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Machine Learning Clinical Order Recommendations for Specialty Consultation Care

专科咨询护理的机器学习临床医嘱建议

基本信息

批准号：
10265158
负责人：
JONATHAN H. CHEN
金额：
$ 39.43万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-25 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10265158
关键词：
Active Learning Acute Address Adopted Adoption Algorithms Caring Clinical Clinical Data Clinical Medicine Complex Computerized Medical Record Consult Consultations Data Data Reporting Data Science Diagnosis Diagnostic tests Electronic Mail Endocrinology Environment Evaluation Event Feedback Foundations Future Goals Graph Healthcare Healthcare Systems Hematology Human Human Resources Hyperthyroidism Individual Industry Information Retrieval Inpatients Knowledge Knowledge Discovery Learning Licensing Machine Learning Manuals Medical Methods Modeling Modern Medicine Monitor Outcome Outpatients Patient-Focused Outcomes Patients Pattern Persons Pharmaceutical Preparations Physicians Primary Health Care Psychological reinforcement Recommendation Records Reproducibility Research Resolution Resources Running Specialist Suggestion System Test Result Testing Thrombocytopenia Time Training Transcend Vision Visit Work base clinical care clinical decision support clinical practice clinical predictors convolutional neural network crowdsourcing data streams design experience individual patient innovation iterative design medical specialties mortality novel open source outcome forecast personalized decision personalized predictions predictive modeling prototype statistical learning statistics tool usability

项目摘要

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

摘要：专业会诊护理的机器学习临床医嘱建议