An Interactive Platform to Visualize Data-Driven Clinical Pathways for the Management of Multiple Chronic Conditions
Patients with multiple chronic conditions(MCC)pose an increasingly complex health management challenge worldwide,particularly due to the significant gap in our understanding of how to provide coordinated care.Drawing on our prior research on learning data-driven clinical pathways from actual practice data,this paper describes a prototype,interactive platform for visualizing the pathways of MCC to support shared decision making.Created using Python web framework,JavaScript library and our clinical pathway learning algorithm,the visualization platform allows clinicians and patients to learn the dominant patterns of coprogression of multiple clinical events from their own data,and interactively explore and interpret the pathways.We demonstrate functionalities of the platform using a cluster of 36 patients,identified from a dataset of 1,084 patients,who are diagnosed with at least chronic kidney disease,hypertension,and diabetes.Future evaluation studies will explore the use of this platform to better understand and manage MCC.
Critical Pathways Computer Graphics Multiple Chronic Conditions
Yiye Zhang Rema Padman
Division of Health Informatics,Department of Health Policy and Research,Weill Cornell Medical Colleg The H.John Heinz III College,Carnegie Mellon University,Pittsburgh,PA,USA
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
672-676
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）