Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL, USA.
Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA.
Biomed Eng Online. 2018 Nov 20;17(Suppl 1):131. doi: 10.1186/s12938-018-0568-3.
This paper studies the temporal consistency of health care expenditures in a large state Medicaid program. Predictive machine learning models were used to forecast the expenditures, especially for the high-cost, high-need (HCHN) patients.
We systematically tests temporal correlation of patient-level health care expenditures in both the short and long terms. The results suggest that medical expenditures are significantly correlated over multiple periods. Our work demonstrates a prevalent and strong temporal correlation and shows promise for predicting future health care expenditures using machine learning. Temporal correlation is stronger in HCHN patients and their expenditures can be better predicted. Including more past periods is beneficial for better predictive performance.
This study shows that there is significant temporal correlation in health care expenditures. Machine learning models can help to accurately forecast the expenditures. These results could advance the field toward precise preventive care to lower overall health care costs and deliver care more efficiently.
本文研究了大型州医疗补助计划中医疗支出的时间一致性。预测机器学习模型被用于预测支出，特别是对于高成本、高需求（HCHN）患者。
我们系统地测试了患者层面医疗支出在短期和长期的时间相关性。结果表明，医疗支出在多个时期存在显著相关性。我们的工作证明了普遍存在且强大的时间相关性，并表明使用机器学习预测未来医疗支出具有广阔的前景。HCHN 患者及其支出的时间相关性更强，并且可以更好地预测。包含更多的过去时期有助于提高预测性能。
本研究表明医疗支出存在显著的时间相关性。机器学习模型可以帮助准确预测支出。这些结果可能会推动该领域朝着精确预防保健的方向发展，以降低整体医疗成本并更有效地提供医疗服务。
