Machine Learning Clinical Order Recommendations for Specialty Consultation Care

摘要：用于专科会诊护理的机器学习临床订单推荐 临床决策支持的未来愿景必须超越可伸缩性、可维护性和 适应能力。到2030年，10万名医生的短缺反映出对医疗保健服务的需求尚未得到满足(且无限) 最稀缺的医疗资源、临床专业知识。仅在美国就有超过2500万人无法获得 医疗专科护理，延误导致死亡率增加20%。没有通道就没有质量。 我们的目标是开发一种完全不同的门诊专科会诊模式，通过归纳 学习嵌入在临床数据中的临床检查。我们专注于预测具体的临床订单 由专科会诊产生的药物和诊断测试。这可以为一层完全 自动化指南将使临床医生能够启动原本等待面对面专科就诊的护理， 向更多患者开放通道。 主要的科学障碍是数据科学的进步和用于整理的决策支持方法 临床知识，通过临床经验、众包和机器不断改进 学习。我们的创新方法是受协作过滤算法的启发，这些算法为“客户喜欢 你还买了这个……“推荐系统，具有回答无限查询的可扩展性、可维护性 通过统计学习，并适应不断变化的临床实践。 我们的团队独特地结合了临床医学、电子病历、临床决策方面的专业知识 支持、统计和机器学习，通过以下目标加强医学专科会诊： (1)开发通过预测将产生的临床订单来生成临床决策支持的方法 内分泌和血液学专科会诊；(2)评估和迭代设计临床协作 基于临床用户对可用性和可接受性的输入来筛选原型；以及(3)确定哪些咨询 通过强化学习和因果推理，临床顺序模式与更好的结果相关 框架。 这些目标的实现将对临床信息检索和知识产生持续、强大的影响 从真实世界的数据中综合临床实践的发现。通过应对临床上的重大挑战 决策支持，这些方法的采用将实现一个愿景，使临床医生能够实践到 他们的许可证，使医疗保健在覆盖范围、响应速度和重复性方面更具可扩展性

Summary: Machine Learning Clinical Order Recommendations for Specialty Consultation Care A future vision of clinical decision support must transcend constraints in scalability, maintainability, and adaptability. The shortage of 100,000 physicians by 2030 reflects unmet (and unlimited) demand for the scarcest healthcare resource, clinical expertise. Over 25 million in the US alone have deficient access to medical specialty care, with delays contributing to 20% higher mortality. There is no quality without access. Our goal is to develop a radically different paradigm for outpatient specialty consultations by inductively learning clinical workups embedded in clinical data. We focus on predicting the concrete clinical orders for medications and diagnostic tests that result from specialty consultations. This can power a tier of fully automated guides that will enable clinicians to initiate care that would otherwise await in-person specialty visits, opening access for more patients. The major scientific barriers are advances in data science and decision support methods for collating clinical knowledge, with continuous improvement through clinical experience, crowdsourcing, and machine learning. Our innovative approach is inspired by collaborative filtering algorithms that power “Customers like you also bought this...” recommender systems with the scalability to answer unlimited queries, maintainability through statistical learning, and adaptability to respond to evolving clinical practices. Our team uniquely combines expertise in clinical medicine, electronic medical records, clinical decision support, statistics and machine learning to enhance medical specialty consultations through aims that seek to: (1) Develop methods to generate clinical decision support by predicting the clinical orders that will result from Endocrinology and Hematology specialty consultations; (2) Evaluate and iteratively design clinical collaborative filtering prototypes based on clinical user input on usability and acceptability; and (3) Determine which consult clinical order patterns are associated with better results through reinforcement learning and causal inference frameworks. Completion of these aims will yield a sustained, powerful impact on clinical information retrieval and knowledge discovery for synthesizing clinical practices from real-world data. By addressing grand challenges in clinical decision support, adoption of these methods will fulfill a vision that empowers clinicians to practice to the top of their license, making healthcare more scalable in reach, responsiveness, and reproducibility