College of Business, Chungbuk National University, Cheongju, Republic of Korea.
Technology Management, Economics and Policy Graduate Program, Department of Industrial Engineering, Seoul National University, Seoul, Republic of Korea.
Big Data. 2019 Sep;7(3):163-175. doi: 10.1089/big.2018.0096. Epub 2019 Jun 27.
Studies found that a small portion of the population spent the majority of health care resources, and they highlighted the importance of predicting high-cost users in the health care management and policy. Most prior research on high-cost user prediction models are based on diagnosis data with additional cost and health care utilization data to improve prediction accuracy. To further improve the prediction of high-cost users, researchers have been testing various new data sources such as self-reported health status data. In this study, we use three categories of medical check-up data, laboratory tests, self-reported medical history, and self-reported health behavior data to build high-cost user prediction models, and to assess the medical check-up features as predictors of high-cost users. Using three data-mining models, logistic regression, random forest, and neural network models, we show that under the diagnosis-based approach, medical check-up data marginally improve diagnosis-based prediction models. Under the cost-based approach, we find that medical check-up data improve cost-based prediction models marginally and medical check-up data can be a viable alternate data source to diagnosis data in predicting high-cost users.
研究发现，一小部分人口消耗了大部分医疗保健资源，这凸显了在医疗保健管理和政策中预测高成本用户的重要性。大多数关于高成本用户预测模型的先前研究都是基于诊断数据，并结合额外的成本和医疗保健利用数据来提高预测准确性。为了进一步提高高成本用户的预测准确性，研究人员一直在测试各种新的数据来源，如自我报告的健康状况数据。在这项研究中，我们使用了三类体检数据、实验室检查、自我报告的病史和自我报告的健康行为数据来构建高成本用户预测模型，并评估体检特征作为高成本用户的预测指标。我们使用三种数据挖掘模型，逻辑回归、随机森林和神经网络模型，表明在基于诊断的方法下，体检数据略微提高了基于诊断的预测模型。在基于成本的方法下，我们发现体检数据略微提高了基于成本的预测模型，并且体检数据可以作为预测高成本用户的诊断数据的可行替代数据来源。
