg, The Netherlands.
Eur J Health Econ. 2020 Nov;21(8):1121-1129. doi: 10.1007/s10198-020-01213-9. Epub 2020 Jun 29.
The Netherlands is currently investigating the feasibility of moving from fee-for-service to prospective payments for home healthcare, which would require a suitable case-mix system. In 2017, health insurers mandated a preliminary case-mix system as a first step towards generating information on client differences in relation to care use. Home healthcare providers have also increasingly adopted standardized nursing terminology (SNT) as part of their electronic health records (EHRs), providing novel data for predictive modelling.
To explore the predictive potential of SNT data for improvement of the existing preliminary Dutch case-mix classification for home healthcare utilization.
We extracted client-level data from the EHRs of a large home healthcare provider, including data from the existing Dutch case-mix system, SNT data (specifically, NANDA-I) and the hours of home healthcare provided. We evaluated the predictive accuracy of the case-mix system and the SNT data separately, and combined, using the machine learning algorithm Random Forest.
The case-mix system had a predictive performance of 22.4% cross-validated R-squared and 6.2% cross-validated Cumming's Prediction Measure (CPM). Adding SNT data led to a substantial relative improvement in predicting home healthcare hours, yielding 32.1% R-squared and 15.4% CPM.
The existing preliminary Dutch case-mix system distinguishes client needs to some degree, but not sufficiently. The results indicate that routinely collected SNT data contain sufficient additional predictive value to warrant further research for use in case-mix system design.
荷兰目前正在研究将家庭医疗保健从按服务收费改为按预期付款的可行性，这将需要一个合适的病例组合系统。2017 年，健康保险公司强制实施了初步的病例组合系统，作为生成与护理使用相关的客户差异信息的第一步。家庭医疗保健提供者也越来越多地采用标准化护理术语（SNT）作为其电子健康记录（EHRs）的一部分，为预测模型提供了新颖的数据。
探索 SNT 数据在改进现有荷兰家庭医疗保健利用初步病例组合分类方面的预测潜力。
我们从一家大型家庭医疗保健提供商的 EHR 中提取了客户层面的数据，包括现有荷兰病例组合系统、SNT 数据（具体为 NANDA-I）和家庭医疗保健提供的小时数的数据。我们使用机器学习算法随机森林分别评估病例组合系统和 SNT 数据的预测准确性，并结合使用。
病例组合系统的预测性能为 22.4%的交叉验证 R-squared 和 6.2%的交叉验证 Cumming's Prediction Measure（CPM）。添加 SNT 数据后，预测家庭医疗保健小时数的相对改善显著，产生 32.1%的 R-squared 和 15.4%的 CPM。
现有的初步荷兰病例组合系统在一定程度上区分了客户的需求，但还不够充分。结果表明，常规收集的 SNT 数据包含足够的额外预测价值，值得进一步研究，以用于病例组合系统设计。
