Department of Computer Information Systems and Quantitative Methods, McCoy College of Business, 601 University Dr, Texas State University, San Marcos 78666, TX, USA.
Ringful Health, Austin, TX, USA.
Comput Methods Programs Biomed. 2017 Jul;145:127-133. doi: 10.1016/j.cmpb.2017.04.004. Epub 2017 Apr 18.
A patient's complete medication history is a crucial element for physicians to develop a full understanding of the patient's medical conditions and treatment options. However, due to the fragmented nature of medical data, this process can be very time-consuming and often impossible for physicians to construct a complete medication history for complex patients. In this paper, we describe an accurate, computationally efficient and scalable algorithm to construct a medication history timeline. The algorithm is developed and validated based on 1 million random prescription records from a large national prescription data aggregator. Our evaluation shows that the algorithm can be scaled horizontally on-demand, making it suitable for future delivery in a cloud-computing environment. We also propose that this cloud-based medication history computation algorithm could be integrated into Electronic Medical Records, enabling informed clinical decision-making at the point of care.
患者完整的用药史是医生全面了解患者病情和治疗方案的关键因素。然而，由于医疗数据的碎片化，这个过程非常耗时，对于复杂的患者，医生往往无法构建完整的用药史。在本文中，我们描述了一种准确、高效且可扩展的算法，用于构建用药史时间线。该算法是基于来自大型全国处方数据聚合器的 100 万份随机处方记录开发和验证的。我们的评估表明，该算法可以按需水平扩展，因此非常适合在云计算环境中进行未来交付。我们还提出，这种基于云的用药史计算算法可以集成到电子病历中，从而在护理点实现明智的临床决策。
