Lei Howard, O'Connell Ryan, Ehwerhemuepha Louis, Taraman Sharief, Feaster William, Chang Anthony
CHOC Children's Hospital, Orange, CA, USA.
The Sharon Disney Lund Medical Intelligence and Innovation Institute (MI3), USA.
Intell Based Med. 2020 Dec;3:100009. doi: 10.1016/j.ibmed.2020.100009. Epub 2020 Oct 22.
The COVID-19 pandemic has required greater minute-to-minute urgency of patient treatment in Intensive Care Units (ICUs), rendering the use of Randomized Controlled Trials (RCTs) too slow to be effective for treatment discovery. There is a need for agility in clinical research, and the use of data science to develop predictive models for patient treatment is a potential solution. However, rapidly developing predictive models in healthcare is challenging given the complexity of healthcare problems and the lack of regular interaction between data scientists and physicians. Data scientists can spend significant time working in isolation to build predictive models that may not be useful in clinical environments. We propose the use of an agile data science framework based on the Scrumban framework used in software development. Scrumban is an iterative framework, where in each iteration larger problems are broken down into simple do-able tasks for data scientists and physicians. The two sides collaborate closely in formulating clinical questions and developing and deploying predictive models into clinical settings. Physicians can provide feedback or new hypotheses given the performance of the model, and refinement of the model or clinical questions can take place in the next iteration. The rapid development of predictive models can now be achieved with increasing numbers of publicly available healthcare datasets and easily accessible cloud-based data science tools. What is truly needed are data scientist and physician partnerships ensuring close collaboration between the two sides in using these tools to develop clinically useful predictive models to meet the demands of the COVID-19 healthcare landscape.
新型冠状病毒肺炎（COVID-19）大流行使得重症监护病房（ICU）的患者治疗需要每分钟都保持更高的紧迫性，这使得随机对照试验（RCT）的使用速度过慢，无法有效地用于治疗探索。临床研究需要灵活性，利用数据科学来开发患者治疗预测模型是一个潜在的解决方案。然而，鉴于医疗保健问题的复杂性以及数据科学家与医生之间缺乏定期互动，在医疗保健领域快速开发预测模型具有挑战性。数据科学家可能会花费大量时间独自工作来构建在临床环境中可能无用的预测模型。我们建议使用基于软件开发中使用的Scrumban框架的敏捷数据科学框架。Scrumban是一个迭代框架，在每次迭代中，较大的问题会被分解为数据科学家和医生可以完成的简单任务。双方在制定临床问题以及开发预测模型并将其部署到临床环境中时密切合作。医生可以根据模型的性能提供反馈或新的假设，并且可以在下一次迭代中对模型或临床问题进行优化。随着越来越多的公开可用医疗保健数据集以及易于访问的基于云的数据科学工具的出现，现在可以实现预测模型的快速开发。真正需要的是数据科学家和医生之间的合作关系，确保双方在使用这些工具开发临床有用的预测模型以满足COVID-19医疗保健形势的需求方面密切合作。
