Improving physician practice efficiency by learning lab test ordering pattern.
The system of electronic medical records (EMR) has been widely used in physician practice. In China, physicians have the time pressure to provide care to many patients in a short period. Improving practice efficiency is a promising direction to mitigate this predicament. During the encounter, ordering lab test is one of the most frequent actions in EMR system. In this paper, our motivation is to save physician's time by providing lab test ordering list to facilitate physician practice. To this end, we developed weight based multi-label classification framework to learn to order lab test for the current encounter according to the historical EMR. Particularly, we propose to learn the physician-specific lab test ordering pattern as different physicians may have different practice behavior on the same population. Experimental results on the real data set demonstrate that physician-specific models can outperform the baseline.
IBM China Research Lab, China.
Peng
P
Cai
Feng
F
Cao
Yuan
Y
Ni
Weijia
W
Shen
Tao
T
Zheng
D001185:Artificial Intelligence; D002681:China; D002983:Clinical Competence / Q000706:statistics & numerical data*; D002984:Clinical Laboratory Information Systems / Q000706:statistics & numerical data*; D019411:Clinical Laboratory Techniques / Q000706:statistics & numerical data*; D004526:Efficiency; D057286:Electronic Health Records / Q000706:statistics & numerical data; D016247:Information Storage and Retrieval / Q000379:methods*; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D057188:Workflow; D016526:Workload