Johns Hopkins University School of Medicine, Department of Biomedical Engineering, Johns Hopkins Medicine, Baltimore, Maryland, USA.
Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.
BMJ Evid Based Med. 2020 Aug;25(4):125-129. doi: 10.1136/bmjebm-2019-111190. Epub 2019 May 11.
The rising burden of healthcare costs suggests that the healthcare system could benefit from novel methods that allow for continuous learning to provide more data-driven, individualised care at lower costs and with improved outcomes. Here, we present our synergistic Learning approach for Prediction, Interpretation/Inference and Communication (Learning PIC) framework to address the challenges hindering the successful implementation of learning healthcare systems and to enable the effective delivery of evidence-based medicine.
不断增加的医疗保健成本负担表明,医疗保健系统可能受益于新方法,这些方法允许持续学习,以更低的成本和更好的结果提供更具数据驱动性和个性化的护理。在这里,我们提出了我们的协同学习预测、解释/推理和通信(Learning PIC)框架方法,以解决阻碍学习型医疗保健系统成功实施的挑战,并实现循证医学的有效交付。